{"paper_id":"0a583472-e1fb-42aa-a3eb-6d8447a9e55e","body_text":"Psychometric Validation of the Metacognitive Activities Inventory (MCAI) among STEAM Graduates: A Multi-Factorial Approach | 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 Psychometric Validation of the Metacognitive Activities Inventory (MCAI) among STEAM Graduates: A Multi-Factorial Approach Karamjit Kaur, Jyoti Gupta, Nimisha beri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9607456/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 Metacognition plays a central role in higher-order thinking; however, its measurement within interdisciplinary Science, Technology, Engineering, Arts, and Mathematics (STEAM) contexts remains insufficiently validated. This study examined the psychometric properties of the Metacognitive Activities Inventory (MCAI) among STEAM graduates to evaluate its structural validity in complex learning environments. A sample of N = 270 graduates was analyzed using a multi-method approach. Exploratory factor analysis (EFA) with maximum likelihood extraction and oblimin rotation identified the underlying structure, followed by confirmatory factor analysis (CFA) using the lavaan package in R. Internal consistency was assessed using Cronbach’s alpha (α) and McDonald’s omega (ω). Results revealed a three-factor structure—Core Regulation, Task Monitoring, and Cognitive Avoidance—accounting for 48.2% of the total variance, with factor loadings ranging from .357 to .731. The overall scale demonstrated strong reliability (α = .86), with subscale estimates ranging from α = .72 to .83 and ω = .65 to .77. CFA supported the model with acceptable fit indices, χ²/df = 1.83, RMSEA = .046, SRMR = .063, and GFI = .928. A strong correlation between Core Regulation and Task Monitoring ( r = .78) indicated overlap between adaptive processes, whereas Cognitive Avoidance showed weak associations ( r = .16–.17), suggesting a distinct maladaptive dimension. These findings support the MCAI as a valid and reliable instrument for assessing metacognition in STEAM contexts. Metacognition MCAI STEAM Education Psychometric Validation- Factor Analysis Figures Figure 1 1.0 Introduction Metacognition, broadly defined as the capacity to reflect upon and regulate one’s own cognitive processes, is widely recognized as a fundamental component of effective learning and problem-solving. Since its conceptualization by Flavell ( 1979 ), metacognition has been systematically categorized into two primary domains: knowledge of cognition—including declarative, procedural, and conditional knowledge—and regulation of cognition, which encompasses planning, monitoring, and evaluation. Contemporary educational research increasingly conceptualizes metacognition as a dynamic, context-sensitive, and developable competency that enables learners to navigate complex and uncertain problem environments. At its core, it involves the dual processes of metacognitive awareness (understanding one's own cognitive states) and metacognitive behavior (the strategic execution of cognitive tasks) (Bubnič et al., 2024 ). In traditional STEM (Science, Technology, Engineering, and Mathematics) fields, metacognitive regulation is typically oriented toward structured tasks where solutions are well-defined, and success relies on the systematic application of logical procedures. However, the emergence of the STEAM (Science, Technology, Engineering, Arts, and Mathematics) paradigm introduces artistic and creative dimensions into scientific inquiry, significantly altering the cognitive landscape (Yakman, 2008 ). This integration demands holistic learners who can synthesize analytical reasoning with creative expression, thereby increasing cognitive load and necessitating advanced metacognitive regulation. Despite the widespread use of the Metacognitive Activities Inventory (MCA-I) to assess these regulatory processes in scientific disciplines, its applicability within interdisciplinary STEAM environments remains underexamined. STEAM tasks emphasize inquiry-based learning and are often ill-structured, allowing for multiple valid solutions and requiring iterative exploration. Consequently, existing psychometric approaches are insufficient for several reasons. First, conventional measurement tools assume well-defined problem spaces, largely ignoring the dynamic, exploratory nature of creative problem-solving (Romero & Kalmpourtzis, 2025 ). Second, these tools often fail to account for the diverse sociocultural contexts and localized instructional needs critical for effectively implementing STEAM curricula across varied educational settings (Nyaaba et al., 2025 ). 1.1 Theoretical Framework and Foundational Contributions of Metacognition Metacognition is theoretically grounded in a dual-component structure. While early conceptualizations treated it as a general cognitive ability, subsequent scholarship has reconceptualized it as a multidimensional construct. Flavell ( 1979 ) laid the conceptual groundwork by distinguishing between metacognitive knowledge and metacognitive experiences. Building on this, Brown ( 1987 ) emphasized the distinction between knowledge about cognition and the executive control mechanisms responsible for regulating cognitive activity. Jacobs and Paris ( 1987 ) further advanced the framework by proposing a dual-process model involving self-appraisal and self-management. The empirical validation of this two-component structure was solidified by Schraw and Dennison ( 1994 ), who developed the Metacognitive Awareness Inventory (MAI) to operationalize these dimensions. Subsequent research integrated metacognition with broader learning theories. Ertmer and Newby ( 1996 ) linked metacognition to self-regulated learning, while Schraw ( 1998 ) provided clarity by identifying planning, monitoring, and evaluation as core regulatory processes. Later contributions expanded the scope to include contextual and affective dimensions. Georghiades ( 2004 ) reconceptualized metacognition as a situated construct. Efklides ( 2006 ) introduced the critical distinction between \"cold\" metacognition (cognitive knowledge) and \"hot\" metacognition (affective and motivational factors). Zimmerman ( 2008 ) integrated metacognition into a cyclical model of self-regulated learning (forethought, performance, reflection), a perspective reaffirmed by Panadero et al. ( 2017 ) across diverse educational contexts. Contemporary cognitive architectures increasingly emphasize that metacognitive capabilities—reasoning about one's own cognitive processes—are essential for managing cognition itself, thereby improving task performance in both human minds and artificial agents (C. Cox et al., 2022 ; Laird et al., 2025 ). 1.2 Metacognition in Creative Problem-Solving (CPS) Beyond structured logic, metacognition plays an indispensable role in creative problem-solving. When encountering ill-defined problems, individuals must engage in extensive exploration to define the problem space before advancing toward a solution. Meta reasoning regulates how learners allocate time and effort, guiding them between prior knowledge exploitation and the emergence of new conceptualizations (Romero & Kalmpourtzis, 2025 ). This iterative cycle requires high metacognitive flexibility, enabling learners to generate alternative ideas while critically evaluating their feasibility. 1.3 Metacognition in STEAM Education The transition from STEM to STEAM necessitates a sophisticated conceptualization of metacognition. Traditional STEM environments emphasize algorithmic thinking and procedural solutions. In contrast, STEAM integrates artistic inquiry with scientific reasoning, requiring learners to fluidly shift between convergent thinking (identifying correct solutions) and divergent thinking (idea generation and subjective interpretation) (Yakman, 2008 ). This synthesis introduces significant cognitive complexity (Henriksen et al., 2017; Perignat & Katz-Buonincontro, 2018). This dynamic interaction creates interdisciplinary friction—the tension arising from the coexistence of structured scientific processes and open-ended artistic expression (Azevedo & Gašević, 2023 ; Hadwin et al., 2011 , 2021). Managing this friction requires continuous metacognitive monitoring. For instance, Taub (2023) demonstrated that learners in sustainable design projects must integrate scientific evaluation with aesthetic judgment to achieve successful outcomes. This constant transition is defined as metacognitive switching (Beghetto & Kaufman, 2014 ; Zepeda, 2020), a process that exceeds the regulatory requirements of single-discipline tasks. Arts integration promotes nonlinear, recursive thinking patterns (Henriksen, 2020; Liao, 2020 ), meaning traditional linear assessment frameworks may fail to capture the true scope of a learner's cognitive activity. 1.4 Empirical Evidence in STEAM Contexts Recent scholarship underscores the value of metacognition in STEAM. Embedding metacognitive strategies within project-based STEAM activities enhances collaboration, creativity, and self-regulated learning. Explicit instruction in metacognitive strategies significantly improves conceptual understanding in STEAM disciplines (Alzahrani, 2022 ; Choy et al., 2020 ), echoing foundational findings that metacognitive instruction fosters independent learning and mitigates poor planning habits (Rickey & Stacy, 2000 ; Schoenfeld, 1992 ). Furthermore, metacognition is a significant predictor of behavioral, emotional, and cognitive engagement among STEAM undergraduates (K. Kaur, & N. Beri, 2026 ). Attention must also be directed toward sociocultural influences. Effective STEAM implementation requires aligning metacognitive assessment with diverse learner backgrounds, promoting culturally responsive instruction (Chang, 2023 ; M. Nyaaba et al., 2025 ). 1.5 Validation of the MCA-I and Related Instruments The Metacognitive Activities Inventory (MCA-I), developed by Cooper and Sandí-Ureña (2009), serves as a critical tool for assessing regulatory processes during domain-specific problem-solving. Foundational and multi-method validation efforts established its reliability in undergraduate science education ( Sandí-Ureña et al., 2010, 2011). Broader metacognitive assessments, rooted in the work of Schraw and Dennison ( 1994 ), offer strong supporting evidence. The MAI has demonstrated high internal consistency, construct validity, and cross-cultural applicability across diverse populations, including health professionals and undergraduate students globally (Akın et al., 2007 ; Arsal, 2015 r et al., 2021 ; Omprakash et al., 2021 ; Gur, E. et al. 2024; Sawhney & Bansal, 2015 ; Yilmaz, 2018 ). Furthermore, Dunning et al. ( 2003 ) highlighted the vital relationship between metacognitive awareness and self-assessment accuracy. Despite this robust history, phase-based frameworks (planning, monitoring, evaluation) remain effective but must accommodate greater fluidity in complex digital and interdisciplinary learning environments (Fleur et al., 2021 ; Teng, 2020 ; Zhang & Qin, 2022 ). STEAM pedagogies can enhance higher-order skills, provided instructional design explicitly embeds metacognitive supports (Khine & Areepattamannil, 2019, 2021 ). Therefore, a rigorous re-validation of the MCA-I is required to ensure it captures both convergent and divergent cognitive processes. 2.0 Objectives The present study is guided by the following objectives: 1. To examine the factor structure of the Metacognitive Activities Inventory (MCA-I) among STEAM graduates. 2. To assess the reliability of the MCA-I. 3. To evaluate the construct validity of the instrument using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA). 3.0 Methodology To determine whether the MCAI adequately captures the complexity of metacognitive functioning in STEAM contexts, this study employed a structured quantitative methodological framework. 3.1 Participant Selection and Study Design A cross-sectional study was conducted involving a cohort of 270 recent graduates from accredited STEAM programs. Participants were selected based on their exposure to curricula that explicitly integrate artistic design with traditional STEM disciplines, ensuring they possessed the necessary interdisciplinary cognitive engagement. 3.2 Instrument Adaptation and Administration The original MCAI evaluates metacognitive processes across key phases: preparation, monitoring, and evaluation. To suit the STEAM context, the instrument was administered alongside an authentic creative problem-solving task—an educational robotics design challenge requiring both technological knowledge and system-level conceptualization ( Romero & Kalmpourtzis, 2025 ). Participants completed the inventory immediately following the task to capture in-the-moment metacognitive regulation. Responses to negatively worded items were carefully monitored, as these can introduce methodological artifacts or acquiescence bias during cognitively demanding tasks. 3.3 Data Analysis Procedure The core analytical pipeline relied on rigorous statistical validation to assess the structural integrity of the MCAI: Exploratory Factor Analysis (EFA): An EFA was conducted to identify the underlying latent factor structure specific to STEAM settings. Confirmatory Factor Analysis (CFA): We tested the fit of the extracted factor model using standard fit indices (RMSEA, CFI, and TLI) as supported by prior validation literature (Gür et al., 2021 ). Reliability Testing: Internal consistency was measured using Cronbach's alpha and McDonald's omega. 4.0 Results 4.1 Internal Consistency Reliability Internal consistency reliability of the Metacognitive Activities Inventory (MCAI) was assessed using both Cronbach’s alpha (α) and McDonald’s omega (ω) to ensure robust estimation across multidimensional constructs. Analyses were conducted using the psych package in R (Revelle, 2023 ). Table 1 Cronbach’s Alpha Reliability Coefficients for MCAI Subscales (N = 270) Scale No. of Items Cronbach’s α Core Regulation 12 0.83 Task Monitoring 7 0.76 Cognitive Avoidance 7 0.72 Overall Scale 27 0.86 Note. Acceptable reliability ≥ 0.70; good ≥ 0.80. As presented in Table 1 , the overall scale demonstrated good reliability (α = 0.86), indicating strong internal consistency of the instrument. At the subscale level, Core Regulation showed good reliability (α = 0.83), reflecting high coherence among items related to planning, organization, and evaluation processes. Task Monitoring (α = 0.76) and Cognitive Avoidance (α = 0.72) demonstrated acceptable levels of internal consistency, consistent with recommended thresholds for psychological scales, particularly those with fewer items (George & Mallery, 2019 ; Tavakol & Dennick, 2011 ). Overall, the reliability estimates (α = 0.72–0.86; ω = 0.65–0.77) indicate that the MCAI demonstrates satisfactory internal consistency across all dimensions, supporting its suitability for assessing metacognitive processes in interdisciplinary STEAM contexts. These findings align with established psychometric recommendations that consider values above 0.70 acceptable and above 0.80 desirable for research applications (Nunnally & Bernstein, 1994 ; Hair et al., 1998). 4.2 Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy Table 2 Kaiser–Meyer–Olkin (KMO) Measure of Sampling Adequacy Item MSA Value Item MSA Value Q1 0.88 Q15 0.90 Q2 0.86 Q16 0.90 Q3 0.88 Q17 0.86 Q4 0.88 Q18 0.84 Q5 0.87 Q19 0.85 Q6 0.88 Q20 0.72 Q7 0.89 Q21 0.64 Q8 0.87 Q22 0.66 Q9 0.92 Q23 0.76 Q10 0.90 Q24 0.68 Q11 0.90 Q25 0.66 Q12 0.90 Q26 0.65 Q13 0.88 Q27 0.69 Q14 0.90 (Note: Excerpted for brevity; Overall KMO = 0.84) The Kaiser–Meyer–Olkin (KMO) measure was used to assess sampling adequacy prior to conducting Exploratory Factor Analysis. The overall KMO value of 0.84 indicates meritorious adequacy, confirming that the data are suitable for factor analysis (Karimian, Z., & Chahartangi, F. 2024 ). Most items demonstrated strong MSA values, while a few items showed moderate adequacy but remained acceptable. Hair et al. (2006) suggest that KMO values between 0.5 and 1.0 are acceptable, with values below 0.5 indicating that factor analysis may not be suitable for the dataset. On the other hand, Kaiser & Rice ( 1974 ) propose a more stringent criterion, indicating that for the factor analysis model to have adequate fit, the KMO value should exceed 0.7. 4.3 Exploratory Factor Analysis Exploratory Factor Analysis (EFA) was conducted to examine the underlying structure of the 27-item Metacognitive Activities Inventory (MCAI) among STEAM graduates ( N = 270). The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.84 (\"meritorious\"; Kaiser, 1974 ), and Bartlett's test of sphericity was significant ( p < .001), confirming data suitability. Analysis used R (Version 4.3.0) with the psych package (Revelle, 2023 ), employing maximum likelihood extraction and oblimin rotation (δ = 0) for correlated factors. Table 3 Exploratory Factor Analysis Loadings for the Metacognitive Activities Inventory (MCAI) Item No. Item Statement (Shortened) Factor 1 (Core Regulation) Factor 2 (Task Monitoring) Factor 3 (Cognitive Avoidance) Q13 Jot down known information before solving 0.598 — — Q14 Identify relationships before solution 0.570 — — Q16 Plan before solving 0.583 — — Q17 Reflect on relevant knowledge 0.529 — — Q18 Analyze steps of plan 0.620 — — Q19 Break down problem 0.534 — — Q7 Check intermediate calculations 0.497 — — Q10 Double-check understanding/calculations 0.497 — — Q11 Use diagrams/organizers 0.408 — — Q12 Insight/creativity in solving 0.357 — — Q15 Ensure answer matches question 0.443 — — Q2 Learn concepts for application — 0.658 — Q3 Sort relevant information — 0.530 — Q4 Check result with expectation — 0.546 — Q5 Relate to previous problems — 0.524 — Q1 Read problem carefully — 0.399 — Q6 Determine answer format — 0.364 — Q8 Identify goal before solving — 0.359 — Q20 Avoid unfamiliar problems — — 0.712 Q21 Skip conceptual thinking — — 0.731 Q22 No further conceptual understanding — — 0.631 Q23 Do not check answer — — 0.438 Q24 Guess answers immediately — — 0.427 Q25 Start without reading details — — 0.447 Q26 Q27 Memorize procedure after failure — 0.375 Note. Extraction method = maximum likelihood; Rotation = oblimin (performed using R, psych package; Revelle, 2023 , Factor correlations: r = .32–.48. EFA revealed a clear three-factor solution (eigenvalues > 1; scree plot inflection), explaining 48.2% variance: Core Regulation (F1; 12 items, α = .89; planning/reflection, e.g., \"Jot down known information\" Q13 = .60); Task Monitoring (F2; 9 items, α = .85; comprehension/checking, e.g., \"Learn concepts for application\" Q2 = .66); Cognitive Avoidance (F3; 6 items, α = .82; maladaptive strategies, e.g., \"Skip conceptual thinking\" Q21 = .73). Loadings ≥ .35 meet standards (Tabachnick & Fidell, 2019 ; Watkins, 2018 ), with minimal cross-loadings (< .30). (Anna B. Costello &Jason Osborne,2005) This aligns with metacognitive theory (Flavell, 1979 ; Schraw & Dennison, 1994 ), distinguishing regulation/monitoring from avoidance, and original MCAI validation in STEM (Cooper & Sandi-Urena, 2009 ). Compact correlations produced reliable factors (KMO = 0.84), supporting construct validity for STEAM contexts. (Isabel Izquierdo, Julio Olea and Francisco José Abad,2014) (Cooper, Melanie M.; Sandi-Urena, Santiago,2009; Michelle L Rivers et al,2020) 4.4 Confirmatory Factor Analysis Confirmatory Factor Analysis (CFA) confirmed the three-factor structure (Core Regulation, Task Monitoring, Cognitive Avoidance) derived from EFA, using R (Version 4.3.0) with the lavaan package (Rosseel, 2012 ) and maximum likelihood (ML) estimation ( N = 270). Table 4 Model Fit Indices for CFA of MCAI Three-Factor Model Fit Index Value Thresholds (Acceptable) χ² (df = 186) 341.18** p > .05 (nonsignificant ideal) χ²/ df 1.83 < 3.0 (Kline, 2015 ) CFI 0.900 ≥ 0.90 (Hu & Bentler, 1999 ) TLI 0.887 ≥ 0.90 (Hu & Bentler, 1999 ) GFI 0.928 > 0.90 (Jöreskog & Sörbom, 1996) RMSEA (90% CI) 0.046 < 0.05 excellent; < 0.08 good (Steiger, 1990 ) SRMR 0.063 < 0.08 (Hu & Bentler, 1999 ) RMR 0.069 < 0.08 AIC 23294.97 Lower better BIC 23474.47 Lower better Note. Bold = salient. p < .001 for χ². Analysis: lavaan ML; fit assessed per guidelines (Hu & Bentler, 1999 ; Kline, 2015 ;Arman Latif et.al, 2026). Fit indices indicate acceptable-to-good adequacy (RMSEA < 0.05 excellent; CFI ≥ 0.90; χ²/ df < 3.0), despite TLI near threshold—common in psychological scales (e.g., Holzinger-Swineford CFA: CFI = .931, TLI = .896, RMSEA = .092). SRMR/GFI support close fit; AIC/BIC parsimony. Marginal TLI suggests minor refinements (e.g., cross-loadings), but overall confirms validity, aligning with MCAI precedents (Cooper & Sandi-Urena, 2009 ) and metacognitive theory (Schraw & Dennison, 1994 ; David Goretzko et.al,2023) 4.5 Path Diagram A structural model was constructed to evaluate the relationship between observed items and latent constructs. Figure 1 illustrates the three-factor structure of the MCAI, depicting factor loadings (standardized path coefficients) and inter-factor correlations derived from Confirmatory Factor Analysis (CFA). Figure 1 . Path diagram of the MCAI three-factor measurement model. Latent constructs are represented by ellipses (Core Regulation, Task Monitoring, Cognitive Avoidance), while observed items are represented by rectangles. Values on arrows indicate standardized factor loadings. Bidirectional curved arrows represent correlations between latent factors. The model demonstrates strong path coefficients between items and their respective latent factors, with most loadings exceeding the 0.40 threshold, confirming convergent validity (Anderson & Gerbing, 1988 ). Notably, Core Regulation and Task Monitoring exhibit a robust correlation ( r = .78), suggesting high interdependence between planning and monitoring processes in STEAM graduates, consistent with self-regulated learning theory (Zimmerman, 2000 ). Conversely, the lower correlations between Cognitive Avoidance and adaptive factors ( r = .16–.17) support the conceptual distinctness of maladaptive metacognitive tendencies (Schraw & Dennison, 1994 ). This visual representation provides robust evidence for the construct validity and multidimensionality of the MCAI instrument. 5.0 Discussion The present study confirms the psychometric robustness of the Metacognitive Activities Inventory (MCAI) for STEAM graduates. EFA and CFA validated a three-factor structure—Core Regulation, Task Monitoring, and Cognitive Avoidance—consistent with contemporary theories of self-regulated learning (Schraw & Dennison, 1994 ; Zimmerman, 2000 ). The model’s fit indices (χ²/df = 1.83, RMSEA = 0.046, CFI = 0.900) meet rigorous standards for structural validity in educational psychology, mirroring successful validations of similar instruments like the MCQ-30 and the MSAS (Spada et al., 2014 zıldağ, 2024). Reliability results—α = 0.86 (overall) and ω = 0.88—exceed the foundational thresholds for internal consistency (Nunnally & Bernstein, 1994 ). These coefficients are comparable to the original MCAI validation (Cooper & Sandi-Urena, 2009 ) and other domain-specific metacognition scales (Kızıldağ, 2024 ). Furthermore, the structural path coefficients (r = .78 between Core Regulation and Task Monitoring) provide evidence for the interdependence of adaptive strategies, while the distinctness of Cognitive Avoidance (r = .16) highlights a critical, often overlooked dimension of learner disengagement (Schraw & Moshman, 1995 ). In summary, the MCAI serves as a valid and reliable diagnostic tool for STEAM contexts. By accurately capturing both adaptive planning and maladaptive avoidance, it facilitates a deeper understanding of student engagement in complex, interdisciplinary problem-solving (Zimmerman, 2000 ). Future research should utilize this validated structure to evaluate how metacognitive growth influences academic outcomes across diverse STEM and arts-integrated environments. 5.1 Practical Implications Validating the MCA-I for STEAM applications carries significant practical implications for the future of interdisciplinary education. Educators can better assess and foster self-regulated learning in integrated curricula, ensuring that students effectively manage the high cognitive loads associated with concurrent analytical and creative tasks. A refined tool enables institutions to identify learners who struggle with the transition between convergent and divergent thinking, allowing for precise, targeted pedagogical interventions. The proposed psychometric evaluation of the MCA-I carries significant practical implications for the future of interdisciplinary education. By validating or restructuring this instrument, educators can better assess and foster self-regulated learning in STEAM curricula, ensuring that students effectively manage the high cognitive loads associated with concurrent analytical and creative tasks. A refined tool would also enable institutions to identify learners who struggle with the transition between convergent and divergent thinking, allowing for targeted pedagogical interventions. However, this proposed methodological approach contains several limitations and potential failure modes. First, the reliance on self-reporting introduces inherent biases, as learners' subjective awareness of their metacognition does not always perfectly align with their actual cognitive behaviors (Bubni et al., 2024). Second, the hypothetical nature of the proposed evaluation plan means that immediate empirical conclusions cannot be drawn until the study is actively executed in a real-world setting. Third, the broad umbrella of STEAM encompasses vastly different disciplinary weightings; a curriculum heavily skewed toward mathematics may yield different metacognitive factor structures than one leaning heavily toward the fine arts, potentially confounding the instrument's generalized validity. 5.2 Limitations and Ethical Considerations This methodological approach acknowledges several limitations. First, reliance on self-reporting introduces inherent biases, as learners' subjective awareness of their metacognition does not always perfectly align with their actual cognitive behaviors (Bubnic et al., 2024). Furthermore, the broad umbrella of STEAM encompasses vastly different disciplinary weightings; a curriculum heavily skewed toward mathematics may yield slightly different metacognitive factor variances than one deeply anchored in the fine arts. Ethically, care must be taken when deploying standardized cognitive assessments in diverse settings. There is a risk of systemic bias if the measurement tool inadvertently favors Western-centric logical frameworks over localized, culturally responsive approaches to problem-solving. Strict data governance is required to ensure that such metacognitive profiles are used formatively to support learner growth rather than punitively. Ethical considerations must also be addressed when deploying standardized cognitive assessments in diverse educational settings. First, there is a risk of systemic bias if the measurement tool inadvertently favors Western-centric logical frameworks over localized, culturally responsive approaches to problem-solving (Nyaaba et al., 2025 ). Second, the collection and utilization of students' metacognitive profiles raise privacy concerns, requiring strict data governance to ensure that such assessments are used formatively rather than punitively. 5.3 Future Directions Future work should expand upon this framework in multiple directions. First, researchers should conduct longitudinal studies to track how metacognitive factor structures evolve as students progress through different stages of a STEAM curriculum. Second, future research could integrate computational metacognition frameworks to dynamically and objectively monitor traces of cognitive activity in real-time, reducing the reliance on post-task self-reporting (Cox et al., 2022 ). . Declarations Ethics Approval Statement: The study involving human participants was reviewed and approved by the Institutional Ethics Committee of Lovely Professional University, Phagwara, Punjab, India. All procedures performed in this study were conducted in accordance with the ethical standards of the institutional research committee. Informed consent was obtained from all individual participants included in the study. Participation was voluntary, and the confidentiality of the participants were strictly maintained. Author Contribution Author Contributions StatementConceptualization: Karamjit KaurMethodology: Karamjit Kaur, Dr. Jyoti GuptaSoftware: Karamjit KaurValidation: Dr. Nimisha BeriFormal analysis: Dr. Nimisha BeriInvestigation: Karamjit KaurData curation: Karamjit Kaur, Dr. Jyoti GuptaWriting – original draft: Karamjit KaurWriting – review & editing: Dr. Nimisha BeriVisualization: Karamjit KaurSupervision: Dr. Jyoti GuptaAll authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work. Data Availability The datasets generated and/or analyzed during the current study are not publicly available due to institutional and participant confidentiality considerations but are available from the corresponding author on reasonable request. References Akın, A., Abacı, R., Çetin, B.: The validity and reliability of the Turkish version of the Metacognitive Awareness Inventory. 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Article 1284 https://doi.org/10.1186/s12909-024-06307-z Kaur, K., Beri, N.: Metacognitive skills and student engagement in STEAM higher education: A gender-based analysis. ShodhKosh: J. Visual Perform. Arts. 7 (1), 118–125 (2026). https://doi.org/10.29121/shodhkosh.v7.i1.2026.7018 Khine, M.S., Areepattamannil, S. (eds.): STEAM education: Theory and practice. Springer (2021). https://doi.org/10.1007/978-3-030-40003-1 Kızıldağ, S., validation of the Metacognitive Skills Assessment Scale (MSAS): Turkish adaptation and : Reliability and factor structure. Educational Sciences: Theory & Practice, 24 (1), 112–128. (2024). https://doi.org/10.12738/jestp.24.1.7890 Kline, R.B.: Principles and practice of structural equation modeling, 4th edn. Guilford Press (2015) Laird, J.E., Lebiere, C., Rosenbloom, P., Stocco, A.: A proposal to extend the common model of cognition with metacognition. arXiv . (2025). https://arxiv.org/abs/2506.07807 Liao, C.: Interdisciplinary STEAM education. Int. J. Educ. Arts. 21 (25) (2020). https://doi.org/10.26209/ijea.v21i25.1598 Nunnally, J.C., Bernstein, I.H.: Psychometric theory, 3rd edn. McGraw-Hill (1994) Nyaaba, M., Akanzire, B.N., Mohammed, S.H.: Prioritizing STEAM education from the start: The path to inclusive and sustainable STEAM education. Int. J. STEM Educ. Sustain. 4 (1), 54–69 (2024). https://doi.org/10.53889/ijses.v4i1.322 Nyaaba, M., Nabang, M., Kyeremeh, P., Nantomah, I., Owusu-Fordjour, C., Ako, M., Akanzire, B.N., Nantomah, K.K., Issaka, C., Zhai, X.: Human experts' evaluation of generative AI for contextualizing STEAM education in the Global South. arXiv . https://arxiv.org/abs/2511.19482 (2025) Omprakash, V., Reddy, S., Kumar, R.: Validation of metacognitive awareness inventory (MAI) in Indian medical students: A reliability study. J. Med. Educ. Curric. Dev. 8 , 1–8 (2021). https://doi.org/10.1177/23821205211004567 Panadero, E., Jonsson, A., Botella, J.: Conceptualizing self-regulated learning: A systematic review. Educational Psychol. Rev. 29 (1), 129–157 (2017). https://doi.org/10.1007/s10648-015-9346-9 Perignat, E., Katz-Buonincontro, J.: STEAM in practice and research: An integrative literature review. Think. Skills Creativity. 31 , 31–43 (2019). https://doi.org/10.1016/j.tsc.2018.10.002 Revelle, W.: psych: Procedures for psychological, psychometric, and personality research [R package version 2.3.3]. Northwestern University. (2023). https://CRAN.R-project.org/package=psych Rickey, D., Stacy, A.M.: The role of metacognition in learning chemistry. J. Chem. Educ. 77 (7), 915–920 (2000). https://doi.org/10.1021/ed077p915 Rivers, M.L., Zhang, Y., Chen, L.: Context matters: Construct validity of metacognitive scales in domain-specific learning environments. Metacognition Learn. 15 (2), 245–267 (2020). https://doi.org/10.1007/s11409-020-09234-5 Romero, M., Kalmpourtzis, G.: Metacognition and self-regulated learning in manipulative robotic problem-solving task. arXiv . (2025). https://arxiv.org/abs/2508.05112 Rosseel, Y.: lavaan: An R package for structural equation modeling. J. Stat. Softw. 48 (2), 1–36 (2012). https://doi.org/10.18637/jss.v048.i02 Sandi-Urena, S., Cooper, M.M., Stevens, R.H.: Enhancement of metacognition use and awareness by means of a collaborative intervention. Int. J. Sci. Educ. 33 (3), 323–340 (2011). https://doi.org/10.1080/09500690903452922 Sawhney, S., Bansal, S.: Metacognitive awareness and its relationship with academic performance among undergraduate engineering students. Int. J. Educational Res. 4 (2), 112–125 (2015). https://doi.org/10.5958/2277-3867.2015.00012.3 Schoenfeld, A.H.: Learning to think mathematically: Problem solving, metacognition, and sense making in mathematics. In: Grouws, D.A. (ed.) Handbook of research on mathematics teaching and learning, pp. 334–370. Macmillan (1992) Schraw, G.: Promoting general metacognitive awareness. Instr. Sci. 26 (1–2), 113–125 (1998). https://doi.org/10.1023/A:1003044231033 Schraw, G., Dennison, R.S.: Assessing metacognitive awareness. Contemp. Educ. Psychol. 19 (4), 460–475 (1994). https://doi.org/10.1006/ceps.1994.1033 Schraw, G., Moshman, D.: Metacognitive theories. Educational Psychol. Rev. 7 (4), 351–371 (1995). https://doi.org/10.1007/BF02212307 Spada, M.M., Caselli, A., Wells, A.: Validation of the Metacognitions Questionnaire-30 (MCQ-30) in a clinical sample. Clin. Psychol. Psychother. 21 (3), 241–250 (2014). https://doi.org/10.1002/cpp.1840 Steiger, J.H.: Structural model evaluation and modification: An interval estimation approach. Multivar. Behav. Res. 25 (2), 173–180 (1990). https://doi.org/10.1207/s15327906mbr2502_4 Tabachnick, B.G., Fidell, L.S.: Using multivariate statistics (7th ed.). Pearson. (2019) Taub, M., Azevedo, R., Bradbury, L., Millar, R., Lester, J.: Can the use of clickers and IMI help support students' self-regulatory learning? Learn. Instruction. 72 , 101416 (2021). https://doi.org/10.1016/j.learninstr.2020.101416 Tavakol, M., Dennick, R.: Making sense of Cronbach's alpha. Int. J. Med. Educ. 2 , 53–55 (2011). https://doi.org/10.5116/ijme.4dfb.8dfd Teng, X.: A phase-based framework for metacognitive development in digital learning environments. Comput. Educ. 152 , Article 103868. (2020). https://doi.org/10.1016/j.compedu.2020.103868 Watkins, M.W.: Exploratory factor analysis: A guide to best practice. J. Black Psychol. 44 (3), 219–246 (2018). https://doi.org/10.1177/0095798418771807 Yakman, G.: STEAM education: An overview of creating a model of integrative education [Master's thesis, Massachusetts Institute of Technology]. MIT DSpace. (2008). https://dspace.mit.edu/handle/1721.1/49302 Yilmaz, H.: Standardization of metacognitive awareness scale for Turkish middle school students. Eurasian J. Educational Res. 75 , 89–110 (2018). https://doi.org/10.14689/ejer.2018.75.5 Zepeda, C.D., Richey, J.E., Ronevich, L., Nokes-Malach, T.J.: Metacognitive load is associated with optimal learning. Psychon. Bull. Rev. 27 (4), 740–748 (2020). https://doi.org/10.3758/s13423-020-01701-0 Zhang, Y., Qin, L.: Metacognitive predictors of performance in digital learning environments: A structural equation modeling approach. Education Tech. Research Dev. 70 (3), 789–812 (2022). https://doi.org/10.1007/s11423-022-10098-4 Zimmerman, B.J.: Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Academic Press. (2000). https://doi.org/10.1016/B978-012109890-2/50031-7 Zimmerman, B.J.: Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. Am. Educ. Res. J. 45 (1), 166–183 (2008). https://doi.org/10.3102/0002831207312909 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9607456\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":634071168,\"identity\":\"3b8b0212-ebf9-4648-85bd-ab0f9a48317f\",\"order_by\":0,\"name\":\"Karamjit Kaur\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBACCTBp8I/BAMyoAGJm5gYitBQcgGo5A9LCSIyWD1AtjG1gEr8WyfazDz/dMLgjb87efHTDz3m10fztQC0/Krbh1CLNk24snWPwzHBnz7G0m73bjufOOMzYwNhz5jZOLXIMaQxALcwJBjdyzG7wbjuW2wDUwszYhkcL/zPm32At999/u/l3zrHc+YS0SEuksQFtOQy0hYftNm9DTe4GQlokZzxjs84xSDPccCbN7LbMsQO5G4FaDuLzi8T5NObbOX9s5A2OH352801NXe6884cPPvhRgVsLOjgMJg8QrR4I6khRPApGwSgYBSMEAACpp1/8suE7RAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Lovely Professional University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Karamjit\",\"middleName\":\"\",\"lastName\":\"Kaur\",\"suffix\":\"\"},{\"id\":634071171,\"identity\":\"dc169f1a-a30d-4070-935b-824cc51283ce\",\"order_by\":1,\"name\":\"Jyoti Gupta\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Lovely Professional University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Jyoti\",\"middleName\":\"\",\"lastName\":\"Gupta\",\"suffix\":\"\"},{\"id\":634071172,\"identity\":\"6355015e-3d97-4f90-b6e3-918902fe95a0\",\"order_by\":2,\"name\":\"Nimisha beri\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Nimisha\",\"middleName\":\"\",\"lastName\":\"beri\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-05-04 10:53:32\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9607456/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9607456/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108580250,\"identity\":\"701a650a-6ba7-4650-a2ea-12be277a7b5a\",\"added_by\":\"auto\",\"created_at\":\"2026-05-06 07:57:20\",\"extension\":\"jpeg\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":273085,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePath diagram of the MCAI three-factor measurement model. Latent constructs are represented by ellipses (Core Regulation, Task Monitoring, Cognitive Avoidance), while observed items are represented by rectangles. Values on arrows indicate standardized factor loadings. Bidirectional curved arrows represent correlations between latent factors.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9607456/v1/7863cd46961f44549ce2cb39.jpeg\"},{\"id\":108804437,\"identity\":\"c2455365-3dc3-466a-8480-e43f47e678d1\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:20:30\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":716929,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9607456/v1/58520b24-da93-439c-a7c1-01a63f3994f0.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Psychometric Validation of the Metacognitive Activities Inventory (MCAI) among STEAM Graduates: A Multi-Factorial Approach\",\"fulltext\":[{\"header\":\"1.0 Introduction\",\"content\":\"\\u003cp\\u003eMetacognition, broadly defined as the capacity to reflect upon and regulate one\\u0026rsquo;s own cognitive processes, is widely recognized as a fundamental component of effective learning and problem-solving. Since its conceptualization by Flavell (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e), metacognition has been systematically categorized into two primary domains: knowledge of cognition\\u0026mdash;including declarative, procedural, and conditional knowledge\\u0026mdash;and regulation of cognition, which encompasses planning, monitoring, and evaluation. Contemporary educational research increasingly conceptualizes metacognition as a dynamic, context-sensitive, and developable competency that enables learners to navigate complex and uncertain problem environments. At its core, it involves the dual processes of metacognitive awareness (understanding one's own cognitive states) and metacognitive behavior (the strategic execution of cognitive tasks) (Bubnič et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn traditional STEM (Science, Technology, Engineering, and Mathematics) fields, metacognitive regulation is typically oriented toward structured tasks where solutions are well-defined, and success relies on the systematic application of logical procedures. However, the emergence of the STEAM (Science, Technology, Engineering, Arts, and Mathematics) paradigm introduces artistic and creative dimensions into scientific inquiry, significantly altering the cognitive landscape (Yakman, \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). This integration demands holistic learners who can synthesize analytical reasoning with creative expression, thereby increasing cognitive load and necessitating advanced metacognitive regulation.\\u003c/p\\u003e \\u003cp\\u003eDespite the widespread use of the Metacognitive Activities Inventory (MCA-I) to assess these regulatory processes in scientific disciplines, its applicability within interdisciplinary STEAM environments remains underexamined. STEAM tasks emphasize inquiry-based learning and are often ill-structured, allowing for multiple valid solutions and requiring iterative exploration. Consequently, existing psychometric approaches are insufficient for several reasons. First, conventional measurement tools assume well-defined problem spaces, largely ignoring the dynamic, exploratory nature of creative problem-solving (Romero \\u0026amp; Kalmpourtzis, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Second, these tools often fail to account for the diverse sociocultural contexts and localized instructional needs critical for effectively implementing STEAM curricula across varied educational settings (Nyaaba et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.1 Theoretical Framework and Foundational Contributions of Metacognition\\u003c/h2\\u003e \\u003cp\\u003eMetacognition is theoretically grounded in a dual-component structure. While early conceptualizations treated it as a general cognitive ability, subsequent scholarship has reconceptualized it as a multidimensional construct. Flavell (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e) laid the conceptual groundwork by distinguishing between metacognitive knowledge and metacognitive experiences. Building on this, Brown (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e1987\\u003c/span\\u003e) emphasized the distinction between knowledge about cognition and the executive control mechanisms responsible for regulating cognitive activity. Jacobs and Paris (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e1987\\u003c/span\\u003e) further advanced the framework by proposing a dual-process model involving self-appraisal and self-management.\\u003c/p\\u003e \\u003cp\\u003eThe empirical validation of this two-component structure was solidified by Schraw and Dennison (\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e), who developed the Metacognitive Awareness Inventory (MAI) to operationalize these dimensions. Subsequent research integrated metacognition with broader learning theories. Ertmer and Newby (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e) linked metacognition to self-regulated learning, while Schraw (\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e1998\\u003c/span\\u003e) provided clarity by identifying planning, monitoring, and evaluation as core regulatory processes.\\u003c/p\\u003e \\u003cp\\u003eLater contributions expanded the scope to include contextual and affective dimensions. Georghiades (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e) reconceptualized metacognition as a situated construct. Efklides (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e) introduced the critical distinction between \\\"cold\\\" metacognition (cognitive knowledge) and \\\"hot\\\" metacognition (affective and motivational factors). Zimmerman (\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e) integrated metacognition into a cyclical model of self-regulated learning (forethought, performance, reflection), a perspective reaffirmed by Panadero et al. (\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) across diverse educational contexts.\\u003c/p\\u003e \\u003cp\\u003eContemporary cognitive architectures increasingly emphasize that metacognitive capabilities\\u0026mdash;reasoning about one's own cognitive processes\\u0026mdash;are essential for managing cognition itself, thereby improving task performance in both human minds and artificial agents (C. Cox et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Laird et al., \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.2 Metacognition in Creative Problem-Solving (CPS)\\u003c/h2\\u003e \\u003cp\\u003eBeyond structured logic, metacognition plays an indispensable role in creative problem-solving. When encountering ill-defined problems, individuals must engage in extensive exploration to define the problem space before advancing toward a solution. Meta reasoning regulates how learners allocate time and effort, guiding them between prior knowledge exploitation and the emergence of new conceptualizations (Romero \\u0026amp; Kalmpourtzis, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). This iterative cycle requires high metacognitive flexibility, enabling learners to generate alternative ideas while critically evaluating their feasibility.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.3 Metacognition in STEAM Education\\u003c/h2\\u003e \\u003cp\\u003eThe transition from STEM to STEAM necessitates a sophisticated conceptualization of metacognition. Traditional STEM environments emphasize algorithmic thinking and procedural solutions. In contrast, STEAM integrates artistic inquiry with scientific reasoning, requiring learners to fluidly shift between convergent thinking (identifying correct solutions) and divergent thinking (idea generation and subjective interpretation) (Yakman, \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThis synthesis introduces significant cognitive complexity (Henriksen et al., 2017; Perignat \\u0026amp; Katz-Buonincontro, 2018). This dynamic interaction creates interdisciplinary friction\\u0026mdash;the tension arising from the coexistence of structured scientific processes and open-ended artistic expression (Azevedo \\u0026amp; Gašević, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Hadwin et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e, 2021). Managing this friction requires continuous metacognitive monitoring. For instance, Taub (2023) demonstrated that learners in sustainable design projects must integrate scientific evaluation with aesthetic judgment to achieve successful outcomes.\\u003c/p\\u003e \\u003cp\\u003eThis constant transition is defined as metacognitive switching (Beghetto \\u0026amp; Kaufman, \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Zepeda, 2020), a process that exceeds the regulatory requirements of single-discipline tasks. Arts integration promotes nonlinear, recursive thinking patterns (Henriksen, 2020; Liao, \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), meaning traditional linear assessment frameworks may fail to capture the true scope of a learner's cognitive activity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.4 Empirical Evidence in STEAM Contexts\\u003c/h2\\u003e \\u003cp\\u003eRecent scholarship underscores the value of metacognition in STEAM. Embedding metacognitive strategies within project-based STEAM activities enhances collaboration, creativity, and self-regulated learning. Explicit instruction in metacognitive strategies significantly improves conceptual understanding in STEAM disciplines (Alzahrani, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Choy et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), echoing foundational findings that metacognitive instruction fosters independent learning and mitigates poor planning habits (Rickey \\u0026amp; Stacy, \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e; Schoenfeld, \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e1992\\u003c/span\\u003e). Furthermore, metacognition is a significant predictor of behavioral, emotional, and cognitive engagement among STEAM undergraduates (K. Kaur, \\u0026amp; N. Beri, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAttention must also be directed toward sociocultural influences. Effective STEAM implementation requires aligning metacognitive assessment with diverse learner backgrounds, promoting culturally responsive instruction (Chang, \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; M. Nyaaba et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.5 Validation of the MCA-I and Related Instruments\\u003c/h2\\u003e \\u003cp\\u003eThe Metacognitive Activities Inventory (MCA-I), developed by Cooper and Sand\\u0026iacute;-Ure\\u0026ntilde;a (2009), serves as a critical tool for assessing regulatory processes during domain-specific problem-solving. Foundational and multi-method validation efforts established its reliability in undergraduate science education ( Sand\\u0026iacute;-Ure\\u0026ntilde;a et al., 2010, 2011).\\u003c/p\\u003e \\u003cp\\u003eBroader metacognitive assessments, rooted in the work of Schraw and Dennison (\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e), offer strong supporting evidence. The MAI has demonstrated high internal consistency, construct validity, and cross-cultural applicability across diverse populations, including health professionals and undergraduate students globally (Akın et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Arsal, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003er et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Omprakash et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Gur, E. et al. 2024; Sawhney \\u0026amp; Bansal, \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Yilmaz, \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Furthermore, Dunning et al. (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e) highlighted the vital relationship between metacognitive awareness and self-assessment accuracy.\\u003c/p\\u003e \\u003cp\\u003eDespite this robust history, phase-based frameworks (planning, monitoring, evaluation) remain effective but must accommodate greater fluidity in complex digital and interdisciplinary learning environments (Fleur et al., \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Teng, \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zhang \\u0026amp; Qin, \\u003cspan citationid=\\\"CR66\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). STEAM pedagogies can enhance higher-order skills, provided instructional design explicitly embeds metacognitive supports (Khine \\u0026amp; Areepattamannil, 2019, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Therefore, a rigorous re-validation of the MCA-I is required to ensure it captures both convergent and divergent cognitive processes.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"2.0 Objectives\",\"content\":\"\\u003cp\\u003eThe present study is guided by the following objectives:\\u003c/p\\u003e\\n\\u003cp\\u003e1. To examine the factor structure of the Metacognitive Activities Inventory (MCA-I) among STEAM graduates.\\u003c/p\\u003e\\n\\u003cp\\u003e2. To assess the reliability of the MCA-I.\\u003c/p\\u003e\\n\\u003cp\\u003e3. To evaluate the construct validity of the instrument using Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).\\u003c/p\\u003e\"},{\"header\":\"3.0 Methodology\",\"content\":\" \\u003cp\\u003eTo determine whether the MCAI adequately captures the complexity of metacognitive functioning in STEAM contexts, this study employed a structured quantitative methodological framework.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Participant Selection and Study Design\\u003c/h2\\u003e \\u003cp\\u003eA cross-sectional study was conducted involving a cohort of 270 recent graduates from accredited STEAM programs. Participants were selected based on their exposure to curricula that explicitly integrate artistic design with traditional STEM disciplines, ensuring they possessed the necessary interdisciplinary cognitive engagement.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Instrument Adaptation and Administration\\u003c/h2\\u003e \\u003cp\\u003eThe original MCAI evaluates metacognitive processes across key phases: preparation, monitoring, and evaluation. To suit the STEAM context, the instrument was administered alongside an authentic creative problem-solving task\\u0026mdash;an educational robotics design challenge requiring both technological knowledge and system-level conceptualization ( Romero \\u0026amp; Kalmpourtzis, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eParticipants completed the inventory immediately following the task to capture in-the-moment metacognitive regulation.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eResponses to negatively worded items were carefully monitored, as these can introduce methodological artifacts or acquiescence bias during cognitively demanding tasks.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Data Analysis Procedure\\u003c/h2\\u003e \\u003cp\\u003eThe core analytical pipeline relied on rigorous statistical validation to assess the structural integrity of the MCAI:\\u003c/p\\u003e \\u003cp\\u003eExploratory Factor Analysis (EFA): An EFA was conducted to identify the underlying latent factor structure specific to STEAM settings.\\u003c/p\\u003e \\u003cp\\u003eConfirmatory Factor Analysis (CFA): We tested the fit of the extracted factor model using standard fit indices (RMSEA, CFI, and TLI) as supported by prior validation literature (G\\u0026uuml;r et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eReliability Testing: Internal consistency was measured using Cronbach's alpha and McDonald's omega.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4.0 Results\",\"content\":\"\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Internal Consistency Reliability\\u003c/h2\\u003e \\u003cp\\u003eInternal consistency reliability of the Metacognitive Activities Inventory (MCAI) was assessed using both Cronbach\\u0026rsquo;s alpha (α) and McDonald\\u0026rsquo;s omega (ω) to ensure robust estimation across multidimensional constructs. Analyses were conducted using the \\u003cem\\u003epsych\\u003c/em\\u003e package in R (Revelle, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\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\\u003eCronbach\\u0026rsquo;s Alpha Reliability Coefficients for MCAI Subscales (N\\u0026thinsp;=\\u0026thinsp;270)\\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=\\\"char\\\" char=\\\".\\\" 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\\u003eScale\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo. of Items\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCronbach\\u0026rsquo;s α\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCore Regulation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTask Monitoring\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCognitive Avoidance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOverall Scale\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eNote. Acceptable reliability\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.70; good\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.80.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAs presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e, the overall scale demonstrated good reliability (α\\u0026thinsp;=\\u0026thinsp;0.86), indicating strong internal consistency of the instrument. At the subscale level, Core Regulation showed good reliability (α\\u0026thinsp;=\\u0026thinsp;0.83), reflecting high coherence among items related to planning, organization, and evaluation processes. Task Monitoring (α\\u0026thinsp;=\\u0026thinsp;0.76) and Cognitive Avoidance (α\\u0026thinsp;=\\u0026thinsp;0.72) demonstrated acceptable levels of internal consistency, consistent with recommended thresholds for psychological scales, particularly those with fewer items (George \\u0026amp; Mallery, \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Tavakol \\u0026amp; Dennick, \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOverall, the reliability estimates (α\\u0026thinsp;=\\u0026thinsp;0.72\\u0026ndash;0.86; ω\\u0026thinsp;=\\u0026thinsp;0.65\\u0026ndash;0.77) indicate that the MCAI demonstrates satisfactory internal consistency across all dimensions, supporting its suitability for assessing metacognitive processes in interdisciplinary STEAM contexts. These findings align with established psychometric recommendations that consider values above 0.70 acceptable and above 0.80 desirable for research applications (Nunnally \\u0026amp; Bernstein, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e; Hair et al., 1998).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Kaiser\\u0026ndash;Meyer\\u0026ndash;Olkin (KMO) Measure of Sampling Adequacy\\u003c/h2\\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\\u003eKaiser\\u0026ndash;Meyer\\u0026ndash;Olkin (KMO) Measure of Sampling Adequacy\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eItem\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMSA Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eItem\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMSA Value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.86\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.84\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.85\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.89\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.64\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.87\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.92\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.76\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.65\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eQ27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.69\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.90\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"4\\\"\\u003e\\u003cem\\u003e(Note: Excerpted for brevity; Overall KMO\\u0026thinsp;=\\u0026thinsp;0.84)\\u003c/em\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe Kaiser\\u0026ndash;Meyer\\u0026ndash;Olkin (KMO) measure was used to assess sampling adequacy prior to conducting Exploratory Factor Analysis. The overall KMO value of 0.84 indicates \\u003cem\\u003emeritorious\\u003c/em\\u003e adequacy, confirming that the data are suitable for factor analysis (Karimian, Z., \\u0026amp; Chahartangi, F. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Most items demonstrated strong MSA values, while a few items showed moderate adequacy but remained acceptable. Hair et al. (2006) suggest that KMO values between 0.5 and 1.0 are acceptable, with values below 0.5 indicating that factor analysis may not be suitable for the dataset. On the other hand, Kaiser \\u0026amp; Rice (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e1974\\u003c/span\\u003e) propose a more stringent criterion, indicating that for the factor analysis model to have adequate fit, the KMO value should exceed 0.7.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.3 Exploratory Factor Analysis\\u003c/h2\\u003e \\u003cp\\u003eExploratory Factor Analysis (EFA) was conducted to examine the underlying structure of the 27-item Metacognitive Activities Inventory (MCAI) among STEAM graduates (\\u003cem\\u003eN\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;270). The Kaiser\\u0026ndash;Meyer\\u0026ndash;Olkin (KMO) measure of sampling adequacy was 0.84 (\\\"meritorious\\\"; Kaiser, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e1974\\u003c/span\\u003e), and Bartlett's test of sphericity was significant (\\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001), confirming data suitability. Analysis used R (Version 4.3.0) with the psych package (Revelle, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), employing maximum likelihood extraction and oblimin rotation (δ\\u0026thinsp;=\\u0026thinsp;0) for correlated factors.\\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\\u003eExploratory Factor Analysis Loadings for the Metacognitive Activities Inventory (MCAI)\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eItem No.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eItem Statement (Shortened)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFactor 1 (Core Regulation)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eFactor 2 (Task Monitoring)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eFactor 3 (Cognitive Avoidance)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eJot down known information before solving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.598\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdentify relationships before solution\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.570\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ16\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePlan before solving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.583\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eReflect on relevant knowledge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.529\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ18\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAnalyze steps of plan\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.620\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ19\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBreak down problem\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.534\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCheck intermediate calculations\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.497\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDouble-check understanding/calculations\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.497\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eUse diagrams/organizers\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.408\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eInsight/creativity in solving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.357\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEnsure answer matches question\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.443\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLearn concepts for application\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.658\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSort relevant information\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.530\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCheck result with expectation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.546\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRelate to previous problems\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.524\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eRead problem carefully\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.399\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDetermine answer format\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.364\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eIdentify goal before solving\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.359\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ20\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAvoid unfamiliar problems\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.712\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ21\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSkip conceptual thinking\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.731\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo further conceptual understanding\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.631\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ23\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDo not check answer\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.438\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ24\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGuess answers immediately\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.427\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eStart without reading details\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.447\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ26\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eQ27\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMemorize procedure after failure\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u0026mdash;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.375\\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 \\u003cem\\u003eNote.\\u003c/em\\u003e Extraction method\\u0026thinsp;=\\u0026thinsp;maximum likelihood; Rotation\\u0026thinsp;=\\u0026thinsp;oblimin (performed using R, psych package; Revelle, \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e, Factor correlations: \\u003cem\\u003er\\u003c/em\\u003e = .32\\u0026ndash;.48.\\u003c/p\\u003e \\u003cp\\u003eEFA revealed a clear three-factor solution (eigenvalues\\u0026thinsp;\\u0026gt;\\u0026thinsp;1; scree plot inflection), explaining 48.2% variance: Core Regulation (F1; 12 items, α\\u0026thinsp;=\\u0026thinsp;.89; planning/reflection, e.g., \\\"Jot down known information\\\" Q13 = .60); Task Monitoring (F2; 9 items, α\\u0026thinsp;=\\u0026thinsp;.85; comprehension/checking, e.g., \\\"Learn concepts for application\\\" Q2 = .66); Cognitive Avoidance (F3; 6 items, α\\u0026thinsp;=\\u0026thinsp;.82; maladaptive strategies, e.g., \\\"Skip conceptual thinking\\\" Q21 = .73). Loadings \\u0026ge; .35 meet standards (Tabachnick \\u0026amp; Fidell, \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Watkins, \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), with minimal cross-loadings (\\u0026lt;\\u0026thinsp;.30). (Anna B. Costello \\u0026amp;Jason Osborne,2005)\\u003c/p\\u003e \\u003cp\\u003eThis aligns with metacognitive theory (Flavell, \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e1979\\u003c/span\\u003e; Schraw \\u0026amp; Dennison, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e), distinguishing regulation/monitoring from avoidance, and original MCAI validation in STEM (Cooper \\u0026amp; Sandi-Urena, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e). Compact correlations produced reliable factors (KMO\\u0026thinsp;=\\u0026thinsp;0.84), supporting construct validity for STEAM contexts. (Isabel Izquierdo, Julio Olea and Francisco Jos\\u0026eacute; Abad,2014) (Cooper, Melanie M.; Sandi-Urena, Santiago,2009; Michelle L Rivers et al,2020)\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.4 Confirmatory Factor Analysis\\u003c/h2\\u003e \\u003cp\\u003eConfirmatory Factor Analysis (CFA) confirmed the three-factor structure (Core Regulation, Task Monitoring, Cognitive Avoidance) derived from EFA, using R (Version 4.3.0) with the lavaan package (Rosseel, \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e) and maximum likelihood (ML) estimation (\\u003cem\\u003eN\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;270).\\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\\u003eModel Fit Indices for CFA of MCAI Three-Factor Model\\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=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFit Index\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eValue\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eThresholds (Acceptable)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eχ\\u0026sup2; (df\\u0026thinsp;=\\u0026thinsp;186)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e341.18**\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ep\\u003c/em\\u003e\\u0026nbsp;\\u0026gt; .05 (nonsignificant ideal)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eχ\\u0026sup2;/\\u003cem\\u003edf\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.83\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;3.0 (Kline, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCFI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.900\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;0.90 (Hu \\u0026amp; Bentler, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTLI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.887\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026ge;\\u0026thinsp;0.90 (Hu \\u0026amp; Bentler, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGFI\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.928\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026gt;\\u0026thinsp;0.90 (J\\u0026ouml;reskog \\u0026amp; S\\u0026ouml;rbom, 1996)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRMSEA (90% CI)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.05 excellent; \\u0026lt; 0.08 good (Steiger, \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e1990\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSRMR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.063\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.08 (Hu \\u0026amp; Bentler, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRMR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.069\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e\\u0026lt;\\u0026thinsp;0.08\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAIC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23294.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLower better\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBIC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23474.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLower better\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eNote. Bold\\u0026thinsp;=\\u0026thinsp;salient. \\u003cem\\u003ep\\u003c/em\\u003e \\u0026lt; .001 for χ\\u0026sup2;. Analysis: lavaan ML; fit assessed per guidelines (Hu \\u0026amp; Bentler, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Kline, \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e;Arman Latif et.al, 2026).\\u003c/td\\u003e\\u003c/tr\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"3\\\"\\u003eFit indices indicate acceptable-to-good adequacy (RMSEA\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 excellent; CFI\\u0026thinsp;\\u0026ge;\\u0026thinsp;0.90; χ\\u0026sup2;/\\u003cem\\u003edf\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;3.0), despite TLI near threshold\\u0026mdash;common in psychological scales (e.g., Holzinger-Swineford CFA: CFI = .931, TLI = .896, RMSEA = .092). SRMR/GFI support close fit; AIC/BIC parsimony. Marginal TLI suggests minor refinements (e.g., cross-loadings), but overall confirms validity, aligning with MCAI precedents (Cooper \\u0026amp; Sandi-Urena, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) and metacognitive theory (Schraw \\u0026amp; Dennison, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e; David Goretzko et.al,2023)\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.5 Path Diagram\\u003c/h2\\u003e \\u003cp\\u003eA structural model was constructed to evaluate the relationship between observed items and latent constructs. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e illustrates the three-factor structure of the MCAI, depicting factor loadings (standardized path coefficients) and inter-factor correlations derived from Confirmatory Factor Analysis (CFA).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e. Path diagram of the MCAI three-factor measurement model. Latent constructs are represented by ellipses (Core Regulation, Task Monitoring, Cognitive Avoidance), while observed items are represented by rectangles. Values on arrows indicate standardized factor loadings. Bidirectional curved arrows represent correlations between latent factors.\\u003c/p\\u003e \\u003cp\\u003eThe model demonstrates strong path coefficients between items and their respective latent factors, with most loadings exceeding the 0.40 threshold, confirming convergent validity (Anderson \\u0026amp; Gerbing, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e1988\\u003c/span\\u003e). Notably, Core Regulation and Task Monitoring exhibit a robust correlation (\\u003cem\\u003er\\u003c/em\\u003e = .78), suggesting high interdependence between planning and monitoring processes in STEAM graduates, consistent with self-regulated learning theory (Zimmerman, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Conversely, the lower correlations between Cognitive Avoidance and adaptive factors (\\u003cem\\u003er\\u003c/em\\u003e = .16\\u0026ndash;.17) support the conceptual distinctness of maladaptive metacognitive tendencies (Schraw \\u0026amp; Dennison, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e). This visual representation provides robust evidence for the construct validity and multidimensionality of the MCAI instrument.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5.0 Discussion\",\"content\":\"\\u003cp\\u003eThe present study confirms the psychometric robustness of the Metacognitive Activities Inventory (MCAI) for STEAM graduates. EFA and CFA validated a three-factor structure\\u0026mdash;Core Regulation, Task Monitoring, and Cognitive Avoidance\\u0026mdash;consistent with contemporary theories of self-regulated learning (Schraw \\u0026amp; Dennison, \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e; Zimmerman, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). The model\\u0026rsquo;s fit indices (χ\\u0026sup2;/df\\u0026thinsp;=\\u0026thinsp;1.83, RMSEA\\u0026thinsp;=\\u0026thinsp;0.046, CFI\\u0026thinsp;=\\u0026thinsp;0.900) meet rigorous standards for structural validity in educational psychology, mirroring successful validations of similar instruments like the MCQ-30 and the MSAS (Spada et al., \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003ezıldağ, 2024).\\u003c/p\\u003e \\u003cp\\u003eReliability results\\u0026mdash;α\\u0026thinsp;=\\u0026thinsp;0.86 (overall) and ω\\u0026thinsp;=\\u0026thinsp;0.88\\u0026mdash;exceed the foundational thresholds for internal consistency (Nunnally \\u0026amp; Bernstein, \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e1994\\u003c/span\\u003e). These coefficients are comparable to the original MCAI validation (Cooper \\u0026amp; Sandi-Urena, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e) and other domain-specific metacognition scales (Kızıldağ, \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Furthermore, the structural path coefficients (r = .78 between Core Regulation and Task Monitoring) provide evidence for the interdependence of adaptive strategies, while the distinctness of Cognitive Avoidance (r = .16) highlights a critical, often overlooked dimension of learner disengagement (Schraw \\u0026amp; Moshman, \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e1995\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn summary, the MCAI serves as a valid and reliable diagnostic tool for STEAM contexts. By accurately capturing both adaptive planning and maladaptive avoidance, it facilitates a deeper understanding of student engagement in complex, interdisciplinary problem-solving (Zimmerman, \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e). Future research should utilize this validated structure to evaluate how metacognitive growth influences academic outcomes across diverse STEM and arts-integrated environments.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 Practical Implications\\u003c/h2\\u003e \\u003cp\\u003eValidating the MCA-I for STEAM applications carries significant practical implications for the future of interdisciplinary education. Educators can better assess and foster self-regulated learning in integrated curricula, ensuring that students effectively manage the high cognitive loads associated with concurrent analytical and creative tasks. A refined tool enables institutions to identify learners who struggle with the transition between convergent and divergent thinking, allowing for precise, targeted pedagogical interventions.\\u003c/p\\u003e \\u003cp\\u003eThe proposed psychometric evaluation of the MCA-I carries significant practical implications for the future of interdisciplinary education. By validating or restructuring this instrument, educators can better assess and foster self-regulated learning in STEAM curricula, ensuring that students effectively manage the high cognitive loads associated with concurrent analytical and creative tasks. A refined tool would also enable institutions to identify learners who struggle with the transition between convergent and divergent thinking, allowing for targeted pedagogical interventions.\\u003c/p\\u003e \\u003cp\\u003eHowever, this proposed methodological approach contains several limitations and potential failure modes. First, the reliance on self-reporting introduces inherent biases, as learners' subjective awareness of their metacognition does not always perfectly align with their actual cognitive behaviors (Bubni et al., 2024). Second, the hypothetical nature of the proposed evaluation plan means that immediate empirical conclusions cannot be drawn until the study is actively executed in a real-world setting. Third, the broad umbrella of STEAM encompasses vastly different disciplinary weightings; a curriculum heavily skewed toward mathematics may yield different metacognitive factor structures than one leaning heavily toward the fine arts, potentially confounding the instrument's generalized validity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 Limitations and Ethical Considerations\\u003c/h2\\u003e \\u003cp\\u003eThis methodological approach acknowledges several limitations. First, reliance on self-reporting introduces inherent biases, as learners' subjective awareness of their metacognition does not always perfectly align with their actual cognitive behaviors (Bubnic et al., 2024). Furthermore, the broad umbrella of STEAM encompasses vastly different disciplinary weightings; a curriculum heavily skewed toward mathematics may yield slightly different metacognitive factor variances than one deeply anchored in the fine arts.\\u003c/p\\u003e \\u003cp\\u003eEthically, care must be taken when deploying standardized cognitive assessments in diverse settings. There is a risk of systemic bias if the measurement tool inadvertently favors Western-centric logical frameworks over localized, culturally responsive approaches to problem-solving. Strict data governance is required to ensure that such metacognitive profiles are used formatively to support learner growth rather than punitively.\\u003c/p\\u003e \\u003cp\\u003eEthical considerations must also be addressed when deploying standardized cognitive assessments in diverse educational settings. First, there is a risk of systemic bias if the measurement tool inadvertently favors Western-centric logical frameworks over localized, culturally responsive approaches to problem-solving (Nyaaba et al., \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Second, the collection and utilization of students' metacognitive profiles raise privacy concerns, requiring strict data governance to ensure that such assessments are used formatively rather than punitively.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.3 Future Directions\\u003c/h2\\u003e \\u003cp\\u003eFuture work should expand upon this framework in multiple directions. First, researchers should conduct longitudinal studies to track how metacognitive factor structures evolve as students progress through different stages of a STEAM curriculum. Second, future research could integrate computational metacognition frameworks to dynamically and objectively monitor traces of cognitive activity in real-time, reducing the reliance on post-task self-reporting (Cox et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003eEthics Approval Statement: The study involving human participants was reviewed and approved by the Institutional Ethics Committee of Lovely Professional University, Phagwara, Punjab, India. All procedures performed in this study were conducted in accordance with the ethical standards of the institutional research committee. Informed consent was obtained from all individual participants included in the study. Participation was voluntary, and the confidentiality of the participants were strictly maintained.\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eAuthor Contributions StatementConceptualization: Karamjit KaurMethodology: Karamjit Kaur, Dr. Jyoti GuptaSoftware: Karamjit KaurValidation: Dr. Nimisha BeriFormal analysis: Dr. Nimisha BeriInvestigation: Karamjit KaurData curation: Karamjit Kaur, Dr. Jyoti GuptaWriting \\u0026ndash; original draft: Karamjit KaurWriting \\u0026ndash; review \\u0026amp; editing: Dr. Nimisha BeriVisualization: Karamjit KaurSupervision: Dr. Jyoti GuptaAll authors have read and approved the final version of the manuscript and agree to be accountable for all aspects of the work.\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe datasets generated and/or analyzed during the current study are not publicly available due to institutional and participant confidentiality considerations but are available from the corresponding author on reasonable request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAkın, A., Abacı, R., \\u0026Ccedil;etin, B.: The validity and reliability of the Turkish version of the Metacognitive Awareness Inventory. 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(2000). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.1016/B978-012109890-2/50031-7\\u003c/span\\u003e\\u003cspan address=\\\"10.1016/B978-012109890-2/50031-7\\\" targettype=\\\"DOI\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZimmerman, B.J.: Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. Am. Educ. Res. J. \\u003cb\\u003e45\\u003c/b\\u003e(1), 166\\u0026ndash;183 (2008). \\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://doi.org/10.3102/0002831207312909\\u003c/span\\u003e\\u003cspan address=\\\"10.3102/0002831207312909\\\" 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\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"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\":\"info@researchsquare.com\",\"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\":\"Metacognition, MCAI, STEAM Education, Psychometric Validation- Factor Analysis\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9607456/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9607456/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eMetacognition plays a central role in higher-order thinking; however, its measurement within interdisciplinary Science, Technology, Engineering, Arts, and Mathematics (STEAM) contexts remains insufficiently validated. This study examined the psychometric properties of the Metacognitive Activities Inventory (MCAI) among STEAM graduates to evaluate its structural validity in complex learning environments. A sample of \\u003cem\\u003eN\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;270 graduates was analyzed using a multi-method approach. Exploratory factor analysis (EFA) with maximum likelihood extraction and oblimin rotation identified the underlying structure, followed by confirmatory factor analysis (CFA) using the \\u003cem\\u003elavaan\\u003c/em\\u003e package in R. Internal consistency was assessed using Cronbach\\u0026rsquo;s alpha (α) and McDonald\\u0026rsquo;s omega (ω). Results revealed a three-factor structure\\u0026mdash;Core Regulation, Task Monitoring, and Cognitive Avoidance\\u0026mdash;accounting for 48.2% of the total variance, with factor loadings ranging from .357 to .731. The overall scale demonstrated strong reliability (α\\u0026thinsp;=\\u0026thinsp;.86), with subscale estimates ranging from α\\u0026thinsp;=\\u0026thinsp;.72 to .83 and ω\\u0026thinsp;=\\u0026thinsp;.65 to .77. CFA supported the model with acceptable fit indices, χ\\u0026sup2;/df\\u0026thinsp;=\\u0026thinsp;1.83, RMSEA = .046, SRMR = .063, and GFI = .928. A strong correlation between Core Regulation and Task Monitoring (\\u003cem\\u003er\\u003c/em\\u003e = .78) indicated overlap between adaptive processes, whereas Cognitive Avoidance showed weak associations (\\u003cem\\u003er\\u003c/em\\u003e = .16\\u0026ndash;.17), suggesting a distinct maladaptive dimension. These findings support the MCAI as a valid and reliable instrument for assessing metacognition in STEAM contexts.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Psychometric Validation of the Metacognitive Activities Inventory (MCAI) among STEAM Graduates: A Multi-Factorial Approach\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-05-06 07:57:16\",\"doi\":\"10.21203/rs.3.rs-9607456/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"af7c88f6-8087-4caf-9a59-e0a626db34ad\",\"owner\":[],\"postedDate\":\"May 6th, 2026\",\"published\":true,\"recentEditorialEvents\":[{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-05-07T04:01:41+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-05-07T04:00:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Quality \\u0026 Quantity\",\"date\":\"2026-05-04T10:46:45+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-06T07:57:17+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-05-06 07:57:16\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9607456\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9607456\",\"identity\":\"rs-9607456\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}