Beyond Elaboration: How Metacognition, Co-regulation, and Social Presence Converge to Drive Engagement in Problem-Based Learning

Beyond Elaboration: How Metacognition, Co-regulation, and Social Presence Converge to Drive Engagement in Problem-Based Learning | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Beyond Elaboration: How Metacognition, Co-regulation, and Social Presence Converge to Drive Engagement in Problem-Based Learning Qing He, Yumeng Yue, Binbin Zheng, George Tipoe, Fraide A. Ganotice, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8425435/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 Problem‑based learning (PBL) is intended to foster collaborative knowledge construction, yet the moment‑to‑moment interplay of metacognition, co‑regulation, and socio‑emotional interaction in PBL tutorials remains underexplored. This mixed‑methods study examined discourse processes in four first‑year medical PBL groups discussing an obstructive sleep apnoea case. Ninety‑minute tutorials were video‑recorded, transcribed, segmented into meaning units, and coded for metacognitive activity, co‑regulation, social‑emotional interaction, and tutor moves. Quantitative analyses included code frequencies, lag sequential analysis of discourse transitions, and Epistemic Network Analysis to model co‑occurrence patterns. Across 649 coded segments (544 student, 105 tutor), elaboration was the dominant metacognitive activity, with orientation, evaluation, and planning also frequent. Co‑regulatory moves were mainly activating and confirming peers, while slowing and change‑oriented regulation were rare. Interactive social presence was the prevalent socio‑emotional form; negative socio‑emotional moves were not observed. The epistemic network revealed a densely connected core linking elaboration, planning, orientation, evaluation, activating, confirming, and interactive social presence, indicating that reasoning, co‑regulation, and social engagement were tightly integrated. Lag sequential analysis showed that elaborative and tutor‑initiated prompts often triggered extended metacognitive sequences, whereas tutor clarifications were uniquely associated with metacognitive moves that also expressed social support. These findings portray PBL as a socio‑cognitively integrated activity and highlight elaboration‑centred discourse, supportive co‑regulation, and facilitative tutor questioning as key levers for fostering productive, psychologically safe collaboration in medical education. Problem-based learning Medical Education Discourse Analysis Metacognition Co-Regulation Social Presence Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Problem-based learning (PBL) is a signature pedagogy in medical education and related health professions programs worldwide. By engaging students in small-group discussions centered on complex patient cases, PBL is intended to foster hypothesis generation, integrative reasoning, and the construction of biomedical and clinical knowledge (e.g., Hmelo-Silver & Barrows, 2008 ). Knowledge construction has been identified as the core process in PBL, shaping both individual and collaborative learning outcomes and contributing to clinical reasoning, diagnostic decision-making, and professional identity formation (Chin & Chia, 2004 ; Schmidt et al., 2011 ). Understanding how knowledge is constructed in situ, particularly in the moment-to-moment discourse of PBL tutorials, can therefore provide critical insights for designing and facilitating learning environments that better support deeper understanding, teamwork, and the development of reflective practitioners. A growing body of research has highlighted the central role of regulation in effective learning. Zimmerman ( 2002 ) defines self-regulation as the processes learners use to plan, monitor, control, and reflect on their cognition, motivation, and behavior in pursuit of goals, with metacognition referring specifically to learners’ monitoring and intentional guidance of their thinking. Metacognition is a key component of self-regulation and a predictor of learning outcomes across domains (Muijs & Bokhove, 2020 ). The PBL environment, with its emphasis on student agency and inquiry, has been shown to encourage students’ metacognitive self-regulation during learning (Sungur & Tekkaya, 2006 ). Beyond the individual, regulation as a social process that unfolds within interaction. Co-regulation refers to regulation that is prompted, supported, or guided by others (e.g., tutors or peers) during the learning process, while socially shared regulation refers to the joint regulation of shared goals, strategies, and monitoring by the group as a collective (Hadwin et al., 2011 ; Hadwin et al., 2017 ). These regulation modes are mutually supportive and interdependent: co-regulatory prompts and scaffolds can catalyze individual self-regulation, and jointly regulated group processes can stabilize and sustain productive collaboration (Hadwin et al., 2017 ; Panadero & Järvelä, 2015 ). Effective collaboration in PBL requires coordinated cognitive work and the regulation of socio-emotional dynamics. Students must integrate diverse perspectives, distribute and manage tasks, and navigate interpersonal interactions and shared uncertainty. Self-regulation equips learners to plan, monitor, and evaluate their own thinking; co-regulation extends these processes to the group by inviting prompts, feedback, and scaffolding of others’ thinking; and SSRL enables the group to align on goals, standards, and strategies (Hadwin et al., 2017 ; Järvelä & Hadwin, 2013 ). At the same time, socio-emotional interaction, such as fostering trust, encouragement, and psychological safety, and managing frustration or disagreement, supports sustained engagement and the willingness to take intellectual risks (Edmondson, 1999 ; Järvenoja & Järvelä, 2009 ; Volet et al., 2009 ). These socio-emotional processes can be productively framed using the Community of Inquiry (CoI) framework, which conceptualizes effective collaborative learning as the intersection of cognitive, social, and teaching presence (Garrison et al., 1999 ). In the CoI framework, interactive social presence supports cognitive presence by maintaining flow and reciprocity in discourse, which in turn sustains inquiry (Garrison et al., 1999 ; Richardson & Lowenthal, 2017; Rourke et al., 1999 ). From this perspective, the discourse of PBL tutorials is not merely a vehicle for information exchange but the primary medium through which participants co-construct meaning, maintain group cohesion, and orchestrate regulatory processes. Research in collaborative learning has begun to uncover the complex coupling between social-emotional and cognitive processes, showing that regulation of emotion and motivation is intertwined with the regulation of cognitive activity and knowledge construction (Bakhtiar et al., 2018 ; Isohätälä et al., 2017 ; Järvenoja & Järvelä, 2009 ; Volet et al., 2012). Because collaborative learning can involve multiple regulation modes that fluctuate over time, and because group members’ behaviors and perceptions can trigger diverse socio-emotional trajectories, understanding how these processes shift under different regulatory conditions, and how they vary across cultural settings, is essential for grasping the complexity of PBL (Frambach et al., 2012 ; Huang & Lajoie, 2023 ). Tutors play a pivotal role in shaping these intertwined cognitive and socio-emotional processes. In PBL, tutors are expected to facilitate and activate student learning, promote effective group functioning, monitor the quality of learning, and intervene when necessary without providing direct answers (De Grave et al, 1999; Hmelo-Silver & Barrows, 2008 ; Maudsley, 1999 ; Schmidt & Moust, 1995 ). Through strategic questioning, modeling, and scaffolding, tutors can help students articulate reasoning, evaluate evidence, and connect biomedical concepts to clinical contexts (Dolmans et al., 2002 ; Hmelo-Silver & Barrows, 2008 ). Their role extends beyond cognition to the cultivation of a psychologically safe environment in which students feel comfortable sharing difficulties, acknowledging knowledge gaps, and constructively challenging one another’s ideas (Edmondson, 1999 ; Hammar Chiriac et al., 2021 ). Effective tutor observation and guidance ensure balanced participation, productive group norms, and adaptive regulation of both task and socio-emotional demands, conditions that are essential for successful PBL sessions (Dolmans et al., 2002 ; Hammar Chiriac et al., 2021 ; Schmidt & Moust, 1995 ). Despite growing recognition of the importance of metacognition, co-regulation, socio-emotional interaction in collaborative learning, research in medical education has only sporadically examined how these processes co-occur and unfold in real PBL tutorials. Much of the existing work investigates these dimensions in isolation or uses self-report measures, which can obscure their interdependence and temporal dynamics (Dolmans et al., 2005 ; Panadero, 2017 ; Hadwin et al., 2017 ). A recent systematic review of process-oriented analysis in PBL discourse has called for future research to investigate teams’ co-regulation of knowledge construction at the episode level (Chen & Zheng, 2025 ). Fine-grained, video-based analyses of discourse moves, capturing how students and tutors actually prompt, scaffold, and monitor their own knowledge construction are still needed in medical education PBL, and cross-cultural differences in these interactional patterns are underexplored (Frambach et al., 2012 ; Schmidt et al., 2011 ). Addressing this gap, the present study provides a micro-analytic account of the co-occurrence and sequential unfolding of cognitive, metacognitive, regulatory, socio-emotional discourse moves between students and tutors during authentic PBL tutorials in an Asian medical school. By examining real-time interactional data, this study advances understanding of how knowledge construction is organized and regulated in practice and offers actionable implications for tutor training, PBL design, and culturally responsive facilitation. To that end, we address the following research questions: What is the relationship between metacognitive activities, co-regulation/SSRL, and socio-emotional interactions in PBL tutorials? How do students’ and tutors’ discourse moves unfold over time during PBL tutorials? Methods Research design This mixed-methods study investigated knowledge construction and discourse dynamics in PBL tutorials. Combining qualitative, video-based discourse analysis with quantitative coding of interactional sequences, the study examined how students and tutors co-regulated learning and managed socio-emotional interactions during collaborative case discussions. The design responds to calls for process-oriented analyses of PBL to inform evidence-based facilitation practices (Chen & Zheng, 2025 ; Dolmans et al., 2005 ; Hmelo-Silver & Barrows, 2008 ). The analytic focus on cognitive, metacognitive, regulatory, and socio-emotional discourse moves is conceptually aligned with the Community of Inquiry framework in emphasizing the interplay of cognitive and social processes in sustaining inquiry. Procedure The study was conducted in a medical school at a university in Hong Kong. Four PBL groups, each including one tutor and approximately 10 students, were invited to participate between January and March 2024. All students were in the first year of the Bachelor of Medicine and Bachelor of Surgery (MBBS) program. The four tutors were drawn from different departments within the university: three from biomedical sciences and one from clinical medicine. All tutors had completed institutional PBL tutor training and had prior experience facilitating PBL for medical students. Students had participated in a previous series of PBL sessions before the study; however, because group composition changes regularly in the curriculum, students within each study group may or may not have previously worked together. The focal PBL case addressed Obstructive Sleep Apnea (OSA), introduced at the beginning of the second semester, and was designed to deepen students’ understanding of the cardiopulmonary and respiratory systems. This case was selected because first-year students had already accumulated some PBL experience in the first semester, allowing the analysis to focus on groups that were familiar with the PBL format rather than still learning the basic procedures. All participating groups completed the OSA case according to the standard curriculum schedule. Researchers did not alter the case materials, the sequencing of activities, or assessment procedures. Participation in the study did not affect students’ course grades. The study received ethics approval from the Human Research Ethics Committee of the university (Reference Number: EA210511). Informed consent was obtained from all participants, including both students and tutors, ensuring voluntary participation and adherence to ethical research standards. Participants were informed of the study’s purpose, procedures, and their right to withdraw at any time without penalty. Data collection Primary data consisted of audiovisual recordings of the first 90-minute tutorial session for each PBL group, capturing the initial case exploration phase during which knowledge gaps, hypothesis generation, and collaborative problem-solving are most pronounced (Hmelo-Silver, 2004 ). Each session was recorded using a combination of fixed-position video cameras and audio recorders to capture speech from all participants with minimal intrusion. Recordings were transcribed verbatim. In addition to spoken utterances, salient non-verbal cues relevant to the interaction (e.g., prolonged silences, overlapping talk, laughter, notable gestures, visible displays of confusion or agreement) were annotated in the transcripts to contextualize the verbal exchanges and support interpretation of socio-emotional and regulatory processes. Data analysis We adopted an iterative, mixed-methods analytic strategy that combined theory-driven qualitative coding with quantitative modeling of discourse patterns, including lag sequential analysis. We grounded our analysis in the framework of Hmelo-Silver & Barrows’s ( 2008 ) model of knowledge construction in PBL. Guided by the framework, we focused on three overarching dimensions of discourse: metacognition (for example, planning, monitoring, and evaluating one’s own or the group’s thinking), co-regulation and socially shared regulation (for example, prompting, scaffolding, coordinating, or aligning others’ cognitive and regulatory activity), and socio-emotional interaction (for example, expressions of support, tension, alignment, or disagreement). These dimensions align with the cognitive and social components of the Community of Inquiry framework and allowed us to examine how cognitive presence, social presence, and aspects of teaching presence were enacted through talk. We first prepared the transcripts for coding by segmenting them into units suitable for analysis. We defined the basic unit of analysis as the “meaning unit,” that is, the smallest segment of talk that expressed a coherent idea or served a single interactional function, in line with prior discourse-analytic work in collaborative learning (e.g., Campbell et al., 2013 ). When a single turn at talk contained multiple functions, for instance, a question followed by a tentative explanation, we divided it into separate meaning units. Conversely, we combined adjacent clauses or turns into a single unit when they were produced by the same speaker, addressed the same topic, and fulfilled the same functional category. For each meaning unit, we recorded the speaker role (tutor vs. student). We allowed multiple codes per meaning unit when the utterance simultaneously served more than one function, such as a metacognitive evaluation expressed in an explicitly supportive socio-emotional tone. Drawing on the published frameworks, we developed an initial coding manual that specified operational definitions and illustrative examples for each category. Two researchers (XX and XX) first read one complete transcript together to familiarize themselves with the data and to test the applicability of the initial categories to the specific PBL context. During this joint review, we refined category labels and adjusted definitions to better capture the nuances of PBL interaction, particularly the boundary between metacognitive moves and socio-emotional evaluations of group functioning, while maintaining conceptual alignment with the source frameworks. We then piloted the draft codebook on a subset of the data (approximately one third of one transcript). XX and XX independently coded this subset, assigning one or more codes to each meaning unit according to the manual. After completing our independent coding, we met to compare the decisions line by line. Where the codes converged, we took this as evidence of clarity and applicability of the definitions. Where they diverged, we revisited the relevant transcript segments and discussed our underlying reasoning. Through this process, we clarified the boundaries between similar categories, introduced subcodes where recurrent distinctions emerged, and expanded the manual with additional examples drawn from the data. As part of our initial analytic exploration, we also examined AI-assisted coding as a supplementary tool. For selected excerpts, we used ChatGPT (OpenAI’s GPT-4 model) to generate tentative suggestions for possible codes, based on brief descriptions of our coding framework. We treated these AI-generated labels solely as heuristic prompts to broaden the set of candidate interpretations and to surface patterns that might be less immediately obvious. This approach is consistent with emerging evidence that large language models can support, but should not replace, human-led text annotation and content analysis when used under careful supervision and with a clear coding scheme (Gilardi et al., 2023 ). At no point did we accept AI-generated codes automatically. XX and XX independently evaluated, modified, or rejected all AI suggestions by comparing them to the finalized codebook and to the full conversational context, and all final coding decisions were made by human coders, in line with established standards of rigor and reflexivity in qualitative research (Miles et al., 2014; Nowell et al., 2017 ). Once we had stabilized the codebook, XX and XX independently coded all meaning units across the four PBL sessions. We worked in multiple passes to reduce fatigue and guard against coder drift from the agreed definitions. After completing the independent coding, we used a negotiated-agreement approach (Campbell et al., 2013 ) to establish a consensus dataset. We first identified all instances of disagreement between XX’s and XX’s coding. For each discrepant meaning unit, we jointly revisited the transcript segment, consulted the codebook, and articulated our rationales for each assigned code. When discussion led to a shared interpretation, we assigned the agreed-upon code or set of codes. This process ensured that the final coded dataset reflected a shared, theory-informed interpretation rather than idiosyncratic judgments. We then used the consensus-coded dataset for both descriptive and sequential quantitative analyses, supported by qualitative micro-analysis. First, we calculated code frequencies and distributions for each group and separately for tutors and students. This allowed us to describe the prevalence of metacognitive, co-regulatory, and socio-emotional moves, examine patterns of co-occurrence between categories (for example, metacognitive moves embedded in socio-emotional support), and track changes in code distributions over the course of the tutorial sessions in relation to the different phases of PBL work. To examine how discourse moves unfolded over time and how specific types of utterances tended to follow one another, we conducted lag sequential analysis (LSA, Bakeman & Quera, 2011 ) on the time-ordered stream of coded meaning units. For this purpose, we treated each meaning unit as an event in a behavioral sequence, labeled with its primary discourse category (e.g., metacognitive move, co-regulatory prompt, socio-emotional support) and speaker role. To ensure sufficient cell counts and interpretability, we collapsed functionally similar subcodes into broader categories for the sequential analysis. We then computed first-order (lag 1) transitional probabilities to estimate how likely a given type of discourse move was to follow another in the next meaning unit. We compared these observed probabilities with expected values under the assumption of independence and calculated adjusted residuals (z-scores) to identify statistically significant patterns of attraction or inhibition between categories (Bakeman & Quera, 2011 ). We interpreted transitions with z > 1.96 (p < .05) as significantly more or less likely than chance, with attention to their coherence within the broader pattern of results and the underlying theory. We ran separate sets of analyses for student moves and tutor moves and, where relevant, for different phases of the PBL session (for example, initial case reading, hypothesis generation, clarification of learning issues). To complement the frequency counts and lag‑sequential analyses, we conducted an epistemic network analysis (ENA) to model how metacognitive, co‑regulatory, and socio‑emotional codes were interconnected in students’ discourse. ENA represents the structure of relations among codes as a weighted network. In our study, each coded meaning unit served as the basic stanza. When two codes from different categories co‑occurred within the same meaning unit (e.g., Evaluation + Activate), this was treated as a co‑occurrence and contributed weight to the connection between those two codes. Co‑occurrences involving only codes from the same overarching category (e.g., Orientation + Evaluation, both metacognitive) were treated as new composite behaviors and were therefore not entered into the ENA co‑occurrence matrix, consistent with our co‑occurrence definitions. Co‑occurrence matrices were aggregated across the four tutorials (109 co‑occurrence events in total) and visualized as a single epistemic network. In the resulting network diagram, nodes represent individual codes, node size reflects overall connection strength (i.e., how frequently that code co‑occurred with all others), and edge thickness represents the relative frequency with which a pair of codes co‑occurred. This visualization allowed us to identify central codes and tightly connected clusters that characterize typical patterns of collaborative regulation in the PBL tutorials. Finally, we selected episodes that were particularly rich in regulatory and socio-emotional activity for qualitative, micro-analytic sequential analysis. In these episodes, we traced in detail how specific discourse moves, for example, a tutor’s regulatory prompt, a student’s expression of uncertainty, or a peer’s supportive response, initiated, sustained, or redirected knowledge construction and group regulation within the evolving community of inquiry. By integrating the quantitative findings from the lag sequential analysis with these fine-grained qualitative interpretations, we generated a nuanced account of how cognitive, metacognitive, regulatory, and socio-emotional processes were jointly enacted and dynamically coordinated in real-time PBL discourse. Results After the analysis of four tutorials, a total of 649 coded discourse segments were identified—105 from tutors and 544 from students. Among students, Elaboration was the most common metacognitive level, accounting for 172 instances (31.6%). Other commonly metacognitive levels of students included Orientation, Evaluation, and Planning (Table 1 ). Among co-regulatory activities, Active was the most frequent, followed by Confirm, while Inhibit activities were less common (Table 2 ). In the social-emotional interactions, Interactive Social Presence (ISP) was the most prevalent. Affective Social Presence (ASP) and Cohesive Social Presence (CSP) were also identified in the tutorials; however, no negative social-emotional interactions were observed. Table 1 Metacognitive activities over time in PBL Metacognitive activities Example Frequency Orientation Also, maybe just very quickly, look at what are some good sleep hygiene practices that we can encourage the patient and his wife to adopt. And I think it's really important to look at this not just from a medical perspective but also from a lifestyle change perspective. 82 Planning And I found a study just now. A prospective cohort study on sleep deficiency, and motor vehicle car crash risk in the general population, and they found that severe sleep apnea was associated with a 123% increased crash risk compared with no sleep apnea. So there is a correlation there. 66 Executing So would medication be required to treat stage 2 hypertension? 2 Monitoring How to find those? 5 Evaluation Yes, I do agree that actually drinking wine can relate to the sleep apnea. And you know, because alcohol basically may end as a muscle relaxant, and therefore at the night time it may relax muscle and further contribute to the airway obstruction… 70 Elaboration There's also noted that patient drinks two to three glasses of red wine with of his friends on the weekends… 172 Table 2 Co-regulatory activities over time in PBL Co-regulatory activities Example Frequency Activate I'd like to expand on the sleep test you mentioned… 63 Confirm I also agree that maybe his hypertension and snoring may affect the daily life… 24 Slow I think that we're on of the coffee before we mentioned. We don't know whether he started drinking coffee before he had high blood pressure or after it developed. So, oh. 5 Change I don't really agree on hiding everything. One of the things that weight gain is obvious… So I think that's something he really tries to hide. But I think some symptoms really go unnoticed. 6 Table 3 Social emotional interactions over time in PBL Social emotional interactions Example Frequency ASP Oh, sorry! What's your question other than like… 6 ISP Yes, I do agree what you have said… 42 CSP Okay, I can. I can write it in… 1 Tutors in the PBL tutorials facilitated rather than replaced student reasoning. In four groups, 105 discourse segments were identified, including questions, statements and task-oriented talk. The most frequent discourse were Request/Directive and Ask for Explanation, which reflect a facilitative role, prompting student thinking and guiding group reasoning without providing direct answers in PBL (Table 4 ). Table 4 Tutor’s activities over time in PBL Tutor’s activities Frequency Tutor non-regulatory statement Compliment 10 Tutor task-oriented talk Pinpoint someone to respond 2 Need clarification 3 Monitoring 7 Self-directed learning 9 Request/Driective 20 Tutor statement Agreement 3 Clarification 10 Tutor question Verification 13 Ask for an action plan 5 Ask for an example 2 Ask for an explanation 19 Ask for details in the case 2 Students’ behavior co-occurred with other behaviors in the tutorials. Only instances where different types of behaviors co-occurred within the same discourse by a person (e.g., Student 5’s discourse coded as evaluation + active) were considered co-occurrences. In contrast, co-occurrences of the same type of activities or interactions (e.g., Student 1’s discourse coded as evaluation + orientation) were treated as new combined behaviors and were not included in the analysis. Figure 1 present the frequencies of co-occurrences among metacognitive activities, co-regulatory episodes, and socio-emotional interactions. ENA analysis To characterise how different kinds of discourse moves were interwoven, we conducted an Epistemic Network Analysis (ENA) on the coded student data. ENA models patterns of association among codes as a weighted network. In our analysis, each meaning unit served as the basic stanza. We defined a co‑occurrence as the presence of two or more codes from different overarching categories (metacognitive activity, co‑regulation, social‑emotional interaction) within the same meaning unit (for example, Evaluation + Activate). When multiple codes belonged to the same category (for example, Orientation + Evaluation, both metacognitive), they were treated as a new composite behaviour and were not entered into the co‑occurrence matrix. For each tutorial, we first constructed co‑occurrence matrices that captured how often each pair of codes occurred together. These matrices were then aggregated across the four groups, yielding 109 distinct co‑occurrence events. The resulting epistemic network was visualised with nodes representing individual codes and edges representing their co‑occurrence frequencies. Node size reflected overall connection strength (the sum of a code’s co‑occurrences with all others), and edge thickness indicated the relative strength of the association between two codes (Fig. 2 ). Inspection of this network allowed us to identify central hubs and tightly linked clusters that typified students’ collaborative regulatory patterns during PBL discussions. LSA analysis Single metacognitive activities encompassed diverse discourse combinations (Fig. 3 ), including transitions from metacognition to same or other levels of metacognition (e.g. Orientation to Orientation, Orientation to Planning), metacognitive clustering (e.g. Planning to Planning + Orientation), co-regulatory (e.g. Orientation to Activate), social-emotional interactions (e.g. Planning to ISP), and integrated sequences such as metacognition with co-regulatory (e.g. Elaboration to Evaluation + Confirm), metacognition with social-emotional interactions (e.g. Evaluation to Elaboration + Orientation + ISP), and even combinations involving metacognition, co-regulatory, and social-emotional interactions (e.g. Planning to Orientation + Confirm + ISP). Interestingly, the discourse moves observed were not limited to starting from metacognition. Students also frequently shifted from social emotional interactions to other social emotional interactions (e.g. CSP to ASP) or metacognitive behaviors (e.g. ASP to planning). In some cases, non-concurrent co-regulatory actions also preceded metacognitive engagement (e.g. Activate to Elaboration). Elaboration + Active was the most frequently occurring composite discourse move, and it often served as a pivot to several types of discourse moves, single or combined metacognition + co-regulation, metacognition + social emotional interactions or all three Metacognition + co-regulatory + social emotional interactions (e.g. Elaboration + Active to Planning + Active, Elaboration + Active to Elaboration + Evaluation + Active, Elaboration + Active to Elaboration + ISP, Elaboration + Active to Elaboration + Active + ISP). Discourse involving Metacognition + co-regulatory + social emotional interactions often led to sequences with the same tripartite structure (e.g., Elaboration + Active + ISP → Orientation + Active + ISP), or to transitions involving single or combined metacognitive moves with or without co-regulatory or social emotional interactions (e.g. Orientation + Confirm + ISP to Elaboration + Active, Planning + Active + ISP to Planning + Elaboration + ISP). Tutor contributions primarily supported student metacognition (Fig. 4 ). Nearly all the tutors’ questions, statements and task-oriented talk lead to different types of students’ metacognitive activities. Verification statements were frequently followed by executing actions or elaboration. Requests for an action plan typically elicited orientation and elaboration moves. Similarly, questions seeking explanations triggered elaboration, monitoring, and planning. Finally, when participants asked for additional details in the case, responses were most to evaluate. Tutor task-oriented talk most often shifted toward orientation, elaboration, and planning. When tutors required clarification, this was typically followed by either planning with elaboration or elaboration alone. While many tutor moves prompted metacognitive responses, only clarification from tutors triggered metacognitive responses coupled with social-emotional components (e.g. Clarification to Evaluation + ISP). This may suggest that clarifications can not only help build cognition, they also shape the group’s relational climate. Discussion We examined student and tutor discourse moves in four PBL tutorials using an integrated coding framework that captured metacognitive activity, co-regulation, socio-emotional interaction, and tutor interventions at the level of turn-by-turn interaction. Our findings highlight the tightly interwoven nature of these processes in authentic medical PBL and extend existing theory in several ways. First, elaboration emerged as the dominant metacognitive activity, followed by orientation, evaluation, and planning, underscoring the centrality of elaborative talk in clinical reasoning and collaborative knowledge building. This pattern is highly consistent with core PBL theory and empirical work showing that knowledge construction in tutorial groups is driven by the activation of prior knowledge and its elaboration through explanation, hypothesis generation, and integration of evidence (Mercer, 2000 ; Schmidt et al., 2011 ; van Boxtel et al., 2000 ). Our data add nuance by showing how these elaborative moves are not only frequent but also highly connected in the discourse: lag-sequential patterns indicated that elaboration tended to trigger further questioning, clarification, and uptake, rather than remaining monologic. In line with cognitive and sociocultural accounts of PBL, elaboration appears to function as a hub that makes reasoning explicit, collectively inspectable, and open to refinement (Hmelo-Silver, 2004 ; Rotgans & Schmidt, 2011 ; Schmidt, 1983 ). This reinforces the idea that PBL is most productive when students are encouraged not simply to “share ideas” but to articulate and interrogate their reasoning in ways that invite transactive responses. Second, within co-regulation, Active and Confirm moves were most frequent, whereas Inhibit and Change moves were rare. This distribution suggests that novice medical students in our context primarily engaged in supportive, forward-moving regulation, prompting participation, acknowledging contributions, and “staying with” the current line of reasoning, rather than overtly corrective or re-directive regulation. This pattern is aligned with prior work on co-regulation and socially shared regulation of learning, which emphasizes that prompts, confirmations, and uptake are foundational for sustaining joint attention, aligning goals, and coordinating strategy use in collaborative tasks (Hadwin et al., 2017 ; Järvelä & Hadwin, 2013 ; Volet et al., 2009 ). At the same time, the scarcity of explicit inhibitory or change-oriented moves suggests that groups may have been less inclined to challenge peers’ ideas, abandon unproductive lines of reasoning, or explicitly renegotiate their plans. This may reflect early-stage group development, novice status, and cultural preferences for maintaining harmony and face in East Asian settings, where direct confrontation and overt control are often avoided (Frambach et al., 2012 ). It may also indicate that more directive forms of regulation were “outsourced” to the tutor, with peers enacting regulation in relatively subtle and implicit ways. Our sequential analyses support this interpretation: we found that co-regulatory Active and Confirm moves often followed tutor prompts or student elaborations, stabilizing ongoing inquiry rather than redirecting it. Third, among socio-emotional interactions, ISP was most prevalent, whereas ASP and CSP forms were less frequent, and no negative socio-emotional moves were observed. This profile suggests that task-focused social engagement (i.e., turn-taking, responsiveness, and acknowledgement) predominated over expressions of emotion or explicit identity-building. In the CoI framework, interactive social presence supports cognitive presence by maintaining flow and reciprocity in discourse, which in turn sustains inquiry (Garrison et al., 1999 ; Richardson et al., 2017; Rourke et al., 1999 ). The absence of negative socio-emotional moves could be interpreted as evidence of psychological safety and supportive norms (Edmondson, 1999 ), but it may also reflect situational and cultural moderation (first tutorials, novice groups, tutor presence, and local communication norms). Rather than indicating the absence of tension, the discourse may reflect culturally shaped ways of managing disagreement implicitly, for example, by redirecting the topic or reformulating contributions without direct rejection. Notably, the most frequent co-occurrences involved Elaboration, Active, and ISP, indicating that cognitive elaboration was often reinforced through co-regulatory prompts and interactive social engagement. These elements do not operate in isolation: metacognition, co-regulation, and socio-emotional interaction mutually scaffold one another as collaborative problem solving unfolds (Bakhtiar et al, 2018 ; Isohätälä et al, 2017 ; Järvenoja & Järvelä, 2009 ). Our lag-sequential patterns showed that elaboration commonly prompted further questioning and uptake, while ISP supported sustained transactivity, aligning with prior findings that co-regulation often begins with questions or explanations and is stabilized by shared positive engagement (Volet et al., 2009 ; Weinberger & Fischer, 2006 ). Tutor discourse moves were dominated by facilitative prompts and scaffolds, echoing established PBL facilitation principles that emphasize guiding inquiry without providing answers and cultivating productive discourse norms (Dolmans et al., 2002 ; Hmelo-Silver & Barrows, 2008 ; Schmidt & Moust, 1995 ). Our analyses indicate that tutor interventions functioned as key catalysts that activated both cognitive and socio-emotional dimensions of group work. Facilitative questioning, probing, and reframing were frequently followed by students’ metacognitive reasoning in combination with interactive social presence, suggesting that tutors effectively invited students to externalize their thinking while maintaining a climate of engagement. In this way, tutors appeared to shape both the epistemic quality of discourse and the climate of participation, helping students to evaluate competing explanations, tolerate uncertainty, and take intellectual risks (Edmondson, 1999 ; Yew & Schmidt, 2012 ). Of particular interest, we observed sequences in which socio-emotional engagement (such as ISP) preceded and appeared to open a pathway into metacognitive reasoning, indicating that “light” social moves can serve as a bridge into deeper inquiry. This extends prior work on emotion and motivation regulation in collaborative learning (D’Mello & Graesser, 2012 ; Isohätälä et al., 2017 ; Pekrun et al., 2006 ) by demonstrating how social presence, co-regulation, and metacognition interlace in the moment-to-moment unfolding of PBL talk. Practical implications Taken together, our findings reinforce socio-cognitive and socio-emotional perspectives of collaborative learning by showing that metacognition, co-regulation, and social presence are interdependent rather than discrete processes (Järvelä & Hadwin, 2013 ; Volet et al., 2012). Group learning in PBL emerges as a dynamic integration of reasoning, regulation, and affect, such that shifts in one dimension (for example, a tutor prompt or a student’s interactive response) can cascade into others (for example, elaboration, evaluation, or planning). Practically, this supports calls to design and facilitate PBL in ways that deliberately intertwine cognitive scaffolds with socio-emotional supports rather than treating them as separate concerns (Dolmans et al., 2016 ; Hmelo-Silver & Barrows, 2008 ). For tutor development, our results point to the value of training that explicitly targets both metacognitive and socio-emotional facilitation. On the cognitive side, tutors can be supported to use questions that prompt articulation of hypotheses, request justification and evidence, and encourage comparison of alternative explanations, thereby increasing elaboration and evaluation. On the socio-emotional side, tutors benefit from strategies for building psychological safety, such as normalizing uncertainty, acknowledging effort, and modeling respectful challenge, so that students feel able to voice tentative ideas and to disagree productively. Our sequential findings suggest that even brief tutor moves can set in motion chains of elaboration and responsive engagement, underscoring the leverage of well-timed interventions. At the group level, our results suggest the need to move beyond a default focus on confirmation toward more adaptive regulatory patterns that include constructive redirection and change when needed. Groups may benefit from explicit norms and scripts that frame disagreement as an epistemic resource rather than a relational threat, such as ground rules for exploratory talk, structured turn-taking, and guidelines for constructive controversy (Johnson & Johnson, 2009 ; Weinberger & Fischer, 2006 ). Co-regulation can also be strengthened by integrating structured prompts for shared monitoring and planning—for example, brief “plan–monitor–evaluate” check-ins, rotating roles for summarizing and questioning, or visual tools that make learning issues and hypotheses visible to all (Kollar et al., 2006 ; Panadero & Järvelä, 2015 ). These supports may help novice groups move from predominantly supportive confirmation toward more flexible, strategy-oriented regulation. Given that our study took place in a Hong Kong medical school, the findings also underscore the importance of culturally responsive facilitation. In contexts where maintaining harmony and respecting hierarchy are highly valued, tutors may need to scaffold forms of challenge and critique that feel legitimate and safe to students, such as inviting alternative explanations, asking for “other possibilities,” or depersonalizing critique by focusing on ideas rather than individuals (Frambach et al., 2012 ). Attention to language (for example, softening markers, inclusive pronouns) and to non-verbal cues may further support the emergence of shared regulation without undermining cultural expectations of respect. Strengths, limitations, and future directions A key strength of this study is its micro-analytic approach to audiovisual data, which allowed us to capture the co-occurrence and sequencing of metacognitive, regulatory, and socio-emotional discourse moves in authentic medical PBL tutorials. By integrating multiple analytic frameworks and adding lag-sequential analysis to fine-grained coding, we were able to show not only which moves occurred, but how they triggered, stabilized, or redirected one another over time. This advance understanding of how knowledge construction is organized and regulated in practice and illuminates the dual cognitive–affective pathways through which tutors and students shape collaboration. At the same time, several limitations warrant caution. Our sample was small (four groups) and focused on the first 90-minute tutorial session for a single clinical case (OSA) with first-year students in one institution. This constrains the generalizability of our findings and likely narrows the range of regulatory and socio-emotional phenomena observed. For example, more complex or conflictual dynamics may emerge in later sessions, with more advanced students, or when groups face greater epistemic uncertainty. The absence of negative socio-emotional moves in our data may reflect situational factors such as novice status, tutor presence, and social desirability rather than their true absence. In addition, although lag-sequential analysis provided valuable temporal insight, it captures primarily short-range dependencies and does not fully model longer trajectories of regulation across sessions. Finally, we focused on discourse processes without directly linking them to individual or group learning outcomes, such as diagnostic accuracy, conceptual understanding, or exam performance. In future work, we recommend extending this line of research in several directions. Longitudinal designs that follow the same PBL groups across multiple cases and semesters would allow us to trace how patterns of metacognition, co-regulation, and social presence evolve as groups mature and as students gain clinical experience. Linking discourse sequences to independent measures of learning and performance would help identify which combinations and trajectories of discourse moves are most productive. Comparative studies across institutions and cultures could clarify how local norms shape the balance between confirmation and challenge, as well as the expression of socio-emotional support and conflict. Methodologically, integrating process data from additional sources, such as physiological or digital trace data, self- and peer-reports of regulation, and post-session reflections, could enrich our understanding of how observed discourse aligns with unobservable cognitive and emotional processes. Conclusion This study demonstrates that metacognition, co-regulation, and social presence form a tightly integrated system in PBL tutorials, with elaboration-centered discourse, supportive peer regulation, and interactive engagement driving collaborative reasoning. Students predominantly activated and confirmed ideas within a psychologically safe climate, while tutors strategically prompted deeper inquiry through questioning rather than direct instruction. For practice, these findings advocate for tutor development emphasizing metacognitive scaffolding alongside socio-emotional attunement, particularly in cultural contexts prioritizing relational harmony. Structured group protocols, such as rotating roles for critique, visual mapping of hypotheses, and norms reframing uncertainty as productive, can help balance confirmatory and exploratory talk. By intentionally designing PBL to synergize cognitive, regulatory, and relational processes, medical educators can transform tutorials into dynamic spaces where engagement fuels both conceptual growth and the collaborative dispositions essential for clinical practice. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This work was supported by the Hong Kong University Grants Committee (UGC) under the General Research Fund (GRF) [Project Number:17119422]. Author Contribution Q.H., B.Z., and F.A.G. conceived and designed the study. Q.H., Y.Y., and F.A.G. were responsible for data collection and analysis. Q.H. and Y.Y. prepared the original manuscript draft. B.Z. contributed to methodological and analytical validation. G.T. and J.P.Y.C. provided critical resources and educational context. All authors contributed to the interpretation of results, reviewed the manuscript drafts, and approved the final version for submission. Acknowledgement The authors wish to thank all the PBL tutors and students whose participation made this study possible. We are also grateful to our collogues for their support and assistance throughout the research process. Data Availability The data that support the findings of this study are available from the corresponding author upon reasonable request. The raw video recordings and full transcripts are not publicly available due to them containing information that could compromise research participant privacy and consent. References Bakeman, R., & Quera, V. (2011). Sequential Analysis and Observational Methods for the Behavioral Sciences. 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By engaging students in small-group discussions centered on complex patient cases, PBL is intended to foster hypothesis generation, integrative reasoning, and the construction of biomedical and clinical knowledge (e.g., Hmelo-Silver \u0026amp; Barrows, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Knowledge construction has been identified as the core process in PBL, shaping both individual and collaborative learning outcomes and contributing to clinical reasoning, diagnostic decision-making, and professional identity formation (Chin \u0026amp; Chia, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Schmidt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Understanding how knowledge is constructed in situ, particularly in the moment-to-moment discourse of PBL tutorials, can therefore provide critical insights for designing and facilitating learning environments that better support deeper understanding, teamwork, and the development of reflective practitioners.\u003c/p\u003e \u003cp\u003eA growing body of research has highlighted the central role of regulation in effective learning. Zimmerman (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) defines self-regulation as the processes learners use to plan, monitor, control, and reflect on their cognition, motivation, and behavior in pursuit of goals, with metacognition referring specifically to learners\u0026rsquo; monitoring and intentional guidance of their thinking. Metacognition is a key component of self-regulation and a predictor of learning outcomes across domains (Muijs \u0026amp; Bokhove, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The PBL environment, with its emphasis on student agency and inquiry, has been shown to encourage students\u0026rsquo; metacognitive self-regulation during learning (Sungur \u0026amp; Tekkaya, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Beyond the individual, regulation as a social process that unfolds within interaction. Co-regulation refers to regulation that is prompted, supported, or guided by others (e.g., tutors or peers) during the learning process, while socially shared regulation refers to the joint regulation of shared goals, strategies, and monitoring by the group as a collective (Hadwin et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Hadwin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These regulation modes are mutually supportive and interdependent: co-regulatory prompts and scaffolds can catalyze individual self-regulation, and jointly regulated group processes can stabilize and sustain productive collaboration (Hadwin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Panadero \u0026amp; J\u0026auml;rvel\u0026auml;, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffective collaboration in PBL requires coordinated cognitive work and the regulation of socio-emotional dynamics. Students must integrate diverse perspectives, distribute and manage tasks, and navigate interpersonal interactions and shared uncertainty. Self-regulation equips learners to plan, monitor, and evaluate their own thinking; co-regulation extends these processes to the group by inviting prompts, feedback, and scaffolding of others\u0026rsquo; thinking; and SSRL enables the group to align on goals, standards, and strategies (Hadwin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; J\u0026auml;rvel\u0026auml; \u0026amp; Hadwin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). At the same time, socio-emotional interaction, such as fostering trust, encouragement, and psychological safety, and managing frustration or disagreement, supports sustained engagement and the willingness to take intellectual risks (Edmondson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; J\u0026auml;rvenoja \u0026amp; J\u0026auml;rvel\u0026auml;, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Volet et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). These socio-emotional processes can be productively framed using the Community of Inquiry (CoI) framework, which conceptualizes effective collaborative learning as the intersection of cognitive, social, and teaching presence (Garrison et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). In the CoI framework, interactive social presence supports cognitive presence by maintaining flow and reciprocity in discourse, which in turn sustains inquiry (Garrison et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Richardson \u0026amp; Lowenthal, 2017; Rourke et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). From this perspective, the discourse of PBL tutorials is not merely a vehicle for information exchange but the primary medium through which participants co-construct meaning, maintain group cohesion, and orchestrate regulatory processes. Research in collaborative learning has begun to uncover the complex coupling between social-emotional and cognitive processes, showing that regulation of emotion and motivation is intertwined with the regulation of cognitive activity and knowledge construction (Bakhtiar et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Isoh\u0026auml;t\u0026auml;l\u0026auml; et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; J\u0026auml;rvenoja \u0026amp; J\u0026auml;rvel\u0026auml;, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Volet et al., 2012). Because collaborative learning can involve multiple regulation modes that fluctuate over time, and because group members\u0026rsquo; behaviors and perceptions can trigger diverse socio-emotional trajectories, understanding how these processes shift under different regulatory conditions, and how they vary across cultural settings, is essential for grasping the complexity of PBL (Frambach et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Huang \u0026amp; Lajoie, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTutors play a pivotal role in shaping these intertwined cognitive and socio-emotional processes. In PBL, tutors are expected to facilitate and activate student learning, promote effective group functioning, monitor the quality of learning, and intervene when necessary without providing direct answers (De Grave et al, 1999; Hmelo-Silver \u0026amp; Barrows, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Maudsley, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Schmidt \u0026amp; Moust, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Through strategic questioning, modeling, and scaffolding, tutors can help students articulate reasoning, evaluate evidence, and connect biomedical concepts to clinical contexts (Dolmans et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hmelo-Silver \u0026amp; Barrows, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Their role extends beyond cognition to the cultivation of a psychologically safe environment in which students feel comfortable sharing difficulties, acknowledging knowledge gaps, and constructively challenging one another\u0026rsquo;s ideas (Edmondson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Hammar Chiriac et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Effective tutor observation and guidance ensure balanced participation, productive group norms, and adaptive regulation of both task and socio-emotional demands, conditions that are essential for successful PBL sessions (Dolmans et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hammar Chiriac et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Schmidt \u0026amp; Moust, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite growing recognition of the importance of metacognition, co-regulation, socio-emotional interaction in collaborative learning, research in medical education has only sporadically examined how these processes co-occur and unfold in real PBL tutorials. Much of the existing work investigates these dimensions in isolation or uses self-report measures, which can obscure their interdependence and temporal dynamics (Dolmans et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Panadero, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Hadwin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A recent systematic review of process-oriented analysis in PBL discourse has called for future research to investigate teams\u0026rsquo; co-regulation of knowledge construction at the episode level (Chen \u0026amp; Zheng, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Fine-grained, video-based analyses of discourse moves, capturing how students and tutors actually prompt, scaffold, and monitor their own knowledge construction are still needed in medical education PBL, and cross-cultural differences in these interactional patterns are underexplored (Frambach et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Schmidt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Addressing this gap, the present study provides a micro-analytic account of the co-occurrence and sequential unfolding of cognitive, metacognitive, regulatory, socio-emotional discourse moves between students and tutors during authentic PBL tutorials in an Asian medical school. By examining real-time interactional data, this study advances understanding of how knowledge construction is organized and regulated in practice and offers actionable implications for tutor training, PBL design, and culturally responsive facilitation. To that end, we address the following research questions:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eWhat is the relationship between metacognitive activities, co-regulation/SSRL, and socio-emotional interactions in PBL tutorials?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHow do students\u0026rsquo; and tutors\u0026rsquo; discourse moves unfold over time during PBL tutorials?\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch design\u003c/h2\u003e \u003cp\u003eThis mixed-methods study investigated knowledge construction and discourse dynamics in PBL tutorials. Combining qualitative, video-based discourse analysis with quantitative coding of interactional sequences, the study examined how students and tutors co-regulated learning and managed socio-emotional interactions during collaborative case discussions. The design responds to calls for process-oriented analyses of PBL to inform evidence-based facilitation practices (Chen \u0026amp; Zheng, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dolmans et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Hmelo-Silver \u0026amp; Barrows, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The analytic focus on cognitive, metacognitive, regulatory, and socio-emotional discourse moves is conceptually aligned with the Community of Inquiry framework in emphasizing the interplay of cognitive and social processes in sustaining inquiry.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThe study was conducted in a medical school at a university in Hong Kong. Four PBL groups, each including one tutor and approximately 10 students, were invited to participate between January and March 2024. All students were in the first year of the Bachelor of Medicine and Bachelor of Surgery (MBBS) program. The four tutors were drawn from different departments within the university: three from biomedical sciences and one from clinical medicine. All tutors had completed institutional PBL tutor training and had prior experience facilitating PBL for medical students. Students had participated in a previous series of PBL sessions before the study; however, because group composition changes regularly in the curriculum, students within each study group may or may not have previously worked together.\u003c/p\u003e \u003cp\u003eThe focal PBL case addressed Obstructive Sleep Apnea (OSA), introduced at the beginning of the second semester, and was designed to deepen students\u0026rsquo; understanding of the cardiopulmonary and respiratory systems. This case was selected because first-year students had already accumulated some PBL experience in the first semester, allowing the analysis to focus on groups that were familiar with the PBL format rather than still learning the basic procedures.\u003c/p\u003e \u003cp\u003eAll participating groups completed the OSA case according to the standard curriculum schedule. Researchers did not alter the case materials, the sequencing of activities, or assessment procedures. Participation in the study did not affect students\u0026rsquo; course grades. The study received ethics approval from the Human Research Ethics Committee of the university (Reference Number: EA210511). Informed consent was obtained from all participants, including both students and tutors, ensuring voluntary participation and adherence to ethical research standards. Participants were informed of the study\u0026rsquo;s purpose, procedures, and their right to withdraw at any time without penalty.\u003c/p\u003e\n\u003ch3\u003eData collection\u003c/h3\u003e\n\u003cp\u003ePrimary data consisted of audiovisual recordings of the first 90-minute tutorial session for each PBL group, capturing the initial case exploration phase during which knowledge gaps, hypothesis generation, and collaborative problem-solving are most pronounced (Hmelo-Silver, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Each session was recorded using a combination of fixed-position video cameras and audio recorders to capture speech from all participants with minimal intrusion.\u003c/p\u003e \u003cp\u003eRecordings were transcribed verbatim. In addition to spoken utterances, salient non-verbal cues relevant to the interaction (e.g., prolonged silences, overlapping talk, laughter, notable gestures, visible displays of confusion or agreement) were annotated in the transcripts to contextualize the verbal exchanges and support interpretation of socio-emotional and regulatory processes.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eWe adopted an iterative, mixed-methods analytic strategy that combined theory-driven qualitative coding with quantitative modeling of discourse patterns, including lag sequential analysis. We grounded our analysis in the framework of Hmelo-Silver \u0026amp; Barrows\u0026rsquo;s (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) model of knowledge construction in PBL. Guided by the framework, we focused on three overarching dimensions of discourse: metacognition (for example, planning, monitoring, and evaluating one\u0026rsquo;s own or the group\u0026rsquo;s thinking), co-regulation and socially shared regulation (for example, prompting, scaffolding, coordinating, or aligning others\u0026rsquo; cognitive and regulatory activity), and socio-emotional interaction (for example, expressions of support, tension, alignment, or disagreement). These dimensions align with the cognitive and social components of the Community of Inquiry framework and allowed us to examine how cognitive presence, social presence, and aspects of teaching presence were enacted through talk.\u003c/p\u003e \u003cp\u003eWe first prepared the transcripts for coding by segmenting them into units suitable for analysis. We defined the basic unit of analysis as the \u0026ldquo;meaning unit,\u0026rdquo; that is, the smallest segment of talk that expressed a coherent idea or served a single interactional function, in line with prior discourse-analytic work in collaborative learning (e.g., Campbell et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). When a single turn at talk contained multiple functions, for instance, a question followed by a tentative explanation, we divided it into separate meaning units. Conversely, we combined adjacent clauses or turns into a single unit when they were produced by the same speaker, addressed the same topic, and fulfilled the same functional category. For each meaning unit, we recorded the speaker role (tutor vs. student). We allowed multiple codes per meaning unit when the utterance simultaneously served more than one function, such as a metacognitive evaluation expressed in an explicitly supportive socio-emotional tone.\u003c/p\u003e \u003cp\u003eDrawing on the published frameworks, we developed an initial coding manual that specified operational definitions and illustrative examples for each category. Two researchers (XX and XX) first read one complete transcript together to familiarize themselves with the data and to test the applicability of the initial categories to the specific PBL context. During this joint review, we refined category labels and adjusted definitions to better capture the nuances of PBL interaction, particularly the boundary between metacognitive moves and socio-emotional evaluations of group functioning, while maintaining conceptual alignment with the source frameworks.\u003c/p\u003e \u003cp\u003eWe then piloted the draft codebook on a subset of the data (approximately one third of one transcript). XX and XX independently coded this subset, assigning one or more codes to each meaning unit according to the manual. After completing our independent coding, we met to compare the decisions line by line. Where the codes converged, we took this as evidence of clarity and applicability of the definitions. Where they diverged, we revisited the relevant transcript segments and discussed our underlying reasoning. Through this process, we clarified the boundaries between similar categories, introduced subcodes where recurrent distinctions emerged, and expanded the manual with additional examples drawn from the data.\u003c/p\u003e \u003cp\u003eAs part of our initial analytic exploration, we also examined AI-assisted coding as a supplementary tool. For selected excerpts, we used ChatGPT (OpenAI\u0026rsquo;s GPT-4 model) to generate tentative suggestions for possible codes, based on brief descriptions of our coding framework. We treated these AI-generated labels solely as heuristic prompts to broaden the set of candidate interpretations and to surface patterns that might be less immediately obvious. This approach is consistent with emerging evidence that large language models can support, but should not replace, human-led text annotation and content analysis when used under careful supervision and with a clear coding scheme (Gilardi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). At no point did we accept AI-generated codes automatically. XX and XX independently evaluated, modified, or rejected all AI suggestions by comparing them to the finalized codebook and to the full conversational context, and all final coding decisions were made by human coders, in line with established standards of rigor and reflexivity in qualitative research (Miles et al., 2014; Nowell et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOnce we had stabilized the codebook, XX and XX independently coded all meaning units across the four PBL sessions. We worked in multiple passes to reduce fatigue and guard against coder drift from the agreed definitions. After completing the independent coding, we used a negotiated-agreement approach (Campbell et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) to establish a consensus dataset. We first identified all instances of disagreement between XX\u0026rsquo;s and XX\u0026rsquo;s coding. For each discrepant meaning unit, we jointly revisited the transcript segment, consulted the codebook, and articulated our rationales for each assigned code. When discussion led to a shared interpretation, we assigned the agreed-upon code or set of codes. This process ensured that the final coded dataset reflected a shared, theory-informed interpretation rather than idiosyncratic judgments.\u003c/p\u003e \u003cp\u003eWe then used the consensus-coded dataset for both descriptive and sequential quantitative analyses, supported by qualitative micro-analysis. First, we calculated code frequencies and distributions for each group and separately for tutors and students. This allowed us to describe the prevalence of metacognitive, co-regulatory, and socio-emotional moves, examine patterns of co-occurrence between categories (for example, metacognitive moves embedded in socio-emotional support), and track changes in code distributions over the course of the tutorial sessions in relation to the different phases of PBL work.\u003c/p\u003e \u003cp\u003eTo examine how discourse moves unfolded over time and how specific types of utterances tended to follow one another, we conducted lag sequential analysis (LSA, Bakeman \u0026amp; Quera, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) on the time-ordered stream of coded meaning units. For this purpose, we treated each meaning unit as an event in a behavioral sequence, labeled with its primary discourse category (e.g., metacognitive move, co-regulatory prompt, socio-emotional support) and speaker role. To ensure sufficient cell counts and interpretability, we collapsed functionally similar subcodes into broader categories for the sequential analysis. We then computed first-order (lag 1) transitional probabilities to estimate how likely a given type of discourse move was to follow another in the next meaning unit. We compared these observed probabilities with expected values under the assumption of independence and calculated adjusted residuals (z-scores) to identify statistically significant patterns of attraction or inhibition between categories (Bakeman \u0026amp; Quera, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). We interpreted transitions with z\u0026thinsp;\u0026gt;\u0026thinsp;1.96 (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) as significantly more or less likely than chance, with attention to their coherence within the broader pattern of results and the underlying theory. We ran separate sets of analyses for student moves and tutor moves and, where relevant, for different phases of the PBL session (for example, initial case reading, hypothesis generation, clarification of learning issues).\u003c/p\u003e \u003cp\u003eTo complement the frequency counts and lag‑sequential analyses, we conducted an epistemic network analysis (ENA) to model how metacognitive, co‑regulatory, and socio‑emotional codes were interconnected in students\u0026rsquo; discourse. ENA represents the structure of relations among codes as a weighted network. In our study, each coded meaning unit served as the basic stanza. When two codes from different categories co‑occurred within the same meaning unit (e.g., Evaluation\u0026thinsp;+\u0026thinsp;Activate), this was treated as a co‑occurrence and contributed weight to the connection between those two codes. Co‑occurrences involving only codes from the same overarching category (e.g., Orientation\u0026thinsp;+\u0026thinsp;Evaluation, both metacognitive) were treated as new composite behaviors and were therefore not entered into the ENA co‑occurrence matrix, consistent with our co‑occurrence definitions. Co‑occurrence matrices were aggregated across the four tutorials (109 co‑occurrence events in total) and visualized as a single epistemic network. In the resulting network diagram, nodes represent individual codes, node size reflects overall connection strength (i.e., how frequently that code co‑occurred with all others), and edge thickness represents the relative frequency with which a pair of codes co‑occurred. This visualization allowed us to identify central codes and tightly connected clusters that characterize typical patterns of collaborative regulation in the PBL tutorials.\u003c/p\u003e \u003cp\u003eFinally, we selected episodes that were particularly rich in regulatory and socio-emotional activity for qualitative, micro-analytic sequential analysis. In these episodes, we traced in detail how specific discourse moves, for example, a tutor\u0026rsquo;s regulatory prompt, a student\u0026rsquo;s expression of uncertainty, or a peer\u0026rsquo;s supportive response, initiated, sustained, or redirected knowledge construction and group regulation within the evolving community of inquiry. By integrating the quantitative findings from the lag sequential analysis with these fine-grained qualitative interpretations, we generated a nuanced account of how cognitive, metacognitive, regulatory, and socio-emotional processes were jointly enacted and dynamically coordinated in real-time PBL discourse.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAfter the analysis of four tutorials, a total of 649 coded discourse segments were identified\u0026mdash;105 from tutors and 544 from students. Among students, Elaboration was the most common metacognitive level, accounting for 172 instances (31.6%). Other commonly metacognitive levels of students included Orientation, Evaluation, and Planning (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among co-regulatory activities, Active was the most frequent, followed by Confirm, while Inhibit activities were less common (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the social-emotional interactions, Interactive Social Presence (ISP) was the most prevalent. Affective Social Presence (ASP) and Cohesive Social Presence (CSP) were also identified in the tutorials; however, no negative social-emotional interactions were observed.\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\u003eMetacognitive activities over time in PBL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetacognitive activities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\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\u003eOrientation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAlso, maybe just very quickly, look at what are some good sleep hygiene practices that we can encourage the patient and his wife to adopt. And I think it's really important to look at this not just from a medical perspective but also from a lifestyle change perspective.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlanning\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnd I found a study just now. A prospective cohort study on sleep deficiency, and motor vehicle car crash risk in the general population, and they found that severe sleep apnea was associated with a 123% increased crash risk compared with no sleep apnea. So there is a correlation there.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExecuting\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSo would medication be required to treat stage 2 hypertension?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonitoring\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHow to find those?\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEvaluation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, I do agree that actually drinking wine can relate to the sleep apnea. And you know, because alcohol basically may end as a muscle relaxant, and therefore at the night time it may relax muscle and further contribute to the airway obstruction\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eElaboration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThere's also noted that patient drinks two to three glasses of red wine with of his friends on the weekends\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e172\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 \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\u003eCo-regulatory activities over time in PBL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCo-regulatory activities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\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\u003eActivate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI'd like to expand on the sleep test you mentioned\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConfirm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI also agree that maybe his hypertension and snoring may affect the daily life\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSlow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI think that we're on of the coffee before we mentioned. We don't know whether he started drinking coffee before he had high blood pressure or after it developed. So, oh.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eChange\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI don't really agree on hiding everything. One of the things that weight gain is obvious\u0026hellip; So I think that's something he really tries to hide. But I think some symptoms really go unnoticed.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\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 \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\u003eSocial emotional interactions over time in PBL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial emotional interactions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency\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\u003eASP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOh, sorry! What's your question other than like\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eISP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes, I do agree what you have said\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCSP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOkay, I can. I can write it in\u0026hellip;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\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\u003eTutors in the PBL tutorials facilitated rather than replaced student reasoning. In four groups, 105 discourse segments were identified, including questions, statements and task-oriented talk. The most frequent discourse were Request/Directive and Ask for Explanation, which reflect a facilitative role, prompting student thinking and guiding group reasoning without providing direct answers in PBL (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eTutor\u0026rsquo;s activities over time in PBL\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTutor\u0026rsquo;s activities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTutor non-regulatory statement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompliment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTutor task-oriented talk\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinpoint someone to respond\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeed clarification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonitoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-directed learning\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRequest/Driective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTutor statement\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgreement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClarification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTutor question\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVerification\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsk for an action plan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsk for an example\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsk for an explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsk for details in the case\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\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\u003eStudents\u0026rsquo; behavior co-occurred with other behaviors in the tutorials. Only instances where different types of behaviors co-occurred within the same discourse by a person (e.g., Student 5\u0026rsquo;s discourse coded as evaluation\u0026thinsp;+\u0026thinsp;active) were considered co-occurrences. In contrast, co-occurrences of the same type of activities or interactions (e.g., Student 1\u0026rsquo;s discourse coded as evaluation\u0026thinsp;+\u0026thinsp;orientation) were treated as new combined behaviors and were not included in the analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the frequencies of co-occurrences among metacognitive activities, co-regulatory episodes, and socio-emotional interactions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eENA analysis\u003c/h2\u003e \u003cp\u003eTo characterise how different kinds of discourse moves were interwoven, we conducted an Epistemic Network Analysis (ENA) on the coded student data. ENA models patterns of association among codes as a weighted network. In our analysis, each meaning unit served as the basic stanza. We defined a co‑occurrence as the presence of two or more codes from different overarching categories (metacognitive activity, co‑regulation, social‑emotional interaction) within the same meaning unit (for example, Evaluation\u0026thinsp;+\u0026thinsp;Activate). When multiple codes belonged to the same category (for example, Orientation\u0026thinsp;+\u0026thinsp;Evaluation, both metacognitive), they were treated as a new composite behaviour and were not entered into the co‑occurrence matrix.\u003c/p\u003e \u003cp\u003eFor each tutorial, we first constructed co‑occurrence matrices that captured how often each pair of codes occurred together. These matrices were then aggregated across the four groups, yielding 109 distinct co‑occurrence events. The resulting epistemic network was visualised with nodes representing individual codes and edges representing their co‑occurrence frequencies. Node size reflected overall connection strength (the sum of a code\u0026rsquo;s co‑occurrences with all others), and edge thickness indicated the relative strength of the association between two codes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Inspection of this network allowed us to identify central hubs and tightly linked clusters that typified students\u0026rsquo; collaborative regulatory patterns during PBL discussions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eLSA analysis\u003c/h3\u003e\n\u003cp\u003eSingle metacognitive activities encompassed diverse discourse combinations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), including transitions from metacognition to same or other levels of metacognition (e.g. Orientation to Orientation, Orientation to Planning), metacognitive clustering (e.g. Planning to Planning\u0026thinsp;+\u0026thinsp;Orientation), co-regulatory (e.g. Orientation to Activate), social-emotional interactions (e.g. Planning to ISP), and integrated sequences such as metacognition with co-regulatory (e.g. Elaboration to Evaluation\u0026thinsp;+\u0026thinsp;Confirm), metacognition with social-emotional interactions (e.g. Evaluation to Elaboration\u0026thinsp;+\u0026thinsp;Orientation\u0026thinsp;+\u0026thinsp;ISP), and even combinations involving metacognition, co-regulatory, and social-emotional interactions (e.g. Planning to Orientation\u0026thinsp;+\u0026thinsp;Confirm\u0026thinsp;+\u0026thinsp;ISP). Interestingly, the discourse moves observed were not limited to starting from metacognition. Students also frequently shifted from social emotional interactions to other social emotional interactions (e.g. CSP to ASP) or metacognitive behaviors (e.g. ASP to planning). In some cases, non-concurrent co-regulatory actions also preceded metacognitive engagement (e.g. Activate to Elaboration).\u003c/p\u003e \u003cp\u003eElaboration\u0026thinsp;+\u0026thinsp;Active was the most frequently occurring composite discourse move, and it often served as a pivot to several types of discourse moves, single or combined metacognition\u0026thinsp;+\u0026thinsp;co-regulation, metacognition\u0026thinsp;+\u0026thinsp;social emotional interactions or all three Metacognition\u0026thinsp;+\u0026thinsp;co-regulatory\u0026thinsp;+\u0026thinsp;social emotional interactions (e.g. Elaboration\u0026thinsp;+\u0026thinsp;Active to Planning\u0026thinsp;+\u0026thinsp;Active, Elaboration\u0026thinsp;+\u0026thinsp;Active to Elaboration\u0026thinsp;+\u0026thinsp;Evaluation\u0026thinsp;+\u0026thinsp;Active, Elaboration\u0026thinsp;+\u0026thinsp;Active to Elaboration\u0026thinsp;+\u0026thinsp;ISP, Elaboration\u0026thinsp;+\u0026thinsp;Active to Elaboration\u0026thinsp;+\u0026thinsp;Active\u0026thinsp;+\u0026thinsp;ISP). Discourse involving Metacognition\u0026thinsp;+\u0026thinsp;co-regulatory\u0026thinsp;+\u0026thinsp;social emotional interactions often led to sequences with the same tripartite structure (e.g., Elaboration\u0026thinsp;+\u0026thinsp;Active\u0026thinsp;+\u0026thinsp;ISP \u0026rarr; Orientation\u0026thinsp;+\u0026thinsp;Active\u0026thinsp;+\u0026thinsp;ISP), or to transitions involving single or combined metacognitive moves with or without co-regulatory or social emotional interactions (e.g. Orientation\u0026thinsp;+\u0026thinsp;Confirm\u0026thinsp;+\u0026thinsp;ISP to Elaboration\u0026thinsp;+\u0026thinsp;Active, Planning\u0026thinsp;+\u0026thinsp;Active\u0026thinsp;+\u0026thinsp;ISP to Planning\u0026thinsp;+\u0026thinsp;Elaboration\u0026thinsp;+\u0026thinsp;ISP).\u003c/p\u003e \u003cp\u003eTutor contributions primarily supported student metacognition (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Nearly all the tutors\u0026rsquo; questions, statements and task-oriented talk lead to different types of students\u0026rsquo; metacognitive activities. Verification statements were frequently followed by executing actions or elaboration. Requests for an action plan typically elicited orientation and elaboration moves. Similarly, questions seeking explanations triggered elaboration, monitoring, and planning. Finally, when participants asked for additional details in the case, responses were most to evaluate. Tutor task-oriented talk most often shifted toward orientation, elaboration, and planning. When tutors required clarification, this was typically followed by either planning with elaboration or elaboration alone. While many tutor moves prompted metacognitive responses, only clarification from tutors triggered metacognitive responses coupled with social-emotional components (e.g. Clarification to Evaluation\u0026thinsp;+\u0026thinsp;ISP). This may suggest that clarifications can not only help build cognition, they also shape the group\u0026rsquo;s relational climate.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWe examined student and tutor discourse moves in four PBL tutorials using an integrated coding framework that captured metacognitive activity, co-regulation, socio-emotional interaction, and tutor interventions at the level of turn-by-turn interaction. Our findings highlight the tightly interwoven nature of these processes in authentic medical PBL and extend existing theory in several ways.\u003c/p\u003e \u003cp\u003eFirst, elaboration emerged as the dominant metacognitive activity, followed by orientation, evaluation, and planning, underscoring the centrality of elaborative talk in clinical reasoning and collaborative knowledge building. This pattern is highly consistent with core PBL theory and empirical work showing that knowledge construction in tutorial groups is driven by the activation of prior knowledge and its elaboration through explanation, hypothesis generation, and integration of evidence (Mercer, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Schmidt et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; van Boxtel et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Our data add nuance by showing how these elaborative moves are not only frequent but also highly connected in the discourse: lag-sequential patterns indicated that elaboration tended to trigger further questioning, clarification, and uptake, rather than remaining monologic. In line with cognitive and sociocultural accounts of PBL, elaboration appears to function as a hub that makes reasoning explicit, collectively inspectable, and open to refinement (Hmelo-Silver, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Rotgans \u0026amp; Schmidt, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Schmidt, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). This reinforces the idea that PBL is most productive when students are encouraged not simply to \u0026ldquo;share ideas\u0026rdquo; but to articulate and interrogate their reasoning in ways that invite transactive responses.\u003c/p\u003e \u003cp\u003eSecond, within co-regulation, Active and Confirm moves were most frequent, whereas Inhibit and Change moves were rare. This distribution suggests that novice medical students in our context primarily engaged in supportive, forward-moving regulation, prompting participation, acknowledging contributions, and \u0026ldquo;staying with\u0026rdquo; the current line of reasoning, rather than overtly corrective or re-directive regulation. This pattern is aligned with prior work on co-regulation and socially shared regulation of learning, which emphasizes that prompts, confirmations, and uptake are foundational for sustaining joint attention, aligning goals, and coordinating strategy use in collaborative tasks (Hadwin et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; J\u0026auml;rvel\u0026auml; \u0026amp; Hadwin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Volet et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). At the same time, the scarcity of explicit inhibitory or change-oriented moves suggests that groups may have been less inclined to challenge peers\u0026rsquo; ideas, abandon unproductive lines of reasoning, or explicitly renegotiate their plans. This may reflect early-stage group development, novice status, and cultural preferences for maintaining harmony and face in East Asian settings, where direct confrontation and overt control are often avoided (Frambach et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). It may also indicate that more directive forms of regulation were \u0026ldquo;outsourced\u0026rdquo; to the tutor, with peers enacting regulation in relatively subtle and implicit ways. Our sequential analyses support this interpretation: we found that co-regulatory Active and Confirm moves often followed tutor prompts or student elaborations, stabilizing ongoing inquiry rather than redirecting it.\u003c/p\u003e \u003cp\u003eThird, among socio-emotional interactions, ISP was most prevalent, whereas ASP and CSP forms were less frequent, and no negative socio-emotional moves were observed. This profile suggests that task-focused social engagement (i.e., turn-taking, responsiveness, and acknowledgement) predominated over expressions of emotion or explicit identity-building. In the CoI framework, interactive social presence supports cognitive presence by maintaining flow and reciprocity in discourse, which in turn sustains inquiry (Garrison et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Richardson et al., 2017; Rourke et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The absence of negative socio-emotional moves could be interpreted as evidence of psychological safety and supportive norms (Edmondson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), but it may also reflect situational and cultural moderation (first tutorials, novice groups, tutor presence, and local communication norms). Rather than indicating the absence of tension, the discourse may reflect culturally shaped ways of managing disagreement implicitly, for example, by redirecting the topic or reformulating contributions without direct rejection. Notably, the most frequent co-occurrences involved Elaboration, Active, and ISP, indicating that cognitive elaboration was often reinforced through co-regulatory prompts and interactive social engagement. These elements do not operate in isolation: metacognition, co-regulation, and socio-emotional interaction mutually scaffold one another as collaborative problem solving unfolds (Bakhtiar et al, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Isoh\u0026auml;t\u0026auml;l\u0026auml; et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; J\u0026auml;rvenoja \u0026amp; J\u0026auml;rvel\u0026auml;, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Our lag-sequential patterns showed that elaboration commonly prompted further questioning and uptake, while ISP supported sustained transactivity, aligning with prior findings that co-regulation often begins with questions or explanations and is stabilized by shared positive engagement (Volet et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Weinberger \u0026amp; Fischer, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTutor discourse moves were dominated by facilitative prompts and scaffolds, echoing established PBL facilitation principles that emphasize guiding inquiry without providing answers and cultivating productive discourse norms (Dolmans et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Hmelo-Silver \u0026amp; Barrows, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Schmidt \u0026amp; Moust, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Our analyses indicate that tutor interventions functioned as key catalysts that activated both cognitive and socio-emotional dimensions of group work. Facilitative questioning, probing, and reframing were frequently followed by students\u0026rsquo; metacognitive reasoning in combination with interactive social presence, suggesting that tutors effectively invited students to externalize their thinking while maintaining a climate of engagement. In this way, tutors appeared to shape both the epistemic quality of discourse and the climate of participation, helping students to evaluate competing explanations, tolerate uncertainty, and take intellectual risks (Edmondson, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Yew \u0026amp; Schmidt, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Of particular interest, we observed sequences in which socio-emotional engagement (such as ISP) preceded and appeared to open a pathway into metacognitive reasoning, indicating that \u0026ldquo;light\u0026rdquo; social moves can serve as a bridge into deeper inquiry. This extends prior work on emotion and motivation regulation in collaborative learning (D\u0026rsquo;Mello \u0026amp; Graesser, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Isoh\u0026auml;t\u0026auml;l\u0026auml; et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pekrun et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) by demonstrating how social presence, co-regulation, and metacognition interlace in the moment-to-moment unfolding of PBL talk.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePractical implications\u003c/h2\u003e \u003cp\u003eTaken together, our findings reinforce socio-cognitive and socio-emotional perspectives of collaborative learning by showing that metacognition, co-regulation, and social presence are interdependent rather than discrete processes (J\u0026auml;rvel\u0026auml; \u0026amp; Hadwin, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Volet et al., 2012). Group learning in PBL emerges as a dynamic integration of reasoning, regulation, and affect, such that shifts in one dimension (for example, a tutor prompt or a student\u0026rsquo;s interactive response) can cascade into others (for example, elaboration, evaluation, or planning). Practically, this supports calls to design and facilitate PBL in ways that deliberately intertwine cognitive scaffolds with socio-emotional supports rather than treating them as separate concerns (Dolmans et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hmelo-Silver \u0026amp; Barrows, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor tutor development, our results point to the value of training that explicitly targets both metacognitive and socio-emotional facilitation. On the cognitive side, tutors can be supported to use questions that prompt articulation of hypotheses, request justification and evidence, and encourage comparison of alternative explanations, thereby increasing elaboration and evaluation. On the socio-emotional side, tutors benefit from strategies for building psychological safety, such as normalizing uncertainty, acknowledging effort, and modeling respectful challenge, so that students feel able to voice tentative ideas and to disagree productively. Our sequential findings suggest that even brief tutor moves can set in motion chains of elaboration and responsive engagement, underscoring the leverage of well-timed interventions.\u003c/p\u003e \u003cp\u003eAt the group level, our results suggest the need to move beyond a default focus on confirmation toward more adaptive regulatory patterns that include constructive redirection and change when needed. Groups may benefit from explicit norms and scripts that frame disagreement as an epistemic resource rather than a relational threat, such as ground rules for exploratory talk, structured turn-taking, and guidelines for constructive controversy (Johnson \u0026amp; Johnson, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Weinberger \u0026amp; Fischer, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Co-regulation can also be strengthened by integrating structured prompts for shared monitoring and planning\u0026mdash;for example, brief \u0026ldquo;plan\u0026ndash;monitor\u0026ndash;evaluate\u0026rdquo; check-ins, rotating roles for summarizing and questioning, or visual tools that make learning issues and hypotheses visible to all (Kollar et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Panadero \u0026amp; J\u0026auml;rvel\u0026auml;, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). These supports may help novice groups move from predominantly supportive confirmation toward more flexible, strategy-oriented regulation.\u003c/p\u003e \u003cp\u003eGiven that our study took place in a Hong Kong medical school, the findings also underscore the importance of culturally responsive facilitation. In contexts where maintaining harmony and respecting hierarchy are highly valued, tutors may need to scaffold forms of challenge and critique that feel legitimate and safe to students, such as inviting alternative explanations, asking for \u0026ldquo;other possibilities,\u0026rdquo; or depersonalizing critique by focusing on ideas rather than individuals (Frambach et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Attention to language (for example, softening markers, inclusive pronouns) and to non-verbal cues may further support the emergence of shared regulation without undermining cultural expectations of respect.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStrengths, limitations, and future directions\u003c/h2\u003e \u003cp\u003eA key strength of this study is its micro-analytic approach to audiovisual data, which allowed us to capture the co-occurrence and sequencing of metacognitive, regulatory, and socio-emotional discourse moves in authentic medical PBL tutorials. By integrating multiple analytic frameworks and adding lag-sequential analysis to fine-grained coding, we were able to show not only which moves occurred, but how they triggered, stabilized, or redirected one another over time. This advance understanding of how knowledge construction is organized and regulated in practice and illuminates the dual cognitive\u0026ndash;affective pathways through which tutors and students shape collaboration.\u003c/p\u003e \u003cp\u003eAt the same time, several limitations warrant caution. Our sample was small (four groups) and focused on the first 90-minute tutorial session for a single clinical case (OSA) with first-year students in one institution. This constrains the generalizability of our findings and likely narrows the range of regulatory and socio-emotional phenomena observed. For example, more complex or conflictual dynamics may emerge in later sessions, with more advanced students, or when groups face greater epistemic uncertainty. The absence of negative socio-emotional moves in our data may reflect situational factors such as novice status, tutor presence, and social desirability rather than their true absence. In addition, although lag-sequential analysis provided valuable temporal insight, it captures primarily short-range dependencies and does not fully model longer trajectories of regulation across sessions. Finally, we focused on discourse processes without directly linking them to individual or group learning outcomes, such as diagnostic accuracy, conceptual understanding, or exam performance.\u003c/p\u003e \u003cp\u003eIn future work, we recommend extending this line of research in several directions. Longitudinal designs that follow the same PBL groups across multiple cases and semesters would allow us to trace how patterns of metacognition, co-regulation, and social presence evolve as groups mature and as students gain clinical experience. Linking discourse sequences to independent measures of learning and performance would help identify which combinations and trajectories of discourse moves are most productive. Comparative studies across institutions and cultures could clarify how local norms shape the balance between confirmation and challenge, as well as the expression of socio-emotional support and conflict. Methodologically, integrating process data from additional sources, such as physiological or digital trace data, self- and peer-reports of regulation, and post-session reflections, could enrich our understanding of how observed discourse aligns with unobservable cognitive and emotional processes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that metacognition, co-regulation, and social presence form a tightly integrated system in PBL tutorials, with elaboration-centered discourse, supportive peer regulation, and interactive engagement driving collaborative reasoning. Students predominantly activated and confirmed ideas within a psychologically safe climate, while tutors strategically prompted deeper inquiry through questioning rather than direct instruction. For practice, these findings advocate for tutor development emphasizing metacognitive scaffolding alongside socio-emotional attunement, particularly in cultural contexts prioritizing relational harmony. Structured group protocols, such as rotating roles for critique, visual mapping of hypotheses, and norms reframing uncertainty as productive, can help balance confirmatory and exploratory talk. By intentionally designing PBL to synergize cognitive, regulatory, and relational processes, medical educators can transform tutorials into dynamic spaces where engagement fuels both conceptual growth and the collaborative dispositions essential for clinical practice.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Competing Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003e This work was supported by the Hong Kong University Grants Committee (UGC) under the General Research Fund (GRF) [Project Number:17119422].\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eQ.H., B.Z., and F.A.G. conceived and designed the study. Q.H., Y.Y., and F.A.G. were responsible for data collection and analysis. Q.H. and Y.Y. prepared the original manuscript draft. B.Z. contributed to methodological and analytical validation. G.T. and J.P.Y.C. provided critical resources and educational context. All authors contributed to the interpretation of results, reviewed the manuscript drafts, and approved the final version for submission.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors wish to thank all the PBL tutors and students whose participation made this study possible. We are also grateful to our collogues for their support and assistance throughout the research process.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request. 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Becoming a Self-Regulated Learner: An Overview. \u003cem\u003eTheory into Practice\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(2), 64\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1207/s15430421tip4102_2\u003c/span\u003e\u003cspan address=\"10.1207/s15430421tip4102_2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"Problem-based learning, Medical Education, Discourse Analysis, Metacognition, Co-Regulation, Social Presence","lastPublishedDoi":"10.21203/rs.3.rs-8425435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8425435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eProblem‑based learning (PBL) is intended to foster collaborative knowledge construction, yet the moment‑to‑moment interplay of metacognition, co‑regulation, and socio‑emotional interaction in PBL tutorials remains underexplored. This mixed‑methods study examined discourse processes in four first‑year medical PBL groups discussing an obstructive sleep apnoea case. Ninety‑minute tutorials were video‑recorded, transcribed, segmented into meaning units, and coded for metacognitive activity, co‑regulation, social‑emotional interaction, and tutor moves. Quantitative analyses included code frequencies, lag sequential analysis of discourse transitions, and Epistemic Network Analysis to model co‑occurrence patterns.\u003c/p\u003e \u003cp\u003eAcross 649 coded segments (544 student, 105 tutor), elaboration was the dominant metacognitive activity, with orientation, evaluation, and planning also frequent. Co‑regulatory moves were mainly activating and confirming peers, while slowing and change‑oriented regulation were rare. Interactive social presence was the prevalent socio‑emotional form; negative socio‑emotional moves were not observed. The epistemic network revealed a densely connected core linking elaboration, planning, orientation, evaluation, activating, confirming, and interactive social presence, indicating that reasoning, co‑regulation, and social engagement were tightly integrated. Lag sequential analysis showed that elaborative and tutor‑initiated prompts often triggered extended metacognitive sequences, whereas tutor clarifications were uniquely associated with metacognitive moves that also expressed social support.\u003c/p\u003e \u003cp\u003eThese findings portray PBL as a socio‑cognitively integrated activity and highlight elaboration‑centred discourse, supportive co‑regulation, and facilitative tutor questioning as key levers for fostering productive, psychologically safe collaboration in medical education.\u003c/p\u003e","manuscriptTitle":"Beyond Elaboration: How Metacognition, Co-regulation, and Social Presence Converge to Drive Engagement in Problem-Based Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 16:37:52","doi":"10.21203/rs.3.rs-8425435/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":"de46aabe-9cdf-49f0-ba04-9c9984690f12","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-18T13:39:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 16:37:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8425435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8425435","identity":"rs-8425435","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}