Who Gets Back on Track? Engagement-Level Differences in Metacognitive Attention Refocusing in Open and Distance Learning

Who Gets Back on Track? 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Engagement-Level Differences in Metacognitive Attention Refocusing in Open and Distance Learning Yanxing Xue, Fariza khalid This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9269094/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In open and distance learning (ODL), learners must sustain engagement with limited external structure, making attention drift a pervasive threat to effective study. This study investigates how ODL students monitor attentional drift and refocus on course tasks, and how attention-refocusing mechanisms vary across high-, moderate-, and low-engagement learner profiles. An exploratory embedded single-case study was conducted in an ODL program at a public university in Malaysia. Nine focal students were analyzed using three data sources: learning management system (LMS) traces from 12 courses, written reflection reports, and a three-hour focus group interview. Multiple mechanism families of attention refocusing were identified. High-engagement learners drew on a broad, coordinated repertoire, often combining several mechanisms and proactively shaping their study time and context. Moderate-engagement learners relied on a narrower, mainly context- and time-focused configuration, whereas low-engagement learners used reactive, fragmented tactics with weak links between long-term goals and immediate actions. Overall, behavioral and cognitive refocusing strategies were employed in broadly similar proportions. The findings foreground attention refocusing as a crucial mechanism through which metacognitive control supports sustained engagement in ODL settings. It shows that a broader, more consistently combined repertoire of refocusing tactics distinguishes highly engaged learners from their peers. The study suggests that ODL design and support services should explicitly scaffold monitoring of drift and provide concrete repertoires of refocusing tactics to help learners “get back on track” in digitally mediated, high-autonomy environments. Social science/Education Biological sciences/Psychology Social science/Psychology attention refocusing learning engagement Open and distance learning metacognitive Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction The broader landscape of online learning now reaches a wide and diverse population, freely accessible online courses and open resources, such as MIT Open Courseware and its recent graduate-level course “How to AI (Almost) Anything,” illustrate how learners anywhere with an internet connection can engage with cutting-edge university content without physical co-presence (He et al., 2025 ). Against this backdrop, it places heavy demands on learners’ ability to sustain engagement without the temporal and social structure of face-to-face classrooms (Bond & Bergdahl, 2023 ). In asynchronous, screen-based environments, students must decide when to log in, how long to persist, and how to deal with frequent internal and external distractions. Prior research consistently shows that difficulties in maintaining attention on course tasks are a major source of low engagement, fragmented participation, and eventual dropout in ODL settings (Bağrıacık Yılmaz, 2022 ; Rahmani et al., 2024 ). At the same time, the rapid proliferation of generative artificial intelligence has automated access to information and many routine cognitive operations in higher education. Rather than reducing the need for learner agency, this shift arguably heightens the importance of self-regulated and metacognitively regulated learning; recent reviews of AI-supported and AI-empowered self-regulated learning indicate that intelligent and generative tools can scaffold goal setting, monitoring and strategy adjustment only when learners actively engage in these regulatory processes instead of outsourcing thinking to the system (Jin et al., 2023 ; Lan & Zhou, 2025 ; Zawacki-Richter et al., 2019 ). In parallel, research on mind wandering and spontaneous thought has demonstrated that attention drift is a ubiquitous feature of cognition and that its impact on learning depends on whether individuals notice the shift and deliberately redirect attention back to task-relevant goals (Bühler et al., 2025 ; Szpunar et al., 2013 ). From this perspective, metacognitive regulation, especially the capacity to detect attention drift and refocus on meaningful learning goals, emerges as a core human capability for sustaining deep engagement with complex tasks in AI-saturated learning environments (Jin et al., 2023 ; Lan & Zhou, 2025 ). Understanding how ODL learners perform this moment-to-moment regulation, and why high- and low-engagement learners differ in their refocusing patterns, is therefore both theoretically and practically significant. In early 2020, schools were closed worldwide due to the outbreak of the COVID-19 pandemic. During this period, ODL expanded rapidly and has increasingly been viewed as an important direction for the future of education (Bond et al., 2021 ). In ODL, where physical separation between instructors and learners is the norm, fostering robust student engagement is essential for successful learning outcomes (Bond et al., 2021 ; Ghosh et al., 2012). Student engagement, a multifaceted construct encompassing cognitive, emotional, and behavioral dimensions, lies at the heart of effective learning experiences in ODL environments. It refers to the extent to which learners actively participate, invest effort, and connect meaningfully with their educational pursuits. The significance of student engagement extends beyond academic attainment and includes broader socio-emotional outcomes such as satisfaction, retention, and persistence. In ODL environments, where learners often juggle multiple commitments and responsibilities, cultivating metacognitive ability, a sense of belonging, and connectedness can play a pivotal role in promoting student success and well-being (Martin & Bolliger, 2018 ). Understanding the dynamics of student engagement in ODL, therefore, requires a nuanced exploration of the factors that shape learners' experiences and behaviors (Bond & Bergdahl, 2023 ). Recent work highlights the crucial role of metacognitive regulation in promoting active and meaningful learning in ODL and underscores the importance of integrating metacognitive instruction and support services into distance learning programs (Anthonysamy, 2021 ). A more specific gap in the current literature concerns the moment-to-moment mechanisms through which learners regain focus after attention drift in online and distance learning contexts. Although prior research acknowledges that metacognitive regulation is critical in ODL, particularly because learners must manage their own attention without the external structure of face-to-face classrooms (Hartley, 2007 ; Kauffman, 2004 ), existing studies tend to treat regulation as a broad construct, often measured through global scales or general SRL behaviors. What remains underexplored is the micro-level process of attention refocusing, that is, how learners notice attention drift and what specific strategies they use to bring their focus back to task. Moreover, recent studies have conceptualized online learning engagement as a multidimensional construct encompassing behavioral, cognitive, emotional, and social dimensions (Heilporn et al., 2024 ; Joshi et al., 2022 ; Zhang et al., 2025 ), yet very few have examined how attention-refocusing strategies vary across learners with different profiles of behavioral, cognitive, and social engagement. There is a notable absence of in-depth, case-based analyses that compare high- and low-engagement learners to uncover heterogeneous patterns of refocusing, or that trace the mechanisms by which learners with varying engagement profiles regulate their attention during authentic ODL learning. This study addresses this gap by analyzing refocusing episodes among ODL learners and mapping how different engagement tiers employ distinct metacognitive strategies to recover from attention drift. The central research question is: How do students engage in metacognitive regulation, including refocusing attention, in the context of open and distance learning? This study addresses these gaps by conducting a case analysis of students enrolled in ODL courses at a public university in Malaysia. It first characterizes learning engagement along behavioral, cognitive, emotional, and social dimensions and classifies learners into high-, moderate-, and low-engagement profiles based on a combination of LMS indicators evidence. Building on this typology, the study focuses specifically on attention refocusing as a distinct phase of metacognitive regulation, tracing how students in each engagement tier deploy strategies to bring their focus back to the task. By comparing the effectiveness of attention-refocusing episodes across differently engaged learners, the study illuminates the mechanisms through which metacognitive regulation supports or fails to support sustained engagement in ODL. These findings, grounded in interdisciplinary perspectives from psychology, education, and learning technologies, provide theoretical and practical guidance for designing more student-centred support systems and instructional practices to enhance engagement in open and distance learning environments. In response to these gaps, this study makes three main contributions to the literature. First, it offers a mechanism-focused account of how ODL learners refocus their attention after drift, by conceptualizing refocusing episodes such as Monitor-Control-Outcome micro-cycles and inductively mapping a repertoire of refocusing strategies (e.g., goal-based and strategy-based refocusing, affective repair, environmental and temporal anchoring, motivational repair). Second, it links these mechanisms to heterogeneous patterns of engagement: combining LMS traces with reflection data, it compares how high-, moderate- and low-engagement learners differ in their refocusing repertoires. Third, methodologically, the study demonstrates how qualitative episode-based coding can be integrated and analyze metacognitive regulation processes in small but data-rich ODL case studies. Together, these contributions extend existing self-regulated learning and mind-wandering research from global reports of attention problems to a finer-grained description of how learners detect attention drift and bring their focus back to task in authentic ODL environments. The remainder of this paper is organized as follows. Section 2 reviews the relevant literature on ODL, student engagement, and metacognitive regulation. Section 3 describes the research context, participants, and methodological approach of the case study. Section 4 presents empirical findings on learning engagement and metacognitive regulation among students in ODL. Section 5 discusses these findings in relation to existing theory and prior research and draws out their practical implications, and concludes by summarizing the main contributions, noting the limitations of the study, and suggesting directions for future research. 2. Literature Review 2.1 Open and Distance Learning, Self-Regulation, and Metacognitive Regulation Open and distance learning (ODL) has expanded access to higher education, but also shifted greater responsibility for learning processes and outcomes onto students. Compared with traditional face-to-face settings, ODL offers limited immediate instructor guidance and peer interaction, so learners must rely more on self-regulation, autonomy, and motivation to maintain effective learning (Broadbent, 2017). This study focuses on a formal open and distance learning (ODL) program because that is where rich, longitudinal data are available. Conceptually, ODL can be seen as a structured subset of online learning: learners’ study at a distance, rely heavily on digital platforms, and face similar challenges of sustaining attention, managing time and regulating their own engagement. Insights into how ODL students monitor and refocus their attention are therefore not only relevant for this specific program, but also informative for understanding attention regulation in online learning more generally. In this context, metacognitive regulation is especially critical because learners must plan, monitor, and evaluate their own learning trajectories to stay on track without continuous external supervision. Within the self-regulated learning tradition, Zimmerman ( 2002 ) conceptualizes self-regulated learning as a cyclical process with three phases: forethought, performance, and self-reflection, each involving metacognitive, motivational, and behavioral processes such as goal setting, strategic planning, self-monitoring, and self-evaluation. This cycle supports deep and self-directed learning in online environments where course structures are comparatively loose, and learners frequently need to repair breakdowns in attention, motivation, or task comprehension. In this view, metacognitive regulation: planning, monitoring, and evaluating one’s own learning operates within and across these phases as a core mechanism of SRL (Schraw & Moshman, 1995 ). 2.2 Multidimensional Engagement and LMS-Based Indicators in ODL Student engagement is widely described as a multidimensional construct that includes behavioral, emotional, and cognitive components (Fredricks, Blumenfeld, & Paris, 2004 ). In ODL settings, engagement is often inferred from digital activity traces and interaction patterns rather than physical presence. The Community of Inquiry framework likewise argues that effective online learning arises from the interplay of social presence, cognitive presence, and teaching presence (Garrison, Anderson, & Archer, 2000 ). Together, these models suggest that any operationalization of engagement in ODL should attend to what students do, how they think, and how they connect with others. Learning analytics research has shown that learning management system (LMS) behavioral traces, such as logins, assessment attempts and completions, submissions, and access frequency, are among the strongest correlates and early predictors of achievement. Early mining of LMS logs demonstrated that patterns of online activity can reliably identify at-risk learners for timely support (Macfadyen & Dawson, 2010 ). Subsequent studies confirmed that attempts, submissions, and access behaviors are significant indicators of course performance (You, 2016 ), and recent reviews have consolidated these findings across platforms and methods. Self-report data also shows convergence with digital traces. For example, Dixson ( 2015 ) found that scores on the Online Student Engagement (OSE) scale align closely with LMS activity, which supports the use of analytics-based profiling when affective or self-report data are unavailable. Building on this literature, the present study adopts a three-axis classification of ODL engagement using LMS data only. Forum posts and replies are interpreted as social engagement because they index interaction and presence in the learning community. Logins and participation in formative assessments are treated as behavioral engagement, capturing the frequency and rhythm of students’ online study actions. Performance on attempted formative assessments is used as an indicator of cognitive engagement quality, reflecting how effectively students process and apply course content. This operationalization is consistent with multidimensional engagement frameworks, is supported by existing learning analytics findings, and is feasible in small case study contexts where continuous self-report measures are not available. 2.3 Metacognitive Regulation: Planning, Monitoring, Evaluation, and Control Within the broader self-regulated learning framework, metacognitive regulation focuses on the regulation of cognition. It comprises planning, monitoring, and evaluation, together with control actions that modify behavior in response to feedback. These processes help learners adapt to complex tasks, manage difficulties, and maintain alignment with goals, thereby promoting deeper learning, critical thinking, and problem solving (Sulisworo et al., 2020 ; Tiruneh et al., 2014 ). Monitoring refers to the continuous evaluation of one’s cognitive processes and learning progress. It is key to maintaining awareness of understanding and performance, detecting discrepancies and errors, and deciding whether change is needed (Pesout & Nietfeld, 2021 ). Evaluation involves a more deliberate review of learning outcomes and strategies, assessing their effectiveness and quality. Based on monitoring and evaluation, learners engage in regulation or control. Experimental work on metacognitive monitoring tasks, such as confidence judgements, predictions of future performance, and comprehension self-assessments, and on metacognitive control tasks, such as strategy selection, goal setting, and planning, provides tools for assessing how accurately learners can track their own performance and how effectively they respond to feedback (Craig et al., 2020 ). Discrepancies between expected and actual performance reveal the calibration of monitoring, while control tasks show how learners adapt their cognitive processes when demands change. In this study, these ideas inform the analytic distinction between noticing attentional drift (monitoring) and acting to refocus attention (control) in authentic ODL settings. 2.4 Mind Wandering, Focus Back Effort, Motivation, and Interest A central challenge in sustained learning, particularly in ODL, where external structure is weaker, is the regulation of mind wandering. Recent work introduces the construct of focus back effort, defined as the effort to redirect attention to the current task when an individual is mind-wandering (He et al., 2023 ). Focus back effort is usually assessed by intermittently probing participants during a task and asking to what extent they are trying to re-engage with the activity. Empirical studies show a positive correlation between focus back effort and functional connectivity within nodes of the executive network (He et al., 2023 ; Zanesco et al., 2024 ), consistent with theoretical models that link the subjective experience of effort to the recruitment of executive control (Kurzban et al., 2013 ). Laboratory research further reports a negative association between focus back effort and the frequency of mind wandering. Learners who invest more effort in refocusing are less likely to drift away from the task (He et al., 2024 ). Motivation is a second key determinant of attentional drift. A substantial body of work shows that higher task-oriented motivation is associated with fewer mind wandering episodes (Robison & Unsworth, 2018 ). Frank et al. ( 2015 ) reported a negative relationship between self-reported mind wandering and motivation to succeed in a reading comprehension task. Experimental manipulations that increase motivation, such as offering the possibility to end the experiment early if performance is good, have been shown to reduce mind wandering (Seli et al., 2019 ). A multifaceted approach that integrates cognitive, dispositional, and contextual predictors found robust associations between individual differences in mind wandering and motivation across tasks (Robison et al., 2020 ). Motivational factors are also closely tied to interest. Individuals who find a task more interesting are generally more motivated to perform it (Hidi & Harackiewicz, 2000 ). Numerous studies have documented a negative relationship between task interest and mind wandering (Kahmann et al., 2022 ; Robison & Unsworth, 2018 ). In educational contexts, individual differences in interest are associated with variations in mind wandering. Unsworth et al. (2013) further showed that motivation mediates the relationship between interest and mind wandering in reading comprehension tasks, treating both constructs as domain specific. Taking together, this literature suggests that focus, effort, motivation, and interest jointly shape how learners experience and regulate attentional drift, making them highly relevant for understanding metacognitive regulation in ODL. 2.5 Conceptual Framing: Attention Refocusing as Metacognitive Control in ODL Taken together, these strands of literature portray ODL as a context in which high self-regulation demands, multidimensional engagement, and fragile attentional states converge. Learners must navigate a digital environment where engagement is expressed through behavioral traces, cognitive effort, and social interaction, while simultaneously managing mind wandering, fluctuating arousal, multiple life roles, and changing motivation. In this landscape, metacognitive regulation, particularly the monitoring of attentional drift and the effective use of refocusing strategies, emerges as a central mechanism that links engagement with learning outcomes. The present study builds on this work by conceptualizing attention refocusing as a core form of metacognitive control in ODL. It examines how learners become aware of internal and external triggers of attentional drift, how they enact behavioral and cognitive strategies to refocus on learning tasks, and how they draw on motivational and attributional processes to sustain engagement over time. This perspective is summarized in the proposed cyclical model of attention regulation in ODL (Fig. 1 ), which guides the subsequent analysis of refocusing episodes and their variation across learners with different engagement profiles. While prior SRL and metacognition research has documented the importance of monitoring and regulation in online learning, less is known about the fine-grained mechanisms by which learners refocus attention after drift, and how these mechanisms vary between high- and low-engagement learners in authentic ODL settings. 3. Methodology 3.1 Research Design This study adopts an exploratory, embedded single-case study design to examine how ODL learners metacognitively regulate their attention and how these processes differ across engagement levels (Baxter & Jack, 2008 ; Yin, 2014). The bounded case is a formal open and distance learning program at a public university in Malaysia, within which nine focal students constitute embedded units of analysis, allowing both in-depth within-learner process tracing and cross-learner comparison across high-, moderate-, and low-engagement profiles (Yin, 2014). Rather than testing a predetermined model, the design is oriented toward unpacking the mechanisms through which learners in this specific context monitor attentional drift, refocus on course tasks, and sustain (or fail to sustain) behavioral, cognitive, and social engagement. An exploratory case study is appropriate because micro-level processes of attention refocusing in authentic ODL settings-and their variation across differently engaged learners—have been only sparsely theorized and empirically documented, particularly in education (Rashid et al., 2019 ; Yazan, 2015 ). By combining multiple qualitative and analytic sources (reflection reports, focus group interviews, and LMS traces) within a single, data-rich case, the design aligns with established case study principles of investigating complex phenomena in context using multiple data sources to enhance analytic depth and credibility (Baxter & Jack, 2008 ; Yin, 2009 ). In this study, the bounded case is a 4 or 5 semester ODL course at a public university in Malaysia. Data collection was organized to capture both individual experience and behavioral evidence of engagement and metacognitive regulation. Near the end of the course, a three-hour focus group interview was conducted with the nine focal students. In addition, LMS trace data (logins, formative assessment completion and scores, and forum participation) were extracted for the nine focal students to construct behavioral engagement profiles. Data analysis proceeded in two linked phases. First, LMS indicators were synthesized into composite engagement scores to derive the three-tier engagement typology that underpins the comparative logic of the study. Engagement tiers were derived using a weighted composite index and a rule-based classification procedure. Second, focus group transcripts and reflection reports were analyzed using qualitative content analysis, with coding focused on episodes in which learners noticed attentional drift and described strategies for refocusing attention. Codes and themes were then compared within and across the three engagement tiers and interpreted alongside LMS profiles to identify convergent and divergent patterns in attention-refocusing mechanisms. By allowing students to articulate their experiences freely in reflection reports, the study sought to identify authentic episodes of cognitive monitoring, strategic control, and evaluative judgment. These responses were then interpreted through reflexive thematic analysis, guided by established theoretical frameworks on metacognitive regulation (e.g., Zimmerman, 2002 ; Winne & Hadwin, 1998 ). This approach aligns with prior research emphasizing that metacognitive processes can manifest implicitly through learners’ natural language and behavior, even in the absence of explicit metacognitive knowledge (Efklides, 2011 ). Thus, the analysis focused not on learners’ use of technical terms but on the underlying regulatory functions embedded within their narratives. This study adopted purposive recruitment within the open and distance learning (ODL) context, combined with voluntary self-selection. Invitation emails were sent to all students enrolled in the relevant ODL courses, and those who were willing to participate responded to the invitation. Nine students ultimately agreed to participate and formed the focal sample for in-depth qualitative analysis. This sampling strategy was appropriate because the study did not aim to achieve statistical representativeness of the entire cohort; rather, it sought to generate an in-depth understanding of metacognitive regulation of learning engagement among participants who were directly situated in the ODL context and able to provide rich accounts of their learning experiences (Creswell, 2014; Yin, 2018 ). Accordingly, the sample is best understood as a volunteer qualitative sample rather than a statistically representative subset of the broader cohort. Table 1 presents the demographic and educational background of the nine focal participants. All are in-service educators enrolled in a bachelor’s degree ODL programme at a public university in Malaysia and are in the later stages of their studies (Semester 4 or 5), having completed 12 ODL courses each. Table 1 Demographic Information and Educational Background of Participants P1 Gender Marital status Education Level Year of Study Background Courses attended number Female Unmarried Bachelor Semester 4 Work at Asia Pacific University 12 P2 Female Unmarried Bachelor Semester 4 Primary school teacher 12 P3 Male Unmarried Bachelor Semester 4 Teacher for 3 months 12 P4 Female Unmarried Bachelor Semester 4 English teacher 12 P5 Male Married and have 3 kids Bachelor Semester 4 College chemistry lecturer 12 P6 Female Married Bachelor Semester 4 Teacher for 12 years 12 P7 Male Married Bachelor Semester 5 Teacher 12 P8 Female Married and have 2 kids Bachelor Semester 5 15 years in a primary school 12 P9 Female Married and have 4 kids Bachelor Semester 5 A lecturer at UIA in the Department of Anesthesiology and Intensive Care 12 The group consists of six females and three males, with a mix of unmarried and married students; several of the married participants have between two and four children. Their professional roles range from primary and secondary school teachers to college lecturers and university lecturers in a medical faculty. Taken together, these characteristics indicate that the focal learners are mature adult students who combine demanding professional duties and, for many, family responsibilities with part-time degree study. At the same time, their substantial prior experience with ODL courses suggests that they are familiar with the LMS and typical course structures. This combination of heavy role demands and accumulated ODL experience is analytically important for the present study: it provides a context in which attentional drift is likely to occur due to time pressure and competing responsibilities, while also allowing us to examine how relatively experienced ODL learners metacognitively regulate and refocus their attention to sustain engagement. 3.2 Defining Engagement Levels To examine how metacognitive regulation operates across different learner profiles, it was first necessary to establish a transparent and analytically tractable categorization of engagement levels. In this study, engagement is treated as a multidimensional construct rather than a single behavioral indicator, and learners are classified into high, moderate, and low engagement groups to support subsequent within-case comparisons. Building on established dimensions of behavioral, cognitive, and social engagement, we developed a composite rating scheme that integrates multiple data sources into a single, interpretable typology. Engagement tiers were derived using a simple weighted composite index and rule-based classification. Additionally, a composite rating method was employed. This method involved clearly defined indicators across three core dimensions of engagement: behavioral, cognitive, and social. Each learner was evaluated within each dimension and assigned a score from 1 to 3, where 3 represented high engagement, 2 moderate engagement, and 1 low engagement. A composite score ranging from 3 to 9 was then calculated by summing the individual scores across dimensions. Learners were grouped as follows: total scores of 8–9 indicated high engagement, 5–7 denoted moderate engagement, and 3–4 indicated low engagement. The engagement classification framework employed a theory-informed weighting scheme of 0.50, 0.30, and 0.20 for behavioral, cognitive, and social engagement, respectively. Behavioral engagement received the highest weight because LMS login activity provided the most continuous and stable trace of routine participation in the present ODL context, consistent with learning analytics research showing that engagement is most frequently operationalized through observable behavioral traces (Bergdahl et al., 2024 ). Accordingly, behavioral engagement received the highest weight because LMS login activity provided the most continuous and stable record of routine participation in this study, while cognitive engagement was represented by formative assessment performance because formative assessment is used to monitor ongoing learning progress and academic development during the learning process (Lu & Cutumisu, 2022 ). Social engagement was assigned a lower weight because forum participation captures an important but more context-dependent form of interaction that may vary with course design and participation requirements rather than learners’ engagement alone. Consistent with multi-component engagement frameworks that do not prescribe canonical weights, our choice prioritizes observability, reliability, and theoretical relevance. The final classification framework is presented in Table 2 below: Table 2 Dimensions, Indicators, Data Sources, and Weights in the Learner Engagement Classification Framework Dimension Indicators Data Source Weight Behavioral Engagement Frequency of LMS logins, number of forum contributions, and formative assessment scores LMS Logs 50% Cognitive Engagement Average score on different course assessments Formative Assessment Score 30% Social Engagement Expression of effort, emotional investment, and reflection on difficulties Participation in Forum (LMS Data) 20% Scoring Criteria for Engagement Classification (1–3 Points) Each learner was evaluated across three engagement dimensions: behavioral, cognitive, and social. For each dimension, students were assigned a score on a 3-point scale (High = 3, Moderate = 2, Low = 1) based on predetermined criteria. These criteria were designed to ensure transparency, consistency, and replicability in the classification process. The scoring rubric is detailed in Table 3 below. This study tiers learner engagement using behavioral, cognitive, and social dimensions and does not include affective engagement in the composite score. The decision is methodological rather than theoretical. While affect is central to engagement theory (e.g., enjoyment, boredom, anxiety) (Pekrun, 2006 ), our dataset lacks objective, consistently sampled, and auditable affect indicators with clear scoring cut-points. In technology-mediated courses, log data and interaction traces readily support behavioral and social metrics, and structured protocol coding supports cognitive strategy use (Henrie, Halverson, & Graham, 2015 ; Broadbent & Poon, 2015 ). Table 3 Scoring Criteria for Behavioral and Social Engagement Based on LMS Data Dimension Score Criteria Description Data Source 1. Behavioral Engagement LMS Data Login Frequency 3 Equal to or above 80% of the group average 2 Within ± 20% of the average 1 Clearly below the average 2. Cognitive Engagement LMS Data Formative Assessment Score 3 ≥ 80% (high performance) 2 60–79% (moderate performance) 1 < 60% (low performance) 3. Social Engagement LMS Data Participation in Forum 3 ≥ 80% (high performance) 2 60–79% (moderate performance) 1 < 60% (low performance) This scoring framework allowed for a nuanced and evidence-based classification of students into high, moderate, or low engagement categories. It also formed the basis for cross-group comparisons in subsequent analyses of metacognitive regulation processes. To assign behavioral engagement scores from log-in frequency, we adopted a three-tier, percentage-based classification rule commonly used in small-sample learning analytics. Specifically, scores were defined as: (1) High = values ≥ 120% of the group mean, (2) Moderate = values falling within ± 20% of the group mean (80%-120%), and (3) Low = values ≤ 80% of the group mean. Percentage-anchored thresholds allow indicators that differ in absolute scale (e.g., raw log-in counts) to be standardised relative to cohort performance, improving cross-participant comparability (Siemens & Baker, 2012 ; You, 2016 ). This strategy is aligned with distribution-based norming frequently applied in learning analytics and educational measurement, where proportional rules yield interpretable and replicable classifications in small samples without requiring distributional assumptions (Tempelaar et al., 2015 ; Cerezo et al., 2016 ). The ± 20% band has been used as a conservative tolerance zone in engagement modelling, reducing the risk of misclassifying marginal fluctuations as meaningful differences (Henrie, Bodily, & Graham, 2015 ). Here, it classifies the composite engagement score into three tiers using fixed, reproducible cut scores: High ≥ 2.50, Moderate = 1.75–2.49, and Low < 1.75. This rule partitions the 1–3 response metric into equal-width intervals, a transparent practice for Likert-type scales when the goal is descriptive comparison rather than latent class discovery (DeVellis, 2017 ; Boone et al., 2014). Conceptually, it is also consistent with distribution-based standard-setting that anchors categories to constant fractions of the scale (or, equivalently, to bands around the mean on the order of ~½ SD), which yields interpretable and replicable thresholds in small-N studies where model-based cut scores would be unstable (Cizek & Bunch, 2007 ; Cohen, 2013 ). We pre-specify the boundaries to facilitate a priori mapping between tiers and our subsequent analysis of refocusing tactics, and we verify robustness by sensitivity checks (e.g., shifting the bounds to 2.40 and 1.80) to ensure findings do not hinge on any single reasonable set of thresholds (Kane, 2013 ; DeVellis, 2017 ). Table 4 provides a quantitative overview of learners’ behavioral engagement, LMS trace data and compiled for the nine focal students across 12 ODL courses. Three indicators were extracted and combined according to the 0.50/0.30/0.20 weighting scheme described above: total login frequency, average formative assessment score, and number of forum contributions. Table 4 LMS-Based Engagement Scores Averaged Across 12 Courses Student ID Login Frequency (50%) Formative Assessment Score (30%) Participation in Forum (20%) Total Score P1 482.9 46.3 18 258.9 P2 369.1 6.5 8 188.1 P3 678.3 37.9 4 351.3 P4 398 30.9 25 213.3 P5 572.9 43.5 22 303.9 P6 357.8 26.6 16 190 P7 739.7 48.3 90 402.3 P8 497.3 51.3 73 278.6 P9 771.6 57.2 161 435.2 P7, P8 and P9 form a high-engagement group, characterized by frequent access, consistently strong formative scores and substantial forum contributions; notably, P7 and P9 display the most intensive and visible participation, while P8 appears to use the platform more efficiently than constantly. P1, P3 and P5 occupy a moderate band, maintaining participation levels sufficient for course progression but with less sustained or socially visible activity, as illustrated by P3’s relatively frequent logins coupled with modest forum use and the generally balanced yet unexceptional profiles of P1 and P5. In contrast, P2, P4 and P6 record sparse logins, very low formative scores and minimal forum postings, yielding the lowest total scores and indicating both weak behavioral presence and limited academic performance rather than “silent but high achieving” engagement. Figure 2 presents a heat matrix illustrating learner engagement across the Behavioural, cognitive, and social dimensions, as well as the overall engagement score. The nine learners were distributed evenly across the three engagement tiers. Three students (P7, P8, and P9) were classified as highly engaged, three (P1, P3, and P5) as moderately engaged, and three (P2, P4, and P6) as low-engagement learners. A clear stratification emerges across participants. P7, P8, and P9 demonstrate consistently high engagement in all three dimensions, reflected by uniformly high intensity in the matrix (scores ≥ 3) and total scores ≥ 2.5. These students maintained frequent interaction with the LMS, achieved high formative assessment performance, and actively participated in forum-based social activities. In contrast, P2, P4, and P6 occupy the lowest band of engagement, with scores clustered at 1 across all dimensions and a total score of 1.0. These participants exhibited limited LMS activity, low formative assessment performance, and minimal social contribution, which suggests weaker self-regulated participation in the ODL environment. The remaining students, P1, P3, and P5, fall within the moderate engagement range, reflected by mixed colours in the heatmap. Their Behavioural and cognitive engagement tends to be stronger than their social participation. The visual gradient suggests that these learners maintained adequate effort and academic processing but did not consistently participate in peer interaction. 4. Metacognitive Regulation in ODL 4.1 Thematic Analysis of Refocusing Strategies within M-C-O During the analysis process, it became evident that attention refocusing emerged as the most salient form of metacognitive control described by participants. Reflection reports and interview transcripts revealed that students often recognized moments of attentional lapses and described various strategies, whether behavioral or cognitive, to redirect their focus toward learning goals. In contrast, instances of other control mechanisms, such as comprehensive strategic revision or emotional regulation, were relatively rare and less elaborate. Consequently, the analytic focus was refined to centre on attention refocusing, capturing both its external manifestations (e.g., changing study environments) and internal processes (e.g., mentally reasserting task priorities). This emphasis not only reflects the predominant patterns observed in the data but also provides a coherent lens through which the dynamics of metacognitive regulation could be meaningfully interpreted. In line with self-regulated learning models that distinguish monitoring, control and evaluation phases of learning (Zimmerman, 2002 ; Panadero, 2017 ), this study conceptualizes each attention-refocusing episode as a Monitor-Control-Outcome (M-C-O) micro-cycle (Fig. 3 ). Learners first notice that their attention has drifted away from the focal task (Monitor), then implement a refocusing response (Control), and finally experience a proximal consequence (Outcome), such as re-entering the task, completing a micro-goal or experiencing reduced stress. The term Outcome is used deliberately to emphasize these short-term, episode-level effects, rather than a full evaluative phase or long-term achievement outcome. Case A (High engagement; P9) Monitor : P9 explicitly notes that time is the constraint, only short windows are available: “The aspect that is still a constraint is time, which cannot be changed or improved.” Control : P9 describes shifting to a phone-first workflow whenever possible: “I try to be consistent and complete tasks little by little according to the time available. I prefer to chase time and use any opportunity to study and do tasks. Even a half-hour window while waiting for the rice to cook can be used productively.” Sub-theme: Fragmented Time Refocusing (TF). Outcome Instead of waiting for the best opportunity, the task was completed step by step. “I try to be consistent and complete tasks little by little according to the time available. I prefer to chase time and use any opportunity to study and do tasks, rather than waiting for the right time.” Case B (Low engagement; P4) Monitor : The team recognized a hard production bottleneck: Powtoon’s free trial lasted only three days, and every slide required voice-over, making the planned workload infeasible within the limit. “Powtoon provides a free trial period of only 3 days… because each video slide requires a lot of attention and needs to be voiced.” Control : They split the template and worked in parallel, and a teammate shared the password of a subscribed Powtoon account so everyone could add the voice-overs and remaining elements. “One of our friends had to share the template with the other 3 of us to work on separately… Our friend had to share the password of the account he subscribed to with all of us so that we could work on voiceovers and other criteria.” Sub-theme: Micro-Tasking to Rebuild Momentum (MRM) Outcome With access and division of labor resolved, the group completed the video with voice-overs, refining character/voice choices that “gave a huge impact to the video.” “We were able to complete the video…” Case C (High engagement, P7) Monitor The learner recognizes that completing the assignment requires mastering multiple new skills and could become overwhelming. Control : Drawing on the personal maxim “want a thousand forces, not a thousand excuses,” he adopts a solution-focused stance: he actively searches for additional information on the internet and deliberately uses any free time to revisit the LMS so that the provided materials are fully utilised. Sub-theme: Self-efficacy Rebuilding (SR) Outcome This motivational and Behavioral stance allows him to keep making progress on the tasks and to maintain confidence in his ability to complete the assignments without experiencing excessive stress. Then this study shows that proactive control is a complementary pattern in highly engaged learners. While the cases above illustrate reactive refocusing, learners first notice that their attention has drifted and then deploy a control move. The narratives of several highly engaged participants revealed a complementary pattern. Rather than waiting to detect distraction, these learners described anticipating likely sources of drift and pre-structuring their time and environments to minimize its occurrence. P5 is better characterized as “plan-control-outcome” proactive management, rather than an on-the-spot narrative of “noticing attention drift and immediately correcting it.” This does not diminish its analytical value; in fact, it nicely complements framework by illustrating how contextual and preparatory strategies can lower the threshold for later refocusing. Case D (High engagement, P7) Planning He anticipates that his attention will be pulled away from class by housework and childcare. Control : He proactively negotiates with his family, informing them in advance and asking his spouse to take over household responsibilities during class time. Sub-theme: Leveraging Schedule Gaps and Family Coordination (LSG) Outcome This secures relatively uninterrupted periods of focus for online learning, effectively protecting a block of time in which sustained attention becomes more feasible. These accounts can be understood as instances of proactive control: learners use their awareness of typical distraction triggers to design schedules, environments, and routines that lower the need for frequent reactive refocusing. Importantly, this pattern does not contradict the M-C-O lens but situates its boundary conditions. For high-engagement learners, attention regulation operates on at least two temporal layers: (a) moment-to-moment corrective moves after drift, as illustrated in the M-C-O episodes above, and (b) longer-horizon, preventive arrangements that make such drift less likely or easier to repair. The present data, therefore, suggest that refocusing on ODL is shaped not only by how learners respond after attention has shifted, but also by how they pre-emptively configure their study contexts to support sustained engagement. 4.2 Mechanisms of Refocusing Attention This section examines how learners metacognitively regain task focus once they become aware of attentional drift in ODL settings. Drawing on framework-based qualitative content analysis of reflection reports and focus group transcripts, we identified seven mechanism families through which students refocused their attention: goal-based refocusing, strategy-based refocusing, affective repair, environmental anchoring, internal conflict reconciliation, temporal anchoring, and motivational repair. In the subsections that follow, we first present the overarching codebook of mechanisms and sub-themes (Table 5 ) and then compare how these mechanisms are differentially orchestrated across high-, moderate-, and low-engagement learners using cross-case matrices (Table 6 ) and within-group distributions (Fig. 4 ). Table 5 represents a cross-case comparative matrix of participants’ attention refocusing strategies following attentional drift, categorized into seven overarching mechanisms. Each theme is further broken down into specific sub-themes grounded in learner narratives and coded using abbreviations. Due to length constraints, a more detailed explanation of the Codebook section is provided in Appendix Table A1. This study identified seven distinct mechanisms by which students refocused their attention after experiencing attentional drift in open and distance learning (ODL) environments: goal-based refocusing, strategy-based refocusing, affective repair, environmental anchoring, internal conflict reconciliation, temporal anchoring, and motivational repair. Each mechanism comprised multiple sub-themes and was enacted through cognitive, behavioral, emotional, and contextual strategies, reflecting the multifaceted nature of metacognitive regulation in digitally mediated, self-directed learning settings. Table 5 Mechanisms and Sub-Themes of Attention Refocusing Code Column/category Label Description DMG GBR (Goal-based refocusing) Daily micro-goal structuring Breaking larger tasks into small daily goals to regain a sense of progress. VAP GBR Visualizing academic and professional advancement Imagining future academic or career outcomes to restore purpose. TF SBR (Strategy-based refocusing) Time fragmentation Splitting study time into short, manageable slots. TSR SBR Task-switching to restore cognitive engagement Switching to another sub-task to refresh attention. MRM SBR Micro-tasking to rebuild momentum Starting with very small, easy tasks to restart work workflow. EDR AR (Affective repair) Emotionally-driven recommitment after language fatigue Using emotional reasons (e.g., personal goals, values) to recommit after feeling tired. ST AR Self-talk for task re-engagement Using inner speech to encourage oneself back to the task. SR AR Self-efficacy rebuilding Reminding oneself of past successes to rebuild confidence. ERL AR Emotional reset and low-stakes re-entry Taking a brief reset and re-entering with low-pressure tasks. FRF AR Fear of regression as a forward-motion catalyst Using fear of falling behind as motivation to move forward. LSG EA (Environmental anchoring) Leveraging schedule gaps and family coordination Using schedule gaps and negotiating with family to secure learning time. ESA EA External support as an attention anchor Getting support from others (e.g., reminders, check-ins) to anchor attention. CDL EA Creating a dedicated and less distracting space Setting up a specific, low-distraction place for online learning. RAP ICR (Internal conflict reconciliation) Recognising and accepting planning failure Acknowledging when original plans failed instead of denying it. APW ICR Awareness of procrastination vs. work demands Noticing the internal conflict between procrastination and actual workload. STR TA (Temporal anchoring) Structured time rituals to reset focus Using fixed routines (e.g., start/stop rituals) to bring attention back. FPR TA Flexible planning and realignment Adjusting timelines and plans to fit current reality. MPV MR (Motivational repair) Mindful pause and value reconnection Pausing to reconnect with personal values behind the task. SIR MR Self-awareness of internal resistance and reigniting enthusiasm Noticing inner resistance and deliberately reigniting interest. IDR MR Identity-driven re-engagement Returning to the task by linking it to one’s desired identity (e.g., future professional, responsible student). Table 5 summarizes 20 sub-themes of attention refocusing, organized into seven mechanism families. Goal-based refocusing (GBR) captures how learners restore focus by breaking work into small daily goals and visualizing future academic or professional outcomes. Strategy-based refocusing (SBR) targets the task structure itself, for example by fragmenting time, switching to a different sub-task, or restarting with very small tasks to rebuild momentum. Affective repair (AR) involves using emotions to re-engage, through self-talk, rebuilding self-efficacy, brief low-stakes re-entry, or even fear of falling behind as a forward-motion trigger. Environmental anchoring (EA) centers on redesigning the study context, such as leveraging schedule gaps, negotiating family support, and creating dedicated low-distraction spaces. Internal conflict reconciliation (ICR) reflects moments when learners acknowledge planning failure or recognize the tension between procrastination and workload, which can open the door to more realistic regulation. Temporal anchoring (TA) refers to structured time rituals and flexible replanning that help reset attention and align tasks with current constraints. Finally, motivational repair (MR) reconnects learners with values and identity through mindful pauses, recognizing inner resistance, and identity-based reminders of who they want to become. Two features are particularly distinctive. First, environmental anchoring and temporal anchoring highlight that attention refocusing in ODL is not only cognitive but deeply embedded in time, family and physical context. Second, motivational and internal-conflict mechanisms show that learners often refocus by renegotiating the meaning of study, accepting imperfections, revisiting values, and linking tasks to a desired future identity, rather than simply “trying harder.” Additionally, the mechanisms of attention refocusing among participants at different levels are illustrated in Table 6 . Table 6 Mechanisms of Refocusing Attention Across Participants Participants GBR SBR AR EA ICR TA MR P1 (Moderate) DMG, VAP CDL, ESA STR P2 (Low) TSR ESA APW STR MPV P3 (Moderate) DMG, VAP ESA STR P4 (Low) DMG, VAP MRM SR ESA IDR P5 (Moderate) SR LSG, ESA APW FPR IDR P6 (Low) DMG, VAP SR LSG, ESA APW STR IDR P7 (High) TF, TSR ST, SR LSG, ESA APW STR, FPR MPV, SIR, IDR P8 (High) VAP SR ESA FPR MPV, SIR P9 (High) DMG, VAP TF, MRM EDR, SR LSG, ESA STR, FPR IDR Note: GBR: Goal-based refocusing; SBR: Strategy-based refocusing; AR: Affective repair; EA: Environmental anchoring; ICR: Internal conflict reconciliation; TA: Temporal anchoring; MR: Motivational repair Table 6 shows that learners classified as high engagement activate a broad and integrated repertoire of mechanisms and frequently chain them within a single episode of refocusing. A typical sequence begins with Goal-Based Refocusing, daily micro-goal structuring and/or visualization of distal academic-professional milestones (DMG/VAP)-immediately followed by Strategy-Based Refocusing (e.g., task-switching or micro-tasking to lower cognitive load: TF/MRM/TSR). This is then stabilized through Temporal Anchoring (structured rituals and flexible replanning: STR/FPR) and Environmental Anchoring (leveraging schedule gaps and external supports: LSG/ESA). High-tier participants also invoke Motivational Repair (identity/values prompts; IDR/MPV/SIR) and, when needed, Affective Repair (self-talk and efficacy rebuilding; EDR/SR). High engagement is characterized by proactive orchestration: learners couple distal goal signals with near-term micro-tactics and contextual/time scaffolds, producing resilient re-entry into task focus. Moderate engagement learners deploy a stable but thinner stack of strategies. Their core configuration combines Goal-Based Refocusing (DMG/VAP) with Environmental Anchoring (ESA/CDL) and Temporal Anchoring (most often STR). Affective or Motivational devices appear episodically (e.g., SR, IDR), and Internal-Conflict Reconciliation (APW) are used to surface procrastination-work trade-offs, though it is less frequently translated into concrete replans than in the high tier. Moderates establish structure and context reliably but chain fewer mechanisms and show less micro-tactic variety than high-engagement peers. The low tier relies on piecemeal, context-driven fixes rather than coordinated sequences; the coupling between long-range aims and near-term tactics is weak. Low engagement profiles exhibit reactive and narrow deployment of mechanisms. Learners tend to use single Strategy-Based pivots (TSR/MRM) or Temporal resets (STR) in isolation, often after a derailment, with Environmental supports (ESA/LSG) compensating for limited self-structuring. Goal signals (DMG/VAP) are intermittent and typically lack explicit next-action decomposition. Motivational/affective tools are lighter-touch and rarely integrated with planning. To further clarify how these mechanisms cluster within different levels of engagement, we examined the relative share of each strategy family within the low-, moderate-, and high-engagement groups. Figure 4 displays the within-group distribution of goal-based, strategy-based, affective, environmental, internal, temporal, and motivational refocusing strategies. The figure shows that, although environmental anchoring is used across all three groups, high-engagement learners draw on a broader portfolio that combines strategy-based and motivational repair, whereas moderate-engagement learners are more context- and time-centric and low-engagement learners rely mainly on goal- and context-based cues. Across engagement tiers in Fig. 4 , several contrasts emerge. High-engagement learners display the clearest distinguishing feature: a pronounced reliance on motivational repair (about one-fifth of their coded strategies), complemented by substantive use of affective regulation, environmental adjustments, and temporal anchoring. Their profile suggests a broader, more self-energizing repertoire that rebuilds meaning and emotion while orchestrating concrete re-entry routines. In contrast, the moderate group concentrates on environmental anchoring and temporal structuring but shows no evidence of strategy-based refocusing. Their re-engagement appears to come from altering context and schedule rather than changing the way tasks are approached. Low-engagement learners, meanwhile, lean on goal-based cues and environmental tweaks, with comparatively limited use of temporal rituals and motivational repair, indicating attempts to restart through proximal goals and workspace adjustments rather than through time scaffolds or value reconnection. Despite these differences, a common baseline is visible: environmental anchoring is used at meaningful rates in all groups, marking “context engineering” as a shared mechanism of refocusing in ODL settings. What varies is portfolio breadth. The high-engagement group distributes its use more evenly across families, including strategy-based and motivational repair-whereas the moderate group is narrowly context/time-centric, and the low group is primarily goal/context-centric. This gradient is consistent with the view that a more diversified repertoire of refocusing tactics co-varied with, and may help sustain, higher engagement. Finally, apparent similarity in the bar chart partly reflects within-group normalization and small cell counts. Even so, qualitative contrasts remain robust: the absence of strategy-based tactics in the moderate group, elevated motivational repair among high-engagement learners, and under-use of temporal anchoring in the low-engagement group. These patterns align with the theory linking sustained engagement to both regulatory breadth and the capacity to restore meaning and affect when effort flags. 4.3 Classification of Refocusing Types: Cognitive and Behavioral Refocusing Refocusing strategies used by ODL students can be categorized into two overarching types: cognitive refocusing and behavioral refocusing. As summarized in Table 7 , cognitive refocusing refers to the internal restoration of learning goals through mental processes such as self-reflection, emotional regulation, and planning. In contrast, behavioral refocusing involves observable, task-based actions that help learners resume task engagement, such as switching to a simpler activity, physically changing environments, or resuming study after a short rest. Table 7 Types of Attention Refocusing Strategies with Descriptions, Examples, and Proportional Usage Type Description Example Percentage (%) Behavioral Refocusing Get back on task through observable, concrete behavior Get up and rest, come back and continue, start the question again, switch to a simple task 52.3% Cognitive Refocusing Mentally restore learning goals through internal reflection, goal review, and emotion regulation Mentally plan the next step, encrypt learning goals, and remind yourself 47.7% Sub-themes categorized under Cognitive Refocusing included strategies such as visualizing academic or professional advancement (VAP), identity-driven re-engagement (IDR), self-talk for task re-engagement (ST), and self-efficacy rebuilding (SR). Participants also demonstrated reflective mechanisms such as recognizing and accepting planning failure (RAP), becoming aware of procrastination and work conflict (APW), and engaging in emotionally grounded resets (e.g., mindful pause and value reconnection, MPV). These processes illustrate the role of internal cognitive and affective control in regulating attention and re-aligning focus with personal goals. In contrast, strategies categorized under behavioral refocusing were more overt and action based. These included daily micro-goal structuring (DMG), task switching (TSR), micro-tasking to rebuild momentum (MRM), and emotional reset through low-stakes re-entry (ERL). Learners also described leveraging structured time rituals (STR), flexible planning and rescheduling (FPR), and redesigning study environments (CDL) to support renewed concentration. These strategies reflect intentional adaptations of physical and temporal contexts to support task continuity and reduce attentional interference. Proportional Breakdown of Refocusing Types Total distinct sub-themes identified across all participants = 20 Purely Cognitive Refocusing: 9 sub-themes Purely Behavioral Refocusing: 10 sub-themes Belong to Both (Overlap): 3 sub-themes Since overlapping themes are shared, we calculate their contribution as 1.5 each for both categories to avoid double-counting : Adjusted Cognitive Count = 9 + 1.5 (ERL, STR, FPR) = 10.5 Adjusted Behavioral Count = 10 + 1.5 = 11.5 Final Proportions : Cognitive Refocusing: 10.5 / (10.5 + 11.5) = 47.7% Behavioral Refocusing: 11.5 / (10.5 + 11.5) = 52.3% A frequency analysis across all identified sub-themes showed that out of 20 distinct strategies, 9 were purely cognitive, 10 were purely behavioral, and 3 exhibited hybrid features. When adjusted for overlap, the proportion of behavioral refocusing strategies accounted for approximately 52.3%, while cognitive refocusing strategies comprised 47.7% of the total. This near-equivalent distribution suggests that both forms of refocusing play critical roles in supporting sustained attention in ODL. However, the slight dominance of behavioral strategies indicates that learners may more frequently rely on tangible, task-oriented responses to drift, especially in asynchronous, high-autonomy learning environments where external scaffolds are minimal. Overall, these findings underscore that attentional regulation in ODL is a multidimensional process requiring both action and reflection. While behavioral responses help restore focus immediately and pragmatically, cognitive strategies ensure that this refocusing remains meaningful, sustainable, and aligned with learners’ broader academic identity and goals. The classification of these mechanisms provides a useful theoretical distinction for future research and offers practical implications for instructional design, such as balancing structural supports with tools that foster internal self-regulation. 4.4 Triangulated Evidence for Engagement and Metacognitive Regulation To examine differences in metacognitive regulation patterns, learners were categorized into high, moderate, and low engagement groups based on a triangulated analysis of three data sources: (1) LMS behavioral records (including login frequency, group forum participation, and formative assessment scores), (2) student reflection reports, and (3) focus group interview transcripts. This multi-source data approach enabled a more comprehensive and contextualized understanding of learner engagement. Taken together, these LMS-based profiles provide a robust quantitative basis for classifying learners into low, moderate, and high engagement tiers. These quantitative patterns are then triangulated with evidence from student reflection reports and focus group interviews to develop a more contextualized interpretation of learners’ engagement and metacognitive regulation. Table 8 presents this triangulated evidence across the nine participants. Table 8 Triangulation of Engagement and Refocusing Mechanisms Participant (tier) LMS evidence of engagement Qualitative evidence (very brief) Triangulated interpretation P1-Moderate Mid-range logins and scores; limited forum use. Uses micro-goals and visualizing course completion to restart work. Behavioral engagement is adequate; refocusing is mainly goal-based but not very frequent. P2-Low Lowest total score; few logins, weak formative results, minimal posts. Reports fatigue after work and often “cannot think straight”; relies on switching to easier tasks. LMS and narrative converge on fragile engagement with short, reactive refocusing episodes. P3-Moderate High logins but modest assessment and forum activity. Describes setting daily goals but sometimes postponing tasks. Appears present in the LMS, but learning effort is uneven; refocusing is used, yet inconsistently. P4-Low Below-average logins and performance; low interaction. Starts with small actions (one article, one forum post) when overwhelmed. Quantitative and qualitative data both indicate hesitant, low-intensity engagement P5-Moderate Mid-range on all three indicators. Plans around work and family; uses environmental and temporal arrangements to secure study time. Engagement is steady but not high; refocusing often takes a proactive, planning-oriented form. P6-Low Similar to P2/P4: low logins, low scores, minimal forum use. Mentions struggling to keep up and needing to “push” herself to return to tasks. Data suggest persistent difficulties sustaining participation and only sporadic refocusing. P7-High Very high logins and strong assessment; active in forums. Emphasizes persistence (“no excuses”), self-talk and task-switching within the course. LMS pattern and narratives align as a highly engaged learner who uses rich, strategy-based refocusing. P8-High High scores across indicators, especially formative assessment and forums. Uses visualizing academic goals and brief “restart” steps to get back on task. Strong cognitive and social engagement supported by robust goal-based and affective refocusing. P9-High Highest overall engagement; most logins and forum posts. Exploits micro-windows to study, negotiates family support, and uses multiple refocusing strategies. and Motivational repair (IDR). Consistent high engagement with diversified refocusing repertoire, matching her top LMS profile. LMS-based engagement patterns across the nine learners show a clear three-tier structure. Aggregating login frequency, formative assessment scores, and forum participation across twelve ODL courses, three students (P7, P8, P9) exhibit consistently high engagement, three (P2, P4, P6) show low engagement, and the remaining three (P1, P3, P5) fall in a moderate range. P9 has the highest composite score (435.2), combining the most frequent logins (771.6 on average) with the highest formative performance and forum participation (161 posts), indicating sustained behavioral, cognitive, and social involvement across courses. P7 and P8 also score well above the sample mean on all three indicators, particularly in their participation in discussion forums, suggesting that highly engaged learners not only access the LMS frequently but also contribute actively to interactive activities. By contrast, P2, P4, and P6 cluster at the lower end of the distribution. Their total scores (around 188–213) reflect fewer logins, weaker formative assessment performance, and minimal forum activity. For example, P2 has one of the lowest formative assessment averages (6.5) and only eight forum posts across courses, which is consistent with a pattern of sporadic, low-intensity engagement. P4 and P6 show slightly higher scores on some dimensions, but their overall profiles remain clearly below the group average. The moderate group (P1, P3, P5) displays more nuanced patterns. P3, for instance, records relatively high login frequency (678.3) but low forum participation (4 posts), suggesting that frequent access to the LMS does not automatically translate into interaction or deeper engagement. P1 and P5 sit near the middle of the sample on all three indicators, with neither pronounced strengths nor severe weaknesses. 5. Discussion and conclusion This study sets out to explore how learners in an open and distance learning (ODL) environment experience challenges to learning engagement, how they use metacognitive regulation to refocus their attention after monitoring, and how these processes differ across students with high, moderate, and low levels of engagement. Drawing on reflection reports, focus group interviews, and learning management system (LMS) traces from a group of students, the research adopted an embedded single-case design and used a Monitor-Control-Outcome (M-C-O) heuristic to organize fine-grained episodes of attention regulation. The findings extend existing work on self-regulated learning, metacognition, and online engagement by linking learners’ moment-to-moment regulatory episodes to empirically derived engagement tiers in a real-world ODL context. The first analytic step of this study asked how ODL learners describe the micro-processes through which they bring their attention back after drift, and how these episodes can be meaningfully represented within a Monitor-Control-Outcome (M-C-O) framework. Section 4.1 showed that learners’ narratives consistently followed this structure: they first noticed that attention or progress was being disrupted (Monitor), then implemented a refocusing move (Control), and finally reported a proximal consequence (Outcome), such as being able to resume the task, complete a small portion of work, or feel less stressed. These episodes predominantly took the form of reactive refocusing (“I realized I was off task, so I…”), but among highly engaged learners there was also evidence of proactive control, in which learners anticipated typical sources of distraction and pre-structured their time, environment, and social arrangements to make sustained attention more feasible (e.g., negotiating family support, planning to use short time gaps, or shifting to a phone-first workflow). The M-C-O schema captured these micro-cycles and showed that attention regulation in ODL is iterative, experimental, and often messy rather than linear and neatly staged. Alongside reactive refocusing after monitoring drift, the study also identified proactive or preventive cycles of metacognitive regulation. Several learners, particularly those in the high-engagement group, described ritualized planning routines, such as a daily “power hour” in which they reviewed their workload, clarified priorities, and converted goals into concrete next actions and time blocks. Other pre-negotiated household roles reserved protected study windows, or pre-structured task sequences before busy periods. These forward-looking routines did not depend on monitoring and regulation; instead, they anticipate potential distractions and prepare accordingly. The analysis of strategy-family shares addressed how the composition of attention-refocusing repertoires differs across low-, moderate- and high-engagement learners. Overall, all three groups showed meaningful use of environmental anchoring, indicating that “context engineering” (e.g., schedule gaps, study spaces, external supports) is a baseline mechanism of refocusing in ODL. What differentiated the groups was the breadth and balance of their portfolios. High-engagement learners drew on a diversified mix of families, with motivational repair accounting for about one-fifth of their coded strategies, alongside substantial affective, temporal and environmental refocusing. Moderate-engagement learners exhibited a narrower, context- and time-centric profile dominated by environmental anchoring and temporal structuring, with no observed strategy-based refocusing. Low-engagement learners relied mainly on goal-based cues and environmental tweaks, with comparatively little temporal anchoring or motivational repair. Framework-based qualitative content analysis of reflection reports and focus-group data yielded seven mechanism families: goal-based, strategy-based, affective, environmental, internal conflict, temporal and motivational refocusing comprising 20 sub-themes. These patterns are broadly consistent with self-regulated learning (SRL) research showing that successful learners tend to combine multiple strategy families: cognitive, metacognitive and resource-management strategies, rather than relying on a single tactic. In large-scale studies and meta-analyses of online and continuing education, goal setting, strategic planning, time management and environment structuring all emerge as positive predictors of persistence and achievement. Our high-engagement learners exemplify this “multi-strategy profile”: they couple distal goal representations with concrete task-level maneuvers and with deliberate management of time blocks and study spaces. By contrast, the narrower and more reactive repertoires observed in the low-engagement group resemble SRL profiles characterized by limited strategy use and weaker outcomes in prior work. The results also diverge from some prior descriptions of “strategic” learners. Quantitative studies sometimes portray moderate achievers as characterised by strong time management and environment structuring (e.g., regular study schedules) with relatively less need for intensive motivational repair (Broadbent & Poon, 2015 ). In our sample, however, moderate-engagement learners’ context- and time-centric profiles were not accompanied by rich cognitive or motivational repertoires and were associated with only mid-level LMS engagement. One plausible explanation is the adult ODL context: for learners juggling work and family responsibilities, temporal and environmental structuring may be necessary just to maintain minimal participation, while sustained high engagement additionally requires strong motivational repair and strategy-based adaptation. The contrast between proactive and mixed strategies in the high-engagement group and isolated, reactive fixes in the low-engagement group speaks to the distinction between proactive and reactive control discussed in the mind-wandering and cognitive-control literature (Braver, 2012 ; Mittelstädt et al., 2024 ). Experimental and neurocognitive studies indicate that sustained attention and goal maintenance rely heavily on proactive control-anticipating demands and maintaining goal representations whereas purely reactive adjustments after conflict are less effective for preventing mind wandering (He et al., 2023 ; He et al., 2024 ). Our data echo this distinction in a real-world ODL context: high-engagement learners not only correct after drift but also pre-structure time and context so that drift is less likely or easier to repair, whereas low-engagement learners tend to “firefight” episodes as they arise. Differences in prior SRL experience, workload, and access to supportive environments may all contribute to these divergent patterns. Several limitations of this study should be acknowledged when interpreting the findings. First, the research adopts an embedded single-case design focused on one formal ODL program at a single institution, with nine focal learners and a wider set of reflection reports from peers. The small, context-specific sample and the institutional and cultural setting mean that the patterns identified here are. They are likely to resonate most strongly with adult, working learners in similar ODL programs and may not fully capture the experiences of younger, campus-based or non-working student populations. These sources provide rich reconstructions of attention-refocusing episodes but remain vulnerable to recall bias and selective reporting; they also foreground episodes that learners themselves perceived as salient. Declarations Ethics statement The study complied with internationally recognized ethical principles (e.g., the Declaration of Helsinki) and relevant institutional and national guidelines. The research involved minimal risk: no clinical procedures were conducted, no minors or vulnerable groups were recruited, and no sensitive personal or health-related data were collected. All participants were adult students enrolled in an open and distance learning program. Recruitment was conducted via email, and students who wished to participate provided written informed consent by replying to the invitation email to indicate their voluntary agreement to take part. In the consent information, they were briefed on the study purpose, procedures, confidentiality safeguards, and their right to withdraw at any time without penalty. Data was anonymized at the point of analysis and is reported only in aggregated form. According to the applicable institutional regulations for minimal-risk educational research of this type, formal IRB/ethics committee approval was not required. This study was approved by the Universiti Kebangsaan Malaysia. Clinical trial number Not applicable. Funding This research is conducted under GG-2021-030 Research Grant. Author Contribution Xue Yanxing conducted the main work of the study, including the conception and design of the research, literature review, data collection, data analysis, interpretation of the results, and drafting of the manuscript. Xue Yanxing also prepared the tables and figures and organized the manuscript structure. Fariza Khalid supervised the research process, provided academic and methodological guidance, contributed to the refinement of the study design and interpretation of the findings, and critically revised the manuscript for important intellectual content. Both authors read and approved the final manuscript. Data Availability The datasets generated and/or analysed during the current study are not publicly available because they contain qualitative data from human participants and may compromise participant privacy and confidentiality. The data are, however, available from the corresponding author on reasonable request. References Anthonysamy, L. The use of metacognitive strategies for undisrupted online learning: Preparing university students in the age of pandemic. Educ. Inform. Technol. 26 (6), 6881–6899 (2021). Bağrıacık Yılmaz, A. & Karataş, S. Why do open and distance education students drop out? Views from various stakeholders. Int. J. Educational Technol. High. Educ. 19 (1), 28 (2022). Baxter, P. & Jack, S. Qualitative case study methodology: Study design and implementation for novice researchers. qualitative Rep. 13 (4), 544–559 (2008). Bergdahl, N., Bond, M., Khosravi, H. & Oxley, E. Unpacking student engagement in higher education learning analytics: A systematic review. Int. J. 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Behavioral, cognitive, emotional and social engagement in mathematics learning during COVID-19 pandemic. PloS one , 17 (11), e0278052. (2022). Kahmann, R., Droop, M. & Lazonder, A. W. Meta-analysis of professional development programs in differentiated instruction. Int. J. Educational Res. 116 , 102072 (2022). Kane, M. T. Validating the interpretations and uses of test scores. J. Educ. Meas. 50 (1), 1–73 (2013). Kauffman, D. F. Self-regulated learning in web-based environments: Instructional tools designed to facilitate cognitive strategy use, metacognitive processing, and motivational beliefs. J. Educational Comput. Res. 30 (1–2), 139–161 (2004). Kurzban, R., Duckworth, A., Kable, J. W. & Myers, J. An opportunity cost model of subjective effort and task performance. Behav. Brain Sci. 36 (6), 661–679 (2013). Lan, M. & Zhou, X. A qualitative systematic review on AI empowered self-regulated learning in higher education. npj Sci. Learn. 10 (1), 21 (2025). Lu, C. & Cutumisu, M. Online engagement and performance on formative assessments mediate the relationship between attendance and course performance. Int. J. educational Technol. High. Educ. 19 (1), 2 (2022). Macfadyen, L. P. & Dawson, S. Mining LMS data to develop an early warning system for educators: A proof of concept. Comput. Educ. 54 (2), 588–599 (2010). Martin, F. & Bolliger, D. U. Engagement matters: Student perceptions on the importance of engagement strategies in the online learning environment. Online Learn. 22 (1), 205–222 (2018). Mittelstädt, V., Mackenzie, I. G., Braun, D. A. & Arrington, C. M. Reactive and proactive control processes in voluntary task choice. Mem. Cognit. 52 (2), 417–429 (2024). Panadero, E. A review of self-regulated learning: Six models and four directions for research. Front. Psychol. 8 , 422 (2017). Pekrun, R. The control-value theory of achievement emotions. Educational Psychol. Rev. 18 (4), 315–341 (2006). Pesout, O. & Nietfeld, J. L. How creative am I? Examining judgments and predictors of creative performance. Think. Skills Creativity . 40 , 100836 (2021). Rahmani, A. M., Groot, W. & Rahmani, H. Dropout in online higher education: a systematic literature review. Int. J. Educational Technol. High. Educ. 21 (1), 19 (2024). Rashid, Y., Rashid, A., Warraich, M. A., Sabir, S. S. & Waseem, A. Case study method: A step-by-step guide for business researchers. Int. J. qualitative methods . 18 , 1609406919862424 (2019). Robison, M. K. & Unsworth, N. Cognitive and contextual correlates of spontaneous and deliberate mind-wandering. J. Experimental Psychology: Learn. Memory Cognition . 44 (1), 85–98 (2018). Robison, M. K., Miller, A. L. & Unsworth, N. A multi-faceted approach to understanding individual differences in mind-wandering. Cognition 198 , 104078 (2020). Schraw, G. & Moshman, D. Metacognitive theories. Educational Psychol. Rev. 7 (4), 351–371 (1995). Seli, P., Schacter, D. L., Risko, E. F. & Smilek, D. Increasing participant motivation reduces rates of intentional and unintentional mind wandering. Psychol. Res. 83 (5), 1057–1069 (2019). Siemens, G. & Baker, R. S. Learning analytics and educational data mining: Towards communication and collaboration. In Proceedings of the 2nd International Conference on Learning Analytics and Knowledge (pp. 252–254). (2012). Sulisworo, D., Fatimah, N. & Sunaryati, S. S. A Quick Study on SRL Profiles of Online Learning Participants during the Anticipation of the Spread of COVID-19. Int. J. Evaluation Res. Educ. 9 (3), 723–730 (2020). Szpunar, K. K., Khan, N. Y. & Schacter, D. L. Interpolated memory tests reduce mind wandering and improve learning of online lectures. Proceedings of the National Academy of Sciences , 110(16), 6313–6317. (2013). Tempelaar, D., Rienties, B. & Giesbers, B. In search for the most informative data for feedback generation: Learning analytics in a data-rich context. Comput. Hum. Behav. 47 , 157–167 (2015). Tiruneh, D. T., Verburgh, A. & Elen, J. Effectiveness of Critical Thinking Instruction in Higher Education: A Systematic Review of Intervention Studies. High. Educ. Stud. 4 (1), 9–44 (2014). Unsworth, N. & McMillan, B. D. Mind wandering and reading comprehension: Examining the roles of working memory capacity, interest, motivation, and topic experience. J. Experimental Psychology: Learn. Memory Cognition . 39 (3), 832–842 (2013). Winne, P. H. & Hadwin, A. F. Studying as self-regulated learning. In (eds Hacker, D. J., Dunlosky, J. & Graesser, A. C.) Metacognition in educational theory and practice (277–304). Lawrence Erlbaum. (1998). Yazan, B. Three approaches to case study methods in education: Yin (Merriam, and Stake, 2015). Yin, R. K. Case study research: Design and methods (Vol. 5). sage. (2009). Yin, R. K. Case study research and applications Vol. 6 (Sage, 2018). You, J. W. Identifying significant indicators using LMS data to predict course achievement in online learning. Internet High. Educ. 29 , 23–30 (2016). Zanesco, A. P., Van Dam, N. T., Denkova, E. & Jha, A. P. Measuring mind wandering with experience sampling during task performance: An item response theory investigation. Behav. Res. Methods . 56 (7), 7707–7727 (2024). Zawacki-Richter, O., Marín, V. I., Bond, M. & Gouverneur, F. Systematic review of research on artificial intelligence applications in higher education–where are the educators? Int. J. educational Technol. High. Educ. 16 (1), 1–27 (2019). Zhang, W. et al. Interplay of student characteristics multidimensional engagement and influencing factors in online computer science education. Sci. Rep. 15 (1), 6976 (2025). Zimmerman, B. J. Becoming a self-regulated learner: An overview. Theory Into Pract. 41 (2), 64–70 (2002). Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9269094","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":625356402,"identity":"1908bdf9-a190-42a6-9f9a-888fedc14868","order_by":0,"name":"Yanxing Xue","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBAC+2bmA4d//LCRY2CGCBiACAl8WgyOsyU+ZuxJMyZBy3keY2MGtsOJDTABwloOM5hJF/Ckpc9vZ38m8YPhsLFuA/PB2zwM2+CGYPqFIU16hoVN7obDPGaSPQyHzcwOsCVb8zDcxqnFgJnhmAQPT1ruBmYeths8DIdtzA7wmEnj18LYJsHDdjhdvpn92c0/YC383whoYWY2BmpJYAB6CugFkMN42AhoYWN8OLMnzRDoF/PfMgbpxmaH2Ywt5xjcNsaphf/8hwMfftjIy/cff2z4psLacNvx5oc33lTclsWlBd0EIGaGMByJ1IIE7EnWMQpGwSgYBcMVAAA7sFTxXIuggQAAAABJRU5ErkJggg==","orcid":"","institution":"National University of Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Yanxing","middleName":"","lastName":"Xue","suffix":""},{"id":625356404,"identity":"06d39833-c42e-4327-a4fd-2206b2da2523","order_by":1,"name":"Fariza khalid","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Fariza","middleName":"","lastName":"khalid","suffix":""}],"badges":[],"createdAt":"2026-03-30 15:25:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9269094/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9269094/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107619825,"identity":"de2e1215-bfec-4c95-9111-6fe4326f068c","added_by":"auto","created_at":"2026-04-23 09:32:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":57138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCyclical Model of Attention Regulation Mediated by Attribution in ODL Contexts\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9269094/v1/8a7d029778f9d6f0b425550a.png"},{"id":107705811,"identity":"bb9f37be-fb12-4563-86d1-5685e0640c04","added_by":"auto","created_at":"2026-04-24 09:15:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":50995,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeat Matrix of Learner Engagement\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9269094/v1/6ad79b4a76d46bf65b573e74.png"},{"id":107619827,"identity":"72f42850-f0db-43af-92fa-bbb8663c251d","added_by":"auto","created_at":"2026-04-23 09:32:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMonitor-Control-Outcome (M-C-O) micro-cycle of attention refocusing\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9269094/v1/03e07699ba4635a4e3c8646a.png"},{"id":107619829,"identity":"11e91c1d-db39-4d2f-92ab-f2207eef7040","added_by":"auto","created_at":"2026-04-23 09:32:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":81674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Attention Refocusing Strategy Families Across Engagement Groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-9269094/v1/421be63ca6d23ff7cf56ff3e.png"},{"id":108677205,"identity":"61db4179-2c82-46f2-b5b5-c33dd777669a","added_by":"auto","created_at":"2026-05-07 08:43:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":848752,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9269094/v1/9edadfdf-e9af-4caf-a578-05cc267406ee.pdf"},{"id":107619828,"identity":"2b94dba8-3b03-4f33-958a-1f24e20dbe61","added_by":"auto","created_at":"2026-04-23 09:32:24","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19283,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9269094/v1/069a3c5ac59bf78a7a20ccd0.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Who Gets Back on Track? Engagement-Level Differences in Metacognitive Attention Refocusing in Open and Distance Learning","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe broader landscape of online learning now reaches a wide and diverse population, freely accessible online courses and open resources, such as MIT Open Courseware and its recent graduate-level course \u0026ldquo;How to AI (Almost) Anything,\u0026rdquo; illustrate how learners anywhere with an internet connection can engage with cutting-edge university content without physical co-presence (He et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Against this backdrop, it places heavy demands on learners\u0026rsquo; ability to sustain engagement without the temporal and social structure of face-to-face classrooms (Bond \u0026amp; Bergdahl, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In asynchronous, screen-based environments, students must decide when to log in, how long to persist, and how to deal with frequent internal and external distractions. Prior research consistently shows that difficulties in maintaining attention on course tasks are a major source of low engagement, fragmented participation, and eventual dropout in ODL settings (Bağrıacık Yılmaz, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Rahmani et al., \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt the same time, the rapid proliferation of generative artificial intelligence has automated access to information and many routine cognitive operations in higher education. Rather than reducing the need for learner agency, this shift arguably heightens the importance of self-regulated and metacognitively regulated learning; recent reviews of AI-supported and AI-empowered self-regulated learning indicate that intelligent and generative tools can scaffold goal setting, monitoring and strategy adjustment only when learners actively engage in these regulatory processes instead of outsourcing thinking to the system (Jin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lan \u0026amp; Zhou, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Zawacki-Richter et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In parallel, research on mind wandering and spontaneous thought has demonstrated that attention drift is a ubiquitous feature of cognition and that its impact on learning depends on whether individuals notice the shift and deliberately redirect attention back to task-relevant goals (B\u0026uuml;hler et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Szpunar et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). From this perspective, metacognitive regulation, especially the capacity to detect attention drift and refocus on meaningful learning goals, emerges as a core human capability for sustaining deep engagement with complex tasks in AI-saturated learning environments (Jin et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Lan \u0026amp; Zhou, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Understanding how ODL learners perform this moment-to-moment regulation, and why high- and low-engagement learners differ in their refocusing patterns, is therefore both theoretically and practically significant.\u003c/p\u003e \u003cp\u003eIn early 2020, schools were closed worldwide due to the outbreak of the COVID-19 pandemic. During this period, ODL expanded rapidly and has increasingly been viewed as an important direction for the future of education (Bond et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In ODL, where physical separation between instructors and learners is the norm, fostering robust student engagement is essential for successful learning outcomes (Bond et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Ghosh et al., 2012). Student engagement, a multifaceted construct encompassing cognitive, emotional, and behavioral dimensions, lies at the heart of effective learning experiences in ODL environments. It refers to the extent to which learners actively participate, invest effort, and connect meaningfully with their educational pursuits.\u003c/p\u003e \u003cp\u003eThe significance of student engagement extends beyond academic attainment and includes broader socio-emotional outcomes such as satisfaction, retention, and persistence. In ODL environments, where learners often juggle multiple commitments and responsibilities, cultivating metacognitive ability, a sense of belonging, and connectedness can play a pivotal role in promoting student success and well-being (Martin \u0026amp; Bolliger, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Understanding the dynamics of student engagement in ODL, therefore, requires a nuanced exploration of the factors that shape learners' experiences and behaviors (Bond \u0026amp; Bergdahl, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recent work highlights the crucial role of metacognitive regulation in promoting active and meaningful learning in ODL and underscores the importance of integrating metacognitive instruction and support services into distance learning programs (Anthonysamy, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA more specific gap in the current literature concerns the moment-to-moment mechanisms through which learners regain focus after attention drift in online and distance learning contexts. Although prior research acknowledges that metacognitive regulation is critical in ODL, particularly because learners must manage their own attention without the external structure of face-to-face classrooms (Hartley, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Kauffman, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), existing studies tend to treat regulation as a broad construct, often measured through global scales or general SRL behaviors. What remains underexplored is the micro-level process of attention refocusing, that is, how learners notice attention drift and what specific strategies they use to bring their focus back to task.\u003c/p\u003e \u003cp\u003eMoreover, recent studies have conceptualized online learning engagement as a multidimensional construct encompassing behavioral, cognitive, emotional, and social dimensions (Heilporn et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Joshi et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), yet very few have examined how attention-refocusing strategies vary across learners with different profiles of behavioral, cognitive, and social engagement. There is a notable absence of in-depth, case-based analyses that compare high- and low-engagement learners to uncover heterogeneous patterns of refocusing, or that trace the mechanisms by which learners with varying engagement profiles regulate their attention during authentic ODL learning. This study addresses this gap by analyzing refocusing episodes among ODL learners and mapping how different engagement tiers employ distinct metacognitive strategies to recover from attention drift. The central research question is: \u003cb\u003eHow do students engage in metacognitive regulation, including refocusing attention, in the context of open and distance learning?\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study addresses these gaps by conducting a case analysis of students enrolled in ODL courses at a public university in Malaysia. It first characterizes learning engagement along behavioral, cognitive, emotional, and social dimensions and classifies learners into high-, moderate-, and low-engagement profiles based on a combination of LMS indicators evidence. Building on this typology, the study focuses specifically on attention refocusing as a distinct phase of metacognitive regulation, tracing how students in each engagement tier deploy strategies to bring their focus back to the task. By comparing the effectiveness of attention-refocusing episodes across differently engaged learners, the study illuminates the mechanisms through which metacognitive regulation supports or fails to support sustained engagement in ODL. These findings, grounded in interdisciplinary perspectives from psychology, education, and learning technologies, provide theoretical and practical guidance for designing more student-centred support systems and instructional practices to enhance engagement in open and distance learning environments.\u003c/p\u003e \u003cp\u003eIn response to these gaps, this study makes three main contributions to the literature. First, it offers a mechanism-focused account of how ODL learners refocus their attention after drift, by conceptualizing refocusing episodes such as Monitor-Control-Outcome micro-cycles and inductively mapping a repertoire of refocusing strategies (e.g., goal-based and strategy-based refocusing, affective repair, environmental and temporal anchoring, motivational repair). Second, it links these mechanisms to heterogeneous patterns of engagement: combining LMS traces with reflection data, it compares how high-, moderate- and low-engagement learners differ in their refocusing repertoires. Third, methodologically, the study demonstrates how qualitative episode-based coding can be integrated and analyze metacognitive regulation processes in small but data-rich ODL case studies. Together, these contributions extend existing self-regulated learning and mind-wandering research from global reports of attention problems to a finer-grained description of how learners detect attention drift and bring their focus back to task in authentic ODL environments.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the relevant literature on ODL, student engagement, and metacognitive regulation. Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e describes the research context, participants, and methodological approach of the case study. Section \u003cspan refid=\"Sec11\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents empirical findings on learning engagement and metacognitive regulation among students in ODL. Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e5\u003c/span\u003e discusses these findings in relation to existing theory and prior research and draws out their practical implications, and concludes by summarizing the main contributions, noting the limitations of the study, and suggesting directions for future research.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Open and Distance Learning, Self-Regulation, and Metacognitive Regulation\u003c/h2\u003e \u003cp\u003eOpen and distance learning (ODL) has expanded access to higher education, but also shifted greater responsibility for learning processes and outcomes onto students. Compared with traditional face-to-face settings, ODL offers limited immediate instructor guidance and peer interaction, so learners must rely more on self-regulation, autonomy, and motivation to maintain effective learning (Broadbent, 2017). This study focuses on a formal open and distance learning (ODL) program because that is where rich, longitudinal data are available. Conceptually, ODL can be seen as a structured subset of online learning: learners\u0026rsquo; study at a distance, rely heavily on digital platforms, and face similar challenges of sustaining attention, managing time and regulating their own engagement. Insights into how ODL students monitor and refocus their attention are therefore not only relevant for this specific program, but also informative for understanding attention regulation in online learning more generally. In this context, metacognitive regulation is especially critical because learners must plan, monitor, and evaluate their own learning trajectories to stay on track without continuous external supervision.\u003c/p\u003e \u003cp\u003eWithin the self-regulated learning tradition, Zimmerman (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) conceptualizes self-regulated learning as a cyclical process with three phases: forethought, performance, and self-reflection, each involving metacognitive, motivational, and behavioral processes such as goal setting, strategic planning, self-monitoring, and self-evaluation. This cycle supports deep and self-directed learning in online environments where course structures are comparatively loose, and learners frequently need to repair breakdowns in attention, motivation, or task comprehension. In this view, metacognitive regulation: planning, monitoring, and evaluating one\u0026rsquo;s own learning operates within and across these phases as a core mechanism of SRL (Schraw \u0026amp; Moshman, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Multidimensional Engagement and LMS-Based Indicators in ODL\u003c/h2\u003e \u003cp\u003eStudent engagement is widely described as a multidimensional construct that includes behavioral, emotional, and cognitive components (Fredricks, Blumenfeld, \u0026amp; Paris, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In ODL settings, engagement is often inferred from digital activity traces and interaction patterns rather than physical presence. The Community of Inquiry framework likewise argues that effective online learning arises from the interplay of social presence, cognitive presence, and teaching presence (Garrison, Anderson, \u0026amp; Archer, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Together, these models suggest that any operationalization of engagement in ODL should attend to what students do, how they think, and how they connect with others.\u003c/p\u003e \u003cp\u003eLearning analytics research has shown that learning management system (LMS) behavioral traces, such as logins, assessment attempts and completions, submissions, and access frequency, are among the strongest correlates and early predictors of achievement. Early mining of LMS logs demonstrated that patterns of online activity can reliably identify at-risk learners for timely support (Macfadyen \u0026amp; Dawson, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Subsequent studies confirmed that attempts, submissions, and access behaviors are significant indicators of course performance (You, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), and recent reviews have consolidated these findings across platforms and methods. Self-report data also shows convergence with digital traces. For example, Dixson (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found that scores on the Online Student Engagement (OSE) scale align closely with LMS activity, which supports the use of analytics-based profiling when affective or self-report data are unavailable.\u003c/p\u003e \u003cp\u003eBuilding on this literature, the present study adopts a three-axis classification of ODL engagement using LMS data only. Forum posts and replies are interpreted as social engagement because they index interaction and presence in the learning community. Logins and participation in formative assessments are treated as behavioral engagement, capturing the frequency and rhythm of students\u0026rsquo; online study actions. Performance on attempted formative assessments is used as an indicator of cognitive engagement quality, reflecting how effectively students process and apply course content. This operationalization is consistent with multidimensional engagement frameworks, is supported by existing learning analytics findings, and is feasible in small case study contexts where continuous self-report measures are not available.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Metacognitive Regulation: Planning, Monitoring, Evaluation, and Control\u003c/h2\u003e \u003cp\u003eWithin the broader self-regulated learning framework, metacognitive regulation focuses on the regulation of cognition. It comprises planning, monitoring, and evaluation, together with control actions that modify behavior in response to feedback. These processes help learners adapt to complex tasks, manage difficulties, and maintain alignment with goals, thereby promoting deeper learning, critical thinking, and problem solving (Sulisworo et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tiruneh et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMonitoring refers to the continuous evaluation of one\u0026rsquo;s cognitive processes and learning progress. It is key to maintaining awareness of understanding and performance, detecting discrepancies and errors, and deciding whether change is needed (Pesout \u0026amp; Nietfeld, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Evaluation involves a more deliberate review of learning outcomes and strategies, assessing their effectiveness and quality. Based on monitoring and evaluation, learners engage in regulation or control.\u003c/p\u003e \u003cp\u003eExperimental work on metacognitive monitoring tasks, such as confidence judgements, predictions of future performance, and comprehension self-assessments, and on metacognitive control tasks, such as strategy selection, goal setting, and planning, provides tools for assessing how accurately learners can track their own performance and how effectively they respond to feedback (Craig et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Discrepancies between expected and actual performance reveal the calibration of monitoring, while control tasks show how learners adapt their cognitive processes when demands change. In this study, these ideas inform the analytic distinction between noticing attentional drift (monitoring) and acting to refocus attention (control) in authentic ODL settings.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Mind Wandering, Focus Back Effort, Motivation, and Interest\u003c/h2\u003e \u003cp\u003eA central challenge in sustained learning, particularly in ODL, where external structure is weaker, is the regulation of mind wandering. Recent work introduces the construct of focus back effort, defined as the effort to redirect attention to the current task when an individual is mind-wandering (He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Focus back effort is usually assessed by intermittently probing participants during a task and asking to what extent they are trying to re-engage with the activity. Empirical studies show a positive correlation between focus back effort and functional connectivity within nodes of the executive network (He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zanesco et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), consistent with theoretical models that link the subjective experience of effort to the recruitment of executive control (Kurzban et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Laboratory research further reports a negative association between focus back effort and the frequency of mind wandering. Learners who invest more effort in refocusing are less likely to drift away from the task (He et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMotivation is a second key determinant of attentional drift. A substantial body of work shows that higher task-oriented motivation is associated with fewer mind wandering episodes (Robison \u0026amp; Unsworth, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Frank et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported a negative relationship between self-reported mind wandering and motivation to succeed in a reading comprehension task. Experimental manipulations that increase motivation, such as offering the possibility to end the experiment early if performance is good, have been shown to reduce mind wandering (Seli et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). A multifaceted approach that integrates cognitive, dispositional, and contextual predictors found robust associations between individual differences in mind wandering and motivation across tasks (Robison et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMotivational factors are also closely tied to interest. Individuals who find a task more interesting are generally more motivated to perform it (Hidi \u0026amp; Harackiewicz, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Numerous studies have documented a negative relationship between task interest and mind wandering (Kahmann et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Robison \u0026amp; Unsworth, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In educational contexts, individual differences in interest are associated with variations in mind wandering. Unsworth et al. (2013) further showed that motivation mediates the relationship between interest and mind wandering in reading comprehension tasks, treating both constructs as domain specific. Taking together, this literature suggests that focus, effort, motivation, and interest jointly shape how learners experience and regulate attentional drift, making them highly relevant for understanding metacognitive regulation in ODL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Conceptual Framing: Attention Refocusing as Metacognitive Control in ODL\u003c/h2\u003e \u003cp\u003eTaken together, these strands of literature portray ODL as a context in which high self-regulation demands, multidimensional engagement, and fragile attentional states converge. Learners must navigate a digital environment where engagement is expressed through behavioral traces, cognitive effort, and social interaction, while simultaneously managing mind wandering, fluctuating arousal, multiple life roles, and changing motivation. In this landscape, metacognitive regulation, particularly the monitoring of attentional drift and the effective use of refocusing strategies, emerges as a central mechanism that links engagement with learning outcomes.\u003c/p\u003e \u003cp\u003eThe present study builds on this work by conceptualizing attention refocusing as a core form of metacognitive control in ODL. It examines how learners become aware of internal and external triggers of attentional drift, how they enact behavioral and cognitive strategies to refocus on learning tasks, and how they draw on motivational and attributional processes to sustain engagement over time. This perspective is summarized in the proposed cyclical model of attention regulation in ODL (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which guides the subsequent analysis of refocusing episodes and their variation across learners with different engagement profiles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhile prior SRL and metacognition research has documented the importance of monitoring and regulation in online learning, less is known about the fine-grained mechanisms by which learners refocus attention after drift, and how these mechanisms vary between high- and low-engagement learners in authentic ODL settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Research Design\u003c/h2\u003e \u003cp\u003eThis study adopts an exploratory, embedded single-case study design to examine how ODL learners metacognitively regulate their attention and how these processes differ across engagement levels (Baxter \u0026amp; Jack, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yin, 2014). The bounded case is a formal open and distance learning program at a public university in Malaysia, within which nine focal students constitute embedded units of analysis, allowing both in-depth within-learner process tracing and cross-learner comparison across high-, moderate-, and low-engagement profiles (Yin, 2014). Rather than testing a predetermined model, the design is oriented toward unpacking the mechanisms through which learners in this specific context monitor attentional drift, refocus on course tasks, and sustain (or fail to sustain) behavioral, cognitive, and social engagement. An exploratory case study is appropriate because micro-level processes of attention refocusing in authentic ODL settings-and their variation across differently engaged learners\u0026mdash;have been only sparsely theorized and empirically documented, particularly in education (Rashid et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Yazan, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). By combining multiple qualitative and analytic sources (reflection reports, focus group interviews, and LMS traces) within a single, data-rich case, the design aligns with established case study principles of investigating complex phenomena in context using multiple data sources to enhance analytic depth and credibility (Baxter \u0026amp; Jack, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Yin, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, the bounded case is a 4 or 5 semester ODL course at a public university in Malaysia. Data collection was organized to capture both individual experience and behavioral evidence of engagement and metacognitive regulation. Near the end of the course, a three-hour focus group interview was conducted with the nine focal students. In addition, LMS trace data (logins, formative assessment completion and scores, and forum participation) were extracted for the nine focal students to construct behavioral engagement profiles. Data analysis proceeded in two linked phases. First, LMS indicators were synthesized into composite engagement scores to derive the three-tier engagement typology that underpins the comparative logic of the study. Engagement tiers were derived using a weighted composite index and a rule-based classification procedure. Second, focus group transcripts and reflection reports were analyzed using qualitative content analysis, with coding focused on episodes in which learners noticed attentional drift and described strategies for refocusing attention. Codes and themes were then compared within and across the three engagement tiers and interpreted alongside LMS profiles to identify convergent and divergent patterns in attention-refocusing mechanisms.\u003c/p\u003e \u003cp\u003eBy allowing students to articulate their experiences freely in reflection reports, the study sought to identify authentic episodes of cognitive monitoring, strategic control, and evaluative judgment. These responses were then interpreted through reflexive thematic analysis, guided by established theoretical frameworks on metacognitive regulation (e.g., Zimmerman, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Winne \u0026amp; Hadwin, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). This approach aligns with prior research emphasizing that metacognitive processes can manifest implicitly through learners\u0026rsquo; natural language and behavior, even in the absence of explicit metacognitive knowledge (Efklides, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Thus, the analysis focused not on learners\u0026rsquo; use of technical terms but on the underlying regulatory functions embedded within their narratives.\u003c/p\u003e \u003cp\u003eThis study adopted purposive recruitment within the open and distance learning (ODL) context, combined with voluntary self-selection. Invitation emails were sent to all students enrolled in the relevant ODL courses, and those who were willing to participate responded to the invitation. Nine students ultimately agreed to participate and formed the focal sample for in-depth qualitative analysis. This sampling strategy was appropriate because the study did not aim to achieve statistical representativeness of the entire cohort; rather, it sought to generate an in-depth understanding of metacognitive regulation of learning engagement among participants who were directly situated in the ODL context and able to provide rich accounts of their learning experiences (Creswell, 2014; Yin, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Accordingly, the sample is best understood as a volunteer qualitative sample rather than a statistically representative subset of the broader cohort. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the demographic and educational background of the nine focal participants. All are in-service educators enrolled in a bachelor\u0026rsquo;s degree ODL programme at a public university in Malaysia and are in the later stages of their studies (Semester 4 or 5), having completed 12 ODL courses each.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic Information and Educational Background of Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYear of Study\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBackground\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCourses attended number\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWork at Asia Pacific University\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePrimary school teacher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTeacher for 3 months\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEnglish teacher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried and have 3 kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCollege chemistry lecturer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTeacher for 12 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTeacher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried and have 2 kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15 years in a primary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarried and have 4 kids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBachelor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSemester 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA lecturer at UIA in the Department of Anesthesiology and Intensive Care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe group consists of six females and three males, with a mix of unmarried and married students; several of the married participants have between two and four children. Their professional roles range from primary and secondary school teachers to college lecturers and university lecturers in a medical faculty. Taken together, these characteristics indicate that the focal learners are mature adult students who combine demanding professional duties and, for many, family responsibilities with part-time degree study. At the same time, their substantial prior experience with ODL courses suggests that they are familiar with the LMS and typical course structures. This combination of heavy role demands and accumulated ODL experience is analytically important for the present study: it provides a context in which attentional drift is likely to occur due to time pressure and competing responsibilities, while also allowing us to examine how relatively experienced ODL learners metacognitively regulate and refocus their attention to sustain engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Defining Engagement Levels\u003c/h2\u003e \u003cp\u003eTo examine how metacognitive regulation operates across different learner profiles, it was first necessary to establish a transparent and analytically tractable categorization of engagement levels. In this study, engagement is treated as a multidimensional construct rather than a single behavioral indicator, and learners are classified into high, moderate, and low engagement groups to support subsequent within-case comparisons. Building on established dimensions of behavioral, cognitive, and social engagement, we developed a composite rating scheme that integrates multiple data sources into a single, interpretable typology. Engagement tiers were derived using a simple weighted composite index and rule-based classification.\u003c/p\u003e \u003cp\u003eAdditionally, a composite rating method was employed. This method involved clearly defined indicators across three core dimensions of engagement: behavioral, cognitive, and social. Each learner was evaluated within each dimension and assigned a score from 1 to 3, where 3 represented high engagement, 2 moderate engagement, and 1 low engagement. A composite score ranging from 3 to 9 was then calculated by summing the individual scores across dimensions. Learners were grouped as follows: total scores of 8\u0026ndash;9 indicated high engagement, 5\u0026ndash;7 denoted moderate engagement, and 3\u0026ndash;4 indicated low engagement.\u003c/p\u003e \u003cp\u003eThe engagement classification framework employed a theory-informed weighting scheme of 0.50, 0.30, and 0.20 for behavioral, cognitive, and social engagement, respectively. Behavioral engagement received the highest weight because LMS login activity provided the most continuous and stable trace of routine participation in the present ODL context, consistent with learning analytics research showing that engagement is most frequently operationalized through observable behavioral traces (Bergdahl et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Accordingly, behavioral engagement received the highest weight because LMS login activity provided the most continuous and stable record of routine participation in this study, while cognitive engagement was represented by formative assessment performance because formative assessment is used to monitor ongoing learning progress and academic development during the learning process (Lu \u0026amp; Cutumisu, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Social engagement was assigned a lower weight because forum participation captures an important but more context-dependent form of interaction that may vary with course design and participation requirements rather than learners\u0026rsquo; engagement alone. Consistent with multi-component engagement frameworks that do not prescribe canonical weights, our choice prioritizes observability, reliability, and theoretical relevance. The final classification framework is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e below:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDimensions, Indicators, Data Sources, and Weights in the Learner Engagement Classification Framework\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency of LMS logins, number of forum contributions, and formative assessment scores\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLMS Logs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage score on different course assessments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormative Assessment Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExpression of effort, emotional investment, and reflection on difficulties\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParticipation in Forum (LMS Data)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eScoring Criteria for Engagement Classification (1\u0026ndash;3 Points)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEach learner was evaluated across three engagement dimensions: behavioral, cognitive, and social. For each dimension, students were assigned a score on a 3-point scale (High\u0026thinsp;=\u0026thinsp;3, Moderate\u0026thinsp;=\u0026thinsp;2, Low\u0026thinsp;=\u0026thinsp;1) based on predetermined criteria. These criteria were designed to ensure transparency, consistency, and replicability in the classification process. The scoring rubric is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003eThis study tiers learner engagement using behavioral, cognitive, and social dimensions and does not include affective engagement in the composite score. The decision is methodological rather than theoretical. While affect is central to engagement theory (e.g., enjoyment, boredom, anxiety) (Pekrun, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), our dataset lacks objective, consistently sampled, and auditable affect indicators with clear scoring cut-points. In technology-mediated courses, log data and interaction traces readily support behavioral and social metrics, and structured protocol coding supports cognitive strategy use (Henrie, Halverson, \u0026amp; Graham, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Broadbent \u0026amp; Poon, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScoring Criteria for Behavioral and Social Engagement Based on LMS Data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCriteria Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1. Behavioral Engagement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLMS Data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLogin Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEqual to or above 80% of the group average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWithin \u0026plusmn;\u0026thinsp;20% of the average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClearly below the average\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2. Cognitive Engagement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLMS Data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormative Assessment Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80% (high performance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;79% (moderate performance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60% (low performance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3. Social Engagement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLMS Data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipation in Forum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;80% (high performance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60\u0026ndash;79% (moderate performance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;60% (low performance)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis scoring framework allowed for a nuanced and evidence-based classification of students into high, moderate, or low engagement categories. It also formed the basis for cross-group comparisons in subsequent analyses of metacognitive regulation processes. To assign behavioral engagement scores from log-in frequency, we adopted a three-tier, percentage-based classification rule commonly used in small-sample learning analytics. Specifically, scores were defined as: (1) High\u0026thinsp;=\u0026thinsp;values\u0026thinsp;\u0026ge;\u0026thinsp;120% of the group mean, (2) Moderate\u0026thinsp;=\u0026thinsp;values falling within \u0026plusmn;\u0026thinsp;20% of the group mean (80%-120%), and (3) Low\u0026thinsp;=\u0026thinsp;values\u0026thinsp;\u0026le;\u0026thinsp;80% of the group mean. Percentage-anchored thresholds allow indicators that differ in absolute scale (e.g., raw log-in counts) to be standardised relative to cohort performance, improving cross-participant comparability (Siemens \u0026amp; Baker, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; You, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This strategy is aligned with distribution-based norming frequently applied in learning analytics and educational measurement, where proportional rules yield interpretable and replicable classifications in small samples without requiring distributional assumptions (Tempelaar et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cerezo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The \u0026plusmn;\u0026thinsp;20% band has been used as a conservative tolerance zone in engagement modelling, reducing the risk of misclassifying marginal fluctuations as meaningful differences (Henrie, Bodily, \u0026amp; Graham, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHere, it classifies the composite engagement score into three tiers using fixed, reproducible cut scores: High\u0026thinsp;\u0026ge;\u0026thinsp;2.50, Moderate\u0026thinsp;=\u0026thinsp;1.75\u0026ndash;2.49, and Low\u0026thinsp;\u0026lt;\u0026thinsp;1.75. This rule partitions the 1\u0026ndash;3 response metric into equal-width intervals, a transparent practice for Likert-type scales when the goal is descriptive comparison rather than latent class discovery (DeVellis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Boone et al., 2014). Conceptually, it is also consistent with distribution-based standard-setting that anchors categories to constant fractions of the scale (or, equivalently, to bands around the mean on the order of ~\u0026frac12; SD), which yields interpretable and replicable thresholds in small-N studies where model-based cut scores would be unstable (Cizek \u0026amp; Bunch, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Cohen, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). We pre-specify the boundaries to facilitate a priori mapping between tiers and our subsequent analysis of refocusing tactics, and we verify robustness by sensitivity checks (e.g., shifting the bounds to 2.40 and 1.80) to ensure findings do not hinge on any single reasonable set of thresholds (Kane, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; DeVellis, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides a quantitative overview of learners\u0026rsquo; behavioral engagement, LMS trace data and compiled for the nine focal students across 12 ODL courses. Three indicators were extracted and combined according to the 0.50/0.30/0.20 weighting scheme described above: total login frequency, average formative assessment score, and number of forum contributions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLMS-Based Engagement Scores Averaged Across 12 Courses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudent ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLogin Frequency (50%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormative Assessment Score (30%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParticipation in Forum (20%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e482.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e258.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e188.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e678.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e351.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e213.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e572.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e303.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e357.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e739.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e402.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e497.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e278.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e771.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e435.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eP7, P8 and P9 form a high-engagement group, characterized by frequent access, consistently strong formative scores and substantial forum contributions; notably, P7 and P9 display the most intensive and visible participation, while P8 appears to use the platform more efficiently than constantly. P1, P3 and P5 occupy a moderate band, maintaining participation levels sufficient for course progression but with less sustained or socially visible activity, as illustrated by P3\u0026rsquo;s relatively frequent logins coupled with modest forum use and the generally balanced yet unexceptional profiles of P1 and P5. In contrast, P2, P4 and P6 record sparse logins, very low formative scores and minimal forum postings, yielding the lowest total scores and indicating both weak behavioral presence and limited academic performance rather than \u0026ldquo;silent but high achieving\u0026rdquo; engagement.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a heat matrix illustrating learner engagement across the Behavioural, cognitive, and social dimensions, as well as the overall engagement score. The nine learners were distributed evenly across the three engagement tiers. Three students (P7, P8, and P9) were classified as highly engaged, three (P1, P3, and P5) as moderately engaged, and three (P2, P4, and P6) as low-engagement learners. A clear stratification emerges across participants. P7, P8, and P9 demonstrate consistently high engagement in all three dimensions, reflected by uniformly high intensity in the matrix (scores\u0026thinsp;\u0026ge;\u0026thinsp;3) and total scores\u0026thinsp;\u0026ge;\u0026thinsp;2.5. These students maintained frequent interaction with the LMS, achieved high formative assessment performance, and actively participated in forum-based social activities. In contrast, P2, P4, and P6 occupy the lowest band of engagement, with scores clustered at 1 across all dimensions and a total score of 1.0. These participants exhibited limited LMS activity, low formative assessment performance, and minimal social contribution, which suggests weaker self-regulated participation in the ODL environment. The remaining students, P1, P3, and P5, fall within the moderate engagement range, reflected by mixed colours in the heatmap. Their Behavioural and cognitive engagement tends to be stronger than their social participation. The visual gradient suggests that these learners maintained adequate effort and academic processing but did not consistently participate in peer interaction.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Metacognitive Regulation in ODL","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Thematic Analysis of Refocusing Strategies within M-C-O\u003c/h2\u003e \u003cp\u003e During the analysis process, it became evident that attention refocusing emerged as the most salient form of metacognitive control described by participants. Reflection reports and interview transcripts revealed that students often recognized moments of attentional lapses and described various strategies, whether behavioral or cognitive, to redirect their focus toward learning goals. In contrast, instances of other control mechanisms, such as comprehensive strategic revision or emotional regulation, were relatively rare and less elaborate. Consequently, the analytic focus was refined to centre on attention refocusing, capturing both its external manifestations (e.g., changing study environments) and internal processes (e.g., mentally reasserting task priorities). This emphasis not only reflects the predominant patterns observed in the data but also provides a coherent lens through which the dynamics of metacognitive regulation could be meaningfully interpreted.\u003c/p\u003e \u003cp\u003eIn line with self-regulated learning models that distinguish monitoring, control and evaluation phases of learning (Zimmerman, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Panadero, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), this study conceptualizes each attention-refocusing episode as a Monitor-Control-Outcome (M-C-O) micro-cycle (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Learners first notice that their attention has drifted away from the focal task (Monitor), then implement a refocusing response (Control), and finally experience a proximal consequence (Outcome), such as re-entering the task, completing a micro-goal or experiencing reduced stress. The term Outcome is used deliberately to emphasize these short-term, episode-level effects, rather than a full evaluative phase or long-term achievement outcome.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCase A (High engagement; P9)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMonitor\u003c/b\u003e: P9 explicitly notes that time is the constraint, only short windows are available: \u0026ldquo;The aspect that is still a constraint is time, which cannot be changed or improved.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003cb\u003eControl\u003c/b\u003e: P9 describes shifting to a phone-first workflow whenever possible: \u0026ldquo;I try to be consistent and complete tasks little by little according to the time available. I prefer to chase time and use any opportunity to study and do tasks. Even a half-hour window while waiting for the rice to cook can be used productively.\u0026rdquo; Sub-theme: Fragmented Time Refocusing (TF).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOutcome\u003c/strong\u003e \u003cp\u003eInstead of waiting for the best opportunity, the task was completed step by step. \u0026ldquo;I try to be consistent and complete tasks little by little according to the time available. I prefer to chase time and use any opportunity to study and do tasks, rather than waiting for the right time.\u0026rdquo;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCase B (Low engagement; P4)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMonitor\u003c/b\u003e: The team recognized a hard production bottleneck: Powtoon\u0026rsquo;s free trial lasted only three days, and every slide required voice-over, making the planned workload infeasible within the limit. \u0026ldquo;Powtoon provides a free trial period of only 3 days\u0026hellip; because each video slide requires a lot of attention and needs to be voiced.\u0026rdquo;\u003c/p\u003e \u003cp\u003e \u003cb\u003eControl\u003c/b\u003e: They split the template and worked in parallel, and a teammate shared the password of a subscribed Powtoon account so everyone could add the voice-overs and remaining elements. \u0026ldquo;One of our friends had to share the template with the other 3 of us to work on separately\u0026hellip; Our friend had to share the password of the account he subscribed to with all of us so that we could work on voiceovers and other criteria.\u0026rdquo; Sub-theme: Micro-Tasking to Rebuild Momentum (MRM)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOutcome\u003c/strong\u003e \u003cp\u003eWith access and division of labor resolved, the group completed the video with voice-overs, refining character/voice choices that \u0026ldquo;gave a huge impact to the video.\u0026rdquo; \u0026ldquo;We were able to complete the video\u0026hellip;\u0026rdquo;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eCase C (High engagement, P7)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMonitor\u003c/strong\u003e \u003cp\u003eThe learner recognizes that completing the assignment requires mastering multiple new skills and could become overwhelming.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eControl\u003c/b\u003e: Drawing on the personal maxim \u0026ldquo;want a thousand forces, not a thousand excuses,\u0026rdquo; he adopts a solution-focused stance: he actively searches for additional information on the internet and deliberately uses any free time to revisit the LMS so that the provided materials are fully utilised. Sub-theme: Self-efficacy Rebuilding (SR)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOutcome\u003c/strong\u003e \u003cp\u003eThis motivational and Behavioral stance allows him to keep making progress on the tasks and to maintain confidence in his ability to complete the assignments without experiencing excessive stress.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThen this study shows that proactive control is a complementary pattern in highly engaged learners. While the cases above illustrate reactive refocusing, learners first notice that their attention has drifted and then deploy a control move. The narratives of several highly engaged participants revealed a complementary pattern. Rather than waiting to detect distraction, these learners described anticipating likely sources of drift and pre-structuring their time and environments to minimize its occurrence. P5 is better characterized as \u0026ldquo;plan-control-outcome\u0026rdquo; proactive management, rather than an on-the-spot narrative of \u0026ldquo;noticing attention drift and immediately correcting it.\u0026rdquo; This does not diminish its analytical value; in fact, it nicely complements framework by illustrating how contextual and preparatory strategies can lower the threshold for later refocusing.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCase D (High engagement, P7)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePlanning\u003c/strong\u003e \u003cp\u003eHe anticipates that his attention will be pulled away from class by housework and childcare.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eControl\u003c/b\u003e: He proactively negotiates with his family, informing them in advance and asking his spouse to take over household responsibilities during class time. Sub-theme: Leveraging Schedule Gaps and Family Coordination (LSG)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOutcome\u003c/strong\u003e \u003cp\u003eThis secures relatively uninterrupted periods of focus for online learning, effectively protecting a block of time in which sustained attention becomes more feasible.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eThese accounts can be understood as instances of proactive control: learners use their awareness of typical distraction triggers to design schedules, environments, and routines that lower the need for frequent reactive refocusing. Importantly, this pattern does not contradict the M-C-O lens but situates its boundary conditions. For high-engagement learners, attention regulation operates on at least two temporal layers: (a) moment-to-moment corrective moves after drift, as illustrated in the M-C-O episodes above, and (b) longer-horizon, preventive arrangements that make such drift less likely or easier to repair. The present data, therefore, suggest that refocusing on ODL is shaped not only by how learners respond after attention has shifted, but also by how they pre-emptively configure their study contexts to support sustained engagement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Mechanisms of Refocusing Attention\u003c/h2\u003e \u003cp\u003eThis section examines how learners metacognitively regain task focus once they become aware of attentional drift in ODL settings. Drawing on framework-based qualitative content analysis of reflection reports and focus group transcripts, we identified seven mechanism families through which students refocused their attention: goal-based refocusing, strategy-based refocusing, affective repair, environmental anchoring, internal conflict reconciliation, temporal anchoring, and motivational repair. In the subsections that follow, we first present the overarching codebook of mechanisms and sub-themes (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) and then compare how these mechanisms are differentially orchestrated across high-, moderate-, and low-engagement learners using cross-case matrices (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and within-group distributions (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e represents a cross-case comparative matrix of participants\u0026rsquo; attention refocusing strategies following attentional drift, categorized into seven overarching mechanisms. Each theme is further broken down into specific sub-themes grounded in learner narratives and coded using abbreviations. Due to length constraints, a more detailed explanation of the Codebook section is provided in Appendix Table A1.\u003c/p\u003e \u003cp\u003eThis study identified seven distinct mechanisms by which students refocused their attention after experiencing attentional drift in open and distance learning (ODL) environments: goal-based refocusing, strategy-based refocusing, affective repair, environmental anchoring, internal conflict reconciliation, temporal anchoring, and motivational repair. Each mechanism comprised multiple sub-themes and was enacted through cognitive, behavioral, emotional, and contextual strategies, reflecting the multifaceted nature of metacognitive regulation in digitally mediated, self-directed learning settings.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanisms and Sub-Themes of Attention Refocusing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eColumn/category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDMG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBR (Goal-based refocusing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDaily micro-goal structuring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBreaking larger tasks into small daily goals to regain a sense of progress.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVisualizing academic and professional advancement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImagining future academic or career outcomes to restore purpose.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBR (Strategy-based refocusing)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTime fragmentation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSplitting study time into short, manageable slots.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTask-switching to restore cognitive engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSwitching to another sub-task to refresh attention.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSBR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMicro-tasking to rebuild momentum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStarting with very small, easy tasks to restart work workflow.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR (Affective repair)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotionally-driven recommitment after language fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsing emotional reasons (e.g., personal goals, values) to recommit after feeling tired.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eST\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-talk for task re-engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsing inner speech to encourage oneself back to the task.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-efficacy rebuilding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReminding oneself of past successes to rebuild confidence.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eERL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmotional reset and low-stakes re-entry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTaking a brief reset and re-entering with low-pressure tasks.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFear of regression as a forward-motion catalyst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsing fear of falling behind as motivation to move forward.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEA (Environmental anchoring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLeveraging schedule gaps and family coordination\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsing schedule gaps and negotiating with family to secure learning time.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExternal support as an attention anchor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGetting support from others (e.g., reminders, check-ins) to anchor attention.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCreating a dedicated and less distracting space\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSetting up a specific, low-distraction place for online learning.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICR (Internal conflict reconciliation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRecognising and accepting planning failure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAcknowledging when original plans failed instead of denying it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAwareness of procrastination vs. work demands\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNoticing the internal conflict between procrastination and actual workload.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTA (Temporal anchoring)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStructured time rituals to reset focus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUsing fixed routines (e.g., start/stop rituals) to bring attention back.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlexible planning and realignment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAdjusting timelines and plans to fit current reality.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR (Motivational repair)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMindful pause and value reconnection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePausing to reconnect with personal values behind the task.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-awareness of internal resistance and reigniting enthusiasm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNoticing inner resistance and deliberately reigniting interest.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIDR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIdentity-driven re-engagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReturning to the task by linking it to one\u0026rsquo;s desired identity (e.g., future professional, responsible student).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes 20 sub-themes of attention refocusing, organized into seven mechanism families. Goal-based refocusing (GBR) captures how learners restore focus by breaking work into small daily goals and visualizing future academic or professional outcomes. Strategy-based refocusing (SBR) targets the task structure itself, for example by fragmenting time, switching to a different sub-task, or restarting with very small tasks to rebuild momentum. Affective repair (AR) involves using emotions to re-engage, through self-talk, rebuilding self-efficacy, brief low-stakes re-entry, or even fear of falling behind as a forward-motion trigger. Environmental anchoring (EA) centers on redesigning the study context, such as leveraging schedule gaps, negotiating family support, and creating dedicated low-distraction spaces. Internal conflict reconciliation (ICR) reflects moments when learners acknowledge planning failure or recognize the tension between procrastination and workload, which can open the door to more realistic regulation. Temporal anchoring (TA) refers to structured time rituals and flexible replanning that help reset attention and align tasks with current constraints. Finally, motivational repair (MR) reconnects learners with values and identity through mindful pauses, recognizing inner resistance, and identity-based reminders of who they want to become.\u003c/p\u003e \u003cp\u003eTwo features are particularly distinctive. First, environmental anchoring and temporal anchoring highlight that attention refocusing in ODL is not only cognitive but deeply embedded in time, family and physical context. Second, motivational and internal-conflict mechanisms show that learners often refocus by renegotiating the meaning of study, accepting imperfections, revisiting values, and linking tasks to a desired future identity, rather than simply \u0026ldquo;trying harder.\u0026rdquo; Additionally, the mechanisms of attention refocusing among participants at different levels are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMechanisms of Refocusing Attention Across Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGBR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSBR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eICR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1 (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMG, VAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCDL, ESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2 (Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMPV\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3 (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMG, VAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4 (Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMG, VAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5 (Moderate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLSG, ESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6 (Low)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMG, VAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLSG, ESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7 (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTF, TSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eST, SR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLSG, ESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAPW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTR, FPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMPV, SIR, IDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8 (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMPV, SIR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP9 (High)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDMG, VAP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTF, MRM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEDR, SR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLSG, ESA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSTR, FPR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIDR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: GBR: Goal-based refocusing; SBR: Strategy-based refocusing; AR: Affective repair; EA: Environmental anchoring; ICR: Internal conflict reconciliation; TA: Temporal anchoring; MR: Motivational repair\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that learners classified as high engagement activate a broad and integrated repertoire of mechanisms and frequently chain them within a single episode of refocusing. A typical sequence begins with Goal-Based Refocusing, daily micro-goal structuring and/or visualization of distal academic-professional milestones (DMG/VAP)-immediately followed by Strategy-Based Refocusing (e.g., task-switching or micro-tasking to lower cognitive load: TF/MRM/TSR). This is then stabilized through Temporal Anchoring (structured rituals and flexible replanning: STR/FPR) and Environmental Anchoring (leveraging schedule gaps and external supports: LSG/ESA). High-tier participants also invoke Motivational Repair (identity/values prompts; IDR/MPV/SIR) and, when needed, Affective Repair (self-talk and efficacy rebuilding; EDR/SR). High engagement is characterized by proactive orchestration: learners couple distal goal signals with near-term micro-tactics and contextual/time scaffolds, producing resilient re-entry into task focus.\u003c/p\u003e \u003cp\u003eModerate engagement learners deploy a stable but thinner stack of strategies. Their core configuration combines Goal-Based Refocusing (DMG/VAP) with Environmental Anchoring (ESA/CDL) and Temporal Anchoring (most often STR). Affective or Motivational devices appear episodically (e.g., SR, IDR), and Internal-Conflict Reconciliation (APW) are used to surface procrastination-work trade-offs, though it is less frequently translated into concrete replans than in the high tier. Moderates establish structure and context reliably but chain fewer mechanisms and show less micro-tactic variety than high-engagement peers. The low tier relies on piecemeal, context-driven fixes rather than coordinated sequences; the coupling between long-range aims and near-term tactics is weak.\u003c/p\u003e \u003cp\u003eLow engagement profiles exhibit reactive and narrow deployment of mechanisms. Learners tend to use single Strategy-Based pivots (TSR/MRM) or Temporal resets (STR) in isolation, often after a derailment, with Environmental supports (ESA/LSG) compensating for limited self-structuring. Goal signals (DMG/VAP) are intermittent and typically lack explicit next-action decomposition. Motivational/affective tools are lighter-touch and rarely integrated with planning.\u003c/p\u003e \u003cp\u003eTo further clarify how these mechanisms cluster within different levels of engagement, we examined the relative share of each strategy family within the low-, moderate-, and high-engagement groups. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displays the within-group distribution of goal-based, strategy-based, affective, environmental, internal, temporal, and motivational refocusing strategies. The figure shows that, although environmental anchoring is used across all three groups, high-engagement learners draw on a broader portfolio that combines strategy-based and motivational repair, whereas moderate-engagement learners are more context- and time-centric and low-engagement learners rely mainly on goal- and context-based cues.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross engagement tiers in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, several contrasts emerge. High-engagement learners display the clearest distinguishing feature: a pronounced reliance on motivational repair (about one-fifth of their coded strategies), complemented by substantive use of affective regulation, environmental adjustments, and temporal anchoring. Their profile suggests a broader, more self-energizing repertoire that rebuilds meaning and emotion while orchestrating concrete re-entry routines. In contrast, the moderate group concentrates on environmental anchoring and temporal structuring but shows no evidence of strategy-based refocusing. Their re-engagement appears to come from altering context and schedule rather than changing the way tasks are approached. Low-engagement learners, meanwhile, lean on goal-based cues and environmental tweaks, with comparatively limited use of temporal rituals and motivational repair, indicating attempts to restart through proximal goals and workspace adjustments rather than through time scaffolds or value reconnection.\u003c/p\u003e \u003cp\u003eDespite these differences, a common baseline is visible: environmental anchoring is used at meaningful rates in all groups, marking \u0026ldquo;context engineering\u0026rdquo; as a shared mechanism of refocusing in ODL settings. What varies is portfolio breadth. The high-engagement group distributes its use more evenly across families, including strategy-based and motivational repair-whereas the moderate group is narrowly context/time-centric, and the low group is primarily goal/context-centric. This gradient is consistent with the view that a more diversified repertoire of refocusing tactics co-varied with, and may help sustain, higher engagement.\u003c/p\u003e \u003cp\u003eFinally, apparent similarity in the bar chart partly reflects within-group normalization and small cell counts. Even so, qualitative contrasts remain robust: the absence of strategy-based tactics in the moderate group, elevated motivational repair among high-engagement learners, and under-use of temporal anchoring in the low-engagement group. These patterns align with the theory linking sustained engagement to both regulatory breadth and the capacity to restore meaning and affect when effort flags.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Classification of Refocusing Types: Cognitive and Behavioral Refocusing\u003c/h2\u003e \u003cp\u003eRefocusing strategies used by ODL students can be categorized into two overarching types: cognitive refocusing and behavioral refocusing. As summarized in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, cognitive refocusing refers to the internal restoration of learning goals through mental processes such as self-reflection, emotional regulation, and planning. In contrast, behavioral refocusing involves observable, task-based actions that help learners resume task engagement, such as switching to a simpler activity, physically changing environments, or resuming study after a short rest.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTypes of Attention Refocusing Strategies with Descriptions, Examples, and Proportional Usage\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eType\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBehavioral Refocusing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGet back on task through observable, concrete behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGet up and rest, come back and continue, start the question again, switch to a simple task\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.3%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCognitive Refocusing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMentally restore learning goals through internal reflection, goal review, and emotion regulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMentally plan the next step, encrypt learning goals, and remind yourself\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.7%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSub-themes categorized under Cognitive Refocusing included strategies such as visualizing academic or professional advancement (VAP), identity-driven re-engagement (IDR), self-talk for task re-engagement (ST), and self-efficacy rebuilding (SR). Participants also demonstrated reflective mechanisms such as recognizing and accepting planning failure (RAP), becoming aware of procrastination and work conflict (APW), and engaging in emotionally grounded resets (e.g., mindful pause and value reconnection, MPV). These processes illustrate the role of internal cognitive and affective control in regulating attention and re-aligning focus with personal goals.\u003c/p\u003e \u003cp\u003eIn contrast, strategies categorized under behavioral refocusing were more overt and action based. These included daily micro-goal structuring (DMG), task switching (TSR), micro-tasking to rebuild momentum (MRM), and emotional reset through low-stakes re-entry (ERL). Learners also described leveraging structured time rituals (STR), flexible planning and rescheduling (FPR), and redesigning study environments (CDL) to support renewed concentration. These strategies reflect intentional adaptations of physical and temporal contexts to support task continuity and reduce attentional interference.\u003c/p\u003e \u003cp\u003e \u003cb\u003eProportional Breakdown of Refocusing Types\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTotal distinct sub-themes identified across all participants\u0026thinsp;=\u0026thinsp;20\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePurely Cognitive Refocusing: 9 sub-themes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePurely Behavioral Refocusing: 10 sub-themes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBelong to Both (Overlap): 3 sub-themes\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSince overlapping themes are shared, we calculate their contribution as 1.5 each for both categories to avoid double-counting\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdjusted Cognitive Count\u0026thinsp;=\u0026thinsp;9\u0026thinsp;+\u0026thinsp;1.5 (ERL, STR, FPR)\u0026thinsp;=\u0026thinsp;10.5\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAdjusted Behavioral Count\u0026thinsp;=\u0026thinsp;10\u0026thinsp;+\u0026thinsp;1.5\u0026thinsp;=\u0026thinsp;11.5\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eFinal Proportions\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cb\u003eCognitive Refocusing: 10.5 / (10.5\u0026thinsp;+\u0026thinsp;11.5)\u0026thinsp;=\u0026thinsp;47.7%\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eBehavioral Refocusing: 11.5 / (10.5\u0026thinsp;+\u0026thinsp;11.5)\u0026thinsp;=\u0026thinsp;52.3%\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA frequency analysis across all identified sub-themes showed that out of 20 distinct strategies, 9 were purely cognitive, 10 were purely behavioral, and 3 exhibited hybrid features. When adjusted for overlap, the proportion of behavioral refocusing strategies accounted for approximately 52.3%, while cognitive refocusing strategies comprised 47.7% of the total. This near-equivalent distribution suggests that both forms of refocusing play critical roles in supporting sustained attention in ODL. However, the slight dominance of behavioral strategies indicates that learners may more frequently rely on tangible, task-oriented responses to drift, especially in asynchronous, high-autonomy learning environments where external scaffolds are minimal.\u003c/p\u003e \u003cp\u003eOverall, these findings underscore that attentional regulation in ODL is a multidimensional process requiring both action and reflection. While behavioral responses help restore focus immediately and pragmatically, cognitive strategies ensure that this refocusing remains meaningful, sustainable, and aligned with learners\u0026rsquo; broader academic identity and goals. The classification of these mechanisms provides a useful theoretical distinction for future research and offers practical implications for instructional design, such as balancing structural supports with tools that foster internal self-regulation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Triangulated Evidence for Engagement and Metacognitive Regulation\u003c/h2\u003e \u003cp\u003eTo examine differences in metacognitive regulation patterns, learners were categorized into high, moderate, and low engagement groups based on a triangulated analysis of three data sources: (1) LMS behavioral records (including login frequency, group forum participation, and formative assessment scores), (2) student reflection reports, and (3) focus group interview transcripts. This multi-source data approach enabled a more comprehensive and contextualized understanding of learner engagement. Taken together, these LMS-based profiles provide a robust quantitative basis for classifying learners into low, moderate, and high engagement tiers. These quantitative patterns are then triangulated with evidence from student reflection reports and focus group interviews to develop a more contextualized interpretation of learners\u0026rsquo; engagement and metacognitive regulation. Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents this triangulated evidence across the nine participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTriangulation of Engagement and Refocusing Mechanisms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParticipant (tier)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLMS evidence of engagement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQualitative evidence (very brief)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTriangulated interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP1-Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMid-range logins and scores; limited forum use.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses micro-goals and visualizing course completion to restart work.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBehavioral engagement is adequate; refocusing is mainly goal-based but not very frequent.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2-Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowest total score; few logins, weak formative results, minimal posts.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReports fatigue after work and often \u0026ldquo;cannot think straight\u0026rdquo;; relies on switching to easier tasks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLMS and narrative converge on fragile engagement with short, reactive refocusing episodes.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP3-Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh logins but modest assessment and forum activity.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDescribes setting daily goals but sometimes postponing tasks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAppears present in the LMS, but learning effort is uneven; refocusing is used, yet inconsistently.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP4-Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow-average logins and performance; low interaction.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStarts with small actions (one article, one forum post) when overwhelmed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQuantitative and qualitative data both indicate hesitant, low-intensity engagement\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP5-Moderate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMid-range on all three indicators.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePlans around work and family; uses environmental and temporal arrangements to secure study time.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEngagement is steady but not high; refocusing often takes a proactive, planning-oriented form.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP6-Low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSimilar to P2/P4: low logins, low scores, minimal forum use.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMentions struggling to keep up and needing to \u0026ldquo;push\u0026rdquo; herself to return to tasks.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData suggest persistent difficulties sustaining participation and only sporadic refocusing.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP7-High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high logins and strong assessment; active in forums.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmphasizes persistence (\u0026ldquo;no excuses\u0026rdquo;), self-talk and task-switching within the course.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLMS pattern and narratives align as a highly engaged learner who uses rich, strategy-based refocusing.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP8-High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh scores across indicators, especially formative assessment and forums.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses visualizing academic goals and brief \u0026ldquo;restart\u0026rdquo; steps to get back on task.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong cognitive and social engagement supported by robust goal-based and affective refocusing.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP9-High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighest overall engagement; most logins and forum posts.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExploits micro-windows to study, negotiates family support, and uses multiple refocusing strategies. and Motivational repair (IDR).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConsistent high engagement with diversified refocusing repertoire, matching her top LMS profile.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eLMS-based engagement patterns across the nine learners show a clear three-tier structure. Aggregating login frequency, formative assessment scores, and forum participation across twelve ODL courses, three students (P7, P8, P9) exhibit consistently high engagement, three (P2, P4, P6) show low engagement, and the remaining three (P1, P3, P5) fall in a moderate range. P9 has the highest composite score (435.2), combining the most frequent logins (771.6 on average) with the highest formative performance and forum participation (161 posts), indicating sustained behavioral, cognitive, and social involvement across courses. P7 and P8 also score well above the sample mean on all three indicators, particularly in their participation in discussion forums, suggesting that highly engaged learners not only access the LMS frequently but also contribute actively to interactive activities.\u003c/p\u003e \u003cp\u003eBy contrast, P2, P4, and P6 cluster at the lower end of the distribution. Their total scores (around 188\u0026ndash;213) reflect fewer logins, weaker formative assessment performance, and minimal forum activity. For example, P2 has one of the lowest formative assessment averages (6.5) and only eight forum posts across courses, which is consistent with a pattern of sporadic, low-intensity engagement. P4 and P6 show slightly higher scores on some dimensions, but their overall profiles remain clearly below the group average.\u003c/p\u003e \u003cp\u003eThe moderate group (P1, P3, P5) displays more nuanced patterns. P3, for instance, records relatively high login frequency (678.3) but low forum participation (4 posts), suggesting that frequent access to the LMS does not automatically translate into interaction or deeper engagement. P1 and P5 sit near the middle of the sample on all three indicators, with neither pronounced strengths nor severe weaknesses.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Discussion and conclusion","content":"\u003cp\u003eThis study sets out to explore how learners in an open and distance learning (ODL) environment experience challenges to learning engagement, how they use metacognitive regulation to refocus their attention after monitoring, and how these processes differ across students with high, moderate, and low levels of engagement. Drawing on reflection reports, focus group interviews, and learning management system (LMS) traces from a group of students, the research adopted an embedded single-case design and used a Monitor-Control-Outcome (M-C-O) heuristic to organize fine-grained episodes of attention regulation. The findings extend existing work on self-regulated learning, metacognition, and online engagement by linking learners\u0026rsquo; moment-to-moment regulatory episodes to empirically derived engagement tiers in a real-world ODL context.\u003c/p\u003e \u003cp\u003eThe first analytic step of this study asked how ODL learners describe the micro-processes through which they bring their attention back after drift, and how these episodes can be meaningfully represented within a Monitor-Control-Outcome (M-C-O) framework. Section \u003cspan refid=\"Sec12\" class=\"InternalRef\"\u003e4.1\u003c/span\u003e showed that learners\u0026rsquo; narratives consistently followed this structure: they first noticed that attention or progress was being disrupted (Monitor), then implemented a refocusing move (Control), and finally reported a proximal consequence (Outcome), such as being able to resume the task, complete a small portion of work, or feel less stressed. These episodes predominantly took the form of reactive refocusing (\u0026ldquo;I realized I was off task, so I\u0026hellip;\u0026rdquo;), but among highly engaged learners there was also evidence of proactive control, in which learners anticipated typical sources of distraction and pre-structured their time, environment, and social arrangements to make sustained attention more feasible (e.g., negotiating family support, planning to use short time gaps, or shifting to a phone-first workflow).\u003c/p\u003e \u003cp\u003eThe M-C-O schema captured these micro-cycles and showed that attention regulation in ODL is iterative, experimental, and often messy rather than linear and neatly staged. Alongside reactive refocusing after monitoring drift, the study also identified proactive or preventive cycles of metacognitive regulation. Several learners, particularly those in the high-engagement group, described ritualized planning routines, such as a daily \u0026ldquo;power hour\u0026rdquo; in which they reviewed their workload, clarified priorities, and converted goals into concrete next actions and time blocks. Other pre-negotiated household roles reserved protected study windows, or pre-structured task sequences before busy periods. These forward-looking routines did not depend on monitoring and regulation; instead, they anticipate potential distractions and prepare accordingly.\u003c/p\u003e \u003cp\u003eThe analysis of strategy-family shares addressed how the composition of attention-refocusing repertoires differs across low-, moderate- and high-engagement learners. Overall, all three groups showed meaningful use of environmental anchoring, indicating that \u0026ldquo;context engineering\u0026rdquo; (e.g., schedule gaps, study spaces, external supports) is a baseline mechanism of refocusing in ODL. What differentiated the groups was the breadth and balance of their portfolios. High-engagement learners drew on a diversified mix of families, with motivational repair accounting for about one-fifth of their coded strategies, alongside substantial affective, temporal and environmental refocusing. Moderate-engagement learners exhibited a narrower, context- and time-centric profile dominated by environmental anchoring and temporal structuring, with no observed strategy-based refocusing. Low-engagement learners relied mainly on goal-based cues and environmental tweaks, with comparatively little temporal anchoring or motivational repair.\u003c/p\u003e \u003cp\u003eFramework-based qualitative content analysis of reflection reports and focus-group data yielded seven mechanism families: goal-based, strategy-based, affective, environmental, internal conflict, temporal and motivational refocusing comprising 20 sub-themes. These patterns are broadly consistent with self-regulated learning (SRL) research showing that successful learners tend to combine multiple strategy families: cognitive, metacognitive and resource-management strategies, rather than relying on a single tactic. In large-scale studies and meta-analyses of online and continuing education, goal setting, strategic planning, time management and environment structuring all emerge as positive predictors of persistence and achievement. Our high-engagement learners exemplify this \u0026ldquo;multi-strategy profile\u0026rdquo;: they couple distal goal representations with concrete task-level maneuvers and with deliberate management of time blocks and study spaces. By contrast, the narrower and more reactive repertoires observed in the low-engagement group resemble SRL profiles characterized by limited strategy use and weaker outcomes in prior work.\u003c/p\u003e \u003cp\u003eThe results also diverge from some prior descriptions of \u0026ldquo;strategic\u0026rdquo; learners. Quantitative studies sometimes portray moderate achievers as characterised by strong time management and environment structuring (e.g., regular study schedules) with relatively less need for intensive motivational repair (Broadbent \u0026amp; Poon, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In our sample, however, moderate-engagement learners\u0026rsquo; context- and time-centric profiles were not accompanied by rich cognitive or motivational repertoires and were associated with only mid-level LMS engagement. One plausible explanation is the adult ODL context: for learners juggling work and family responsibilities, temporal and environmental structuring may be necessary just to maintain minimal participation, while sustained high engagement additionally requires strong motivational repair and strategy-based adaptation.\u003c/p\u003e \u003cp\u003eThe contrast between proactive and mixed strategies in the high-engagement group and isolated, reactive fixes in the low-engagement group speaks to the distinction between proactive and reactive control discussed in the mind-wandering and cognitive-control literature (Braver, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Mittelst\u0026auml;dt et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Experimental and neurocognitive studies indicate that sustained attention and goal maintenance rely heavily on proactive control-anticipating demands and maintaining goal representations whereas purely reactive adjustments after conflict are less effective for preventing mind wandering (He et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; He et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Our data echo this distinction in a real-world ODL context: high-engagement learners not only correct after drift but also pre-structure time and context so that drift is less likely or easier to repair, whereas low-engagement learners tend to \u0026ldquo;firefight\u0026rdquo; episodes as they arise. Differences in prior SRL experience, workload, and access to supportive environments may all contribute to these divergent patterns.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study should be acknowledged when interpreting the findings. First, the research adopts an embedded single-case design focused on one formal ODL program at a single institution, with nine focal learners and a wider set of reflection reports from peers. The small, context-specific sample and the institutional and cultural setting mean that the patterns identified here are. They are likely to resonate most strongly with adult, working learners in similar ODL programs and may not fully capture the experiences of younger, campus-based or non-working student populations. These sources provide rich reconstructions of attention-refocusing episodes but remain vulnerable to recall bias and selective reporting; they also foreground episodes that learners themselves perceived as salient.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics statement\u003c/h2\u003e\n\u003cp\u003eThe study complied with internationally recognized ethical principles (e.g., the Declaration of Helsinki) and relevant institutional and national guidelines. The research involved minimal risk: no clinical procedures were conducted, no minors or vulnerable groups were recruited, and no sensitive personal or health-related data were collected. All participants were adult students enrolled in an open and distance learning program. Recruitment was conducted via email, and students who wished to participate provided written informed consent by replying to the invitation email to indicate their voluntary agreement to take part. In the consent information, they were briefed on the study purpose, procedures, confidentiality safeguards, and their right to withdraw at any time without penalty. Data was anonymized at the point of analysis and is reported only in aggregated form. According to the applicable institutional regulations for minimal-risk educational research of this type, formal IRB/ethics committee approval was not required. This study was approved by the Universiti Kebangsaan Malaysia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research is conducted under GG-2021-030 Research Grant.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eXue Yanxing conducted the main work of the study, including the conception and design of the research, literature review, data collection, data analysis, interpretation of the results, and drafting of the manuscript. Xue Yanxing also prepared the tables and figures and organized the manuscript structure. Fariza Khalid supervised the research process, provided academic and methodological guidance, contributed to the refinement of the study design and interpretation of the findings, and critically revised the manuscript for important intellectual content. Both authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are not publicly available because they contain qualitative data from human participants and may compromise participant privacy and confidentiality. The data are, however, available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnthonysamy, L. The use of metacognitive strategies for undisrupted online learning: Preparing university students in the age of pandemic. \u003cem\u003eEduc. Inform. Technol.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (6), 6881\u0026ndash;6899 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBağrıacık Yılmaz, A. \u0026amp; Karataş, S. Why do open and distance education students drop out? Views from various stakeholders. \u003cem\u003eInt. J. 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Becoming a self-regulated learner: An overview. \u003cem\u003eTheory Into Pract.\u003c/em\u003e \u003cb\u003e41\u003c/b\u003e (2), 64\u0026ndash;70 (2002).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"attention refocusing, learning engagement, Open and distance learning, metacognitive","lastPublishedDoi":"10.21203/rs.3.rs-9269094/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9269094/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn open and distance learning (ODL), learners must sustain engagement with limited external structure, making attention drift a pervasive threat to effective study. This study investigates how ODL students monitor attentional drift and refocus on course tasks, and how attention-refocusing mechanisms vary across high-, moderate-, and low-engagement learner profiles. An exploratory embedded single-case study was conducted in an ODL program at a public university in Malaysia. Nine focal students were analyzed using three data sources: learning management system (LMS) traces from 12 courses, written reflection reports, and a three-hour focus group interview. Multiple mechanism families of attention refocusing were identified. High-engagement learners drew on a broad, coordinated repertoire, often combining several mechanisms and proactively shaping their study time and context. Moderate-engagement learners relied on a narrower, mainly context- and time-focused configuration, whereas low-engagement learners used reactive, fragmented tactics with weak links between long-term goals and immediate actions. Overall, behavioral and cognitive refocusing strategies were employed in broadly similar proportions. The findings foreground attention refocusing as a crucial mechanism through which metacognitive control supports sustained engagement in ODL settings. It shows that a broader, more consistently combined repertoire of refocusing tactics distinguishes highly engaged learners from their peers. The study suggests that ODL design and support services should explicitly scaffold monitoring of drift and provide concrete repertoires of refocusing tactics to help learners \u0026ldquo;get back on track\u0026rdquo; in digitally mediated, high-autonomy environments.\u003c/p\u003e","manuscriptTitle":"Who Gets Back on Track? Engagement-Level Differences in Metacognitive Attention Refocusing in Open and Distance Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:32:19","doi":"10.21203/rs.3.rs-9269094/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"67bd7fbb-2a64-40ec-b511-9451d17b0d70","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T08:28:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-30T23:12:15+00:00","index":32,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66566158,"name":"Social science/Education"},{"id":66566159,"name":"Biological sciences/Psychology"},{"id":66566160,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2026-05-07T08:43:23+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:32:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9269094","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9269094","identity":"rs-9269094","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}