Bridging the Achievement Gap: A Metacognitive Intervention to Enhance Analytical Reasoning and Reduce Cognitive Bias in High- and Low-Achieving Students

Bridging the Achievement Gap: A Metacognitive Intervention to Enhance Analytical Reasoning and Reduce Cognitive Bias in High- and Low-Achieving Students | 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 Bridging the Achievement Gap: A Metacognitive Intervention to Enhance Analytical Reasoning and Reduce Cognitive Bias in High- and Low-Achieving Students Halimah Ibrahim Alfailakawy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7408588/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 This study tested a targeted metacognitive intervention designed to enhance analytical reasoning, reduce cognitive bias, and subsequently narrow the cognitive achievement gap. A quasi-experimental, pre-test-post-test-follow-up design was employed with 120 Kuwaiti undergraduate students, who were stratified by prior academic performance (high-achieving, n = 60; low-achieving, n = 60) and then randomly assigned to an eight-week intervention or a control group. Analytical reasoning and susceptibility to cognitive bias were measured at three time points. A mixed-model ANOVA revealed a significant three-way interaction (p < .001), indicating the intervention’s effects were moderated by achievement level. While all students in the intervention group demonstrated significant gains, improvements were most pronounced for the low-achieving students, whose post-intervention scores in both domains converged significantly with those of their high-achieving peers. These notable gains were marked by very large effect sizes for the low-achieving group (d = 2.45 for analytical reasoning; d = -3.27 for cognitive bias) and were successfully retained at a six-week follow-up. Findings provide compelling evidence that metacognitive training is a promising and durable tool for fostering cognitive equity. This research offers a scalable, evidence-based model for supporting struggling learners and cultivating more robust, less-biased reasoning across the student population. Metacognition Analytical Reasoning Cognitive Bias Achievement Gap Higher Education Cognitive Intervention Introduction The persistent achievement gap between high- and low-achieving students remains one of the most significant challenges in modern education. This gap is typically measured by grades and test scores, but its roots lie deeper, in disparities in higher-order thinking skills, including the ability to think critically and analytically [1] . While low-achieving students may struggle with foundational cognitive strategies [2] , even high-achieving students are not immune to cognitive pitfalls. High academic performance does not automatically confer sophisticated reasoning; in fact, it can mask an underlying vulnerability to cognitive biases [3, 4] . Cognitive biases are systematic patterns of deviation from rational judgment that affect all thinkers. For instance, confirmation bias—the tendency to favor information that supports existing beliefs—can entrench a high-achiever’s overconfidence or reinforce a low-achiever’s sense of academic futility [5] . Such biases prevent objective analysis and impede the development of authentic analytical reasoning: the skill of systematically deconstructing problems, evaluating evidence, and forming logical conclusions [6, 7] . Addressing these biases is crucial for cultivating robust intellectual habits in all students. Metacognition, or "thinking about thinking," offers a powerful pedagogical framework to address these challenges. Metacognitive strategies are self-regulatory skills that enable individuals to plan, monitor, and evaluate their own thinking processes [8, 9] . For low-achieving students, explicit instruction provides a toolkit for approaching complex problems [10] . For high-achieving students, it serves to refine existing skills and challenge biases [11] . By fostering self-regulation, metacognition acts as an "inclusion amplifier," equipping all students with the tools to manage their own learning [12] . While many interventions focus on content-knowledge gaps, few have examined metacognitive training as a direct mechanism for closing the gap in thinking skills by comparing its effects on both high- and low-achieving students [13] . This study addresses this void by first establishing the relationship between analytical reasoning and cognitive bias. It then investigates the effectiveness of a targeted metacognitive program in enhancing analytical reasoning and reducing cognitive bias across both achievement groups. A central aim is to determine if the intervention can significantly narrow the pre-existing cognitive gap, thereby providing evidence for a pedagogical approach that not only enhances high performers’ skills but also elevates the capabilities of those who struggle, fostering a more equitable educational landscape. Research Questions: This study sought to answer the following research questions: What is the nature of the relationship between analytical reasoning and cognitive bias across high- and low-achieving undergraduate students? How effective is a metacognitive training program in cultivating analytical reasoning skills in high-achieving versus low-achieving students? To what extent does a metacognitive training program reduce cognitive bias in high-achieving and low-achieving students, and does the intervention succeed in narrowing the initial gap in these skills between the two groups? Literature Review This literature review examines the theoretical and empirical foundations for the current study. It explores the complex interplay between the academic achievement gap, the development of higher-order thinking, the pervasive influence of cognitive bias, and the potential of metacognitive interventions to foster cognitive equity. The Achievement Gap and the Imperative for Higher-Order Thinking The persistent gap in academic outcomes between high- and low-achieving students represents one of the most pressing challenges in contemporary education [14, 15] . This disparity, while often quantified through standardized test scores and grades, is fundamentally a reflection of deeper inequalities in the development of higher-order thinking skills [16] . Historically, educational systems have sometimes operated under the flawed assumption that advanced cognitive processes are the exclusive domain of high-achievers, while low-achieving students require a focus on rote memorization [17] . This belief perpetuates a cycle of underachievement by denying struggling students the very tools they need to overcome their academic challenges [18] . Recent pedagogical research strongly refutes this outdated view, emphasizing that all students, regardless of their current achievement level, possess the capacity to develop sophisticated thinking skills when provided with appropriate instructional support [19] . Educational initiatives aimed at closing the achievement gap must therefore move beyond remedial content delivery and instead focus on cultivating these essential cognitive and metacognitive competencies [20] . Doing so empowers low-achieving students to become active, engaged, and capable learners. This necessary instructional support is embodied in evidence-based strategies designed to make complex cognition accessible. Methodologies such as scaffolding and cognitive activation have proven effective in developing higher-order thinking in all learners [14, 16] . For instance, the 'Visible Thinking' approach externalizes thought processes, allowing low-achieving students to internalize the sophisticated strategies of their peers [15] . These techniques function as a critical "bridge," connecting a student’s current abilities to more advanced cognitive processes and effectively narrowing the performance gap [21, 22] . Investing in such pedagogies is therefore a direct investment in educational equity, creating tangible pathways for students to cultivate the robust thinking skills essential for future success. The Centrality of Analytical Thinking Analytical thinking stands as a cornerstone of higher-order cognition: the systematic process of breaking down complex problems, identifying logical structures, and evaluating information to arrive at reasoned judgments [23] . Mastery of analytical thinking is a foundational skill for lifelong learning and effective problem-solving [24] . For low-achieving students, developing these skills is critical, as it equips them with a structured approach to tackle difficult material and overcome learning obstacles [16] . Educational strategies that explicitly teach and scaffold analytical reasoning have demonstrated significant success in improving outcomes for all students, particularly those who struggle academically [15, 25] . Building this cognitive bridge requires intentional pedagogical design that fosters the metacognitive skills underpinning effective analysis [26] . Students develop analytical proficiency not just by learning procedures, but by actively monitoring their thought processes, questioning assumptions, and evaluating their strategies [27] . A key challenge, particularly for struggling learners, is managing cognitive load. Emerging research suggests that technologies like AI can play a supportive role by automating routine tasks, thereby freeing students to engage more deeply in analytical thinking [28, 29] . Ultimately, the cultivation of analytical thinking guides students to become active constructors of knowledge, capable of both dissecting and synthesizing information in meaningful ways. The Universal Challenge of Cognitive Bias A formidable barrier to clear analytical thinking is the universal human susceptibility to cognitive bias—inherent mental shortcuts and systematic errors that can lead to irrational conclusions. These biases are not a sign of low intelligence; they affect high- and low-achievers alike, often operating outside of conscious awareness [30] . For instance, confirmation bias can lead any student to uncritically accept information that aligns with their beliefs while dismissing contradictory evidence [31] . Computational models suggest these biases can significantly influence a student’s learning trajectory, reinforcing negative self-perceptions in low-achievers and intellectual arrogance in high-achievers [32] . Therefore, any effective educational intervention must include strategies to help students recognize and mitigate these biases [33, 34] . Countering ingrained cognitive biases requires cultivating metacognitive awareness—the ability to turn one's analytical lens inward. This self-regulation enables students to question their assumptions and actively search for disconfirming evidence, mitigating the pull of biases [20, 34] . While crucial for students who have not developed these habits, it is equally vital for high-achievers, whose expertise can lead to overconfidence [32, 35] . Explicitly teaching metacognitive strategies is thus a fundamental component of developing analytical rigor, allowing learners to override automatic, biased thinking and engage in more objective, evidence-based reasoning. Indeed, research demonstrates the pervasive and detrimental impact of biases on academic performance [3, 4, 12] , reinforcing the need for interventions that build these metacognitive defenses. Metacognition as a Bridge for the Cognitive Divide Metacognition, or “thinking about thinking,” has emerged as a high-impact pedagogical approach for addressing the cognitive dimensions of the achievement gap [36, 37] . It involves metacognitive knowledge (awareness of one’s cognition) and regulation (the ability to plan, monitor, and evaluate one's thinking) [20, 38] . By making these internal processes explicit, metacognitive instruction provides a transparent roadmap for learning that research overwhelmingly shows is powerful for low-achieving students [39, 40] . When struggling learners are explicitly taught how to plan, monitor, and reflect, they gain a sense of agency that is transformative [18, 35, 41] . Crucially, metacognitive strategies serve as a powerful bridge to narrow the cognitive gap between high- and low-achievers [21, 28, 42] . While high-achieving students benefit from refining their skills, the relative gains are often greatest for their lower-performing peers, reducing performance disparities [43] . The deliberate cultivation of metacognitive awareness is thus a fundamental tool for creating more equitable learning environments [22, 44] . Although the literature highlights metacognition's efficacy in promoting self-regulated learning [10, 11, 33] , few studies have implemented culturally specific interventions or directly compared outcomes across achievement levels within a single program [6, 15] . The present study extends this work by implementing and evaluating a targeted metacognitive program in a Kuwaiti undergraduate setting, with the specific aim of enhancing analytical reasoning and reducing cognitive bias among both high- and low-achieving students to examine its potential to foster cognitive equity. Hypothese Based on the literature review, the following hypotheses were formulated for this study: A significant inverse relationship exists between analytical reasoning skills and susceptibility to cognitive bias across both high- and low-achieving college students in the State of Kuwait. The metacognitive training program will lead to a significant improvement in analytical reasoning skills for both high- and low-achieving college students. It is further hypothesized that the magnitude of improvement will be greater for the low-achieving group, thereby narrowing the initial achievement gap in this skill. Following the intervention, both high- and low-achieving students will demonstrate a significant reduction in cognitive bias. The reduction is expected to be more pronounced in the low-achieving group, leading to a convergence in scores between the two achievement groups. Method Participants and Design This study employed a quasi-experimental, pre-test-post-test control group design with academic achievement level (high vs. low) as a between-subjects factor. Participants were 120 undergraduate students (60 male, 60 female; ages 19–22) from the College of Basic Education in Kuwait. Students were first stratified into two groups based on their cumulative grade point average (CGPA): high-achieving (n = 60; top quartile) and low-achieving (n = 60; bottom quartile). Within each stratum, participants were randomly assigned to either an experimental group receiving the metacognitive intervention or a control group attending their regular tutorial sessions. This process resulted in four groups of 30 students each: High-Achieving Experimental, Low-Achieving Experimental, High-Achieving Control, and Low-Achieving Control. Instruments All study materials were translated into Arabic and subsequently back-translated to ensure cultural and linguistic appropriateness. Analytical Reasoning Scale (ARS). The ARS is a 30-item multiple-choice instrument adapted from established frameworks of critical thinking assessment [ 45 ] to assess analytical reasoning across three subscales: Deconstructive Analysis, Synthetic Reasoning, and Applied Reasoning. Scores range from 0 to 30. A pilot study confirmed strong psychometric properties, including excellent internal consistency (Cronbach’s α = .87), strong test-retest reliability (r = .85), and evidence of content, construct, and criterion validity. This scale has been published elsewhere, and the appropriate reference is provided in the manuscript [ 45 ]. Cognitive Bias Scale (CBS). Developed for this study based on the seminal work on cognitive heuristics [ 46 ], the CBS is a 15-item instrument measuring susceptibility to Confirmation, Anchoring, and Overconfidence biases. Items are scored on a 4-point scale, with total scores ranging from 0 to 45 (higher scores indicate greater bias). Validation studies confirmed good internal consistency (Cronbach’s α = .81), strong test-retest reliability (r = .82), and evidence of content, construct, and criterion validity. As this scale was developed for the current study, an English language version has been included as a supplementary file and is cited here as Supplementary File 1. Metacognitive Training Program. The intervention was a structured, eight-week Metacognitive Training Program consisting of weekly 60-minute sessions. Grounded in self-regulated learning theory, the program was designed to enhance analytical reasoning and reduce cognitive bias by teaching explicit metacognitive strategies. The curriculum focused on four core, scaffolded skill areas: goal setting and planning, which involved deconstructing complex tasks; self-monitoring and reflection to assess understanding and question thinking processes; cognitive bias awareness, where students learned to identify and challenge common biases; and strategy evaluation, guiding them to reflect on and adapt their learning approaches. Procedure and Data Analysis Plan Following institutional approval, all participants provided written informed consent. Data were collected at three time points: pre-intervention, post-intervention (immediately after the 8-week program), and a six-week follow-up. All quantitative data were analyzed using IBM SPSS Statistics (Version 28) with an alpha level of α = .05. To test the first hypothesis regarding the relationship between analytical reasoning and cognitive bias, a Pearson correlation coefficient was calculated using pre-intervention data. To test the second and third hypotheses on the intervention's effectiveness and its impact on the achievement gap, a 2 (Intervention: experimental, control) x 2 (Achievement: high, low) x 3 (Time: pre-test, post-test, follow-up) mixed-model Analysis of Variance (ANOVA) was conducted for both the ARS and CBS scores. This analysis allowed for the examination of the crucial three-way interaction to determine if the program's effectiveness differed between high- and low-achieving students. Significant effects were followed by post-hoc tests with Bonferroni correction. Ethical Considerations This study was conducted in strict adherence to established ethical principles. Full approval was obtained from the institutional review board at the College of Basic Education (Protocol #CBE-2023-017). All participants, being of legal age, provided written informed consent after being briefed on the study's purpose and their right to withdraw at any time without penalty. To maintain confidentiality, participants were assigned pseudonyms, and all data were coded and stored securely. Findings are reported only in aggregate form. Results This study investigated the effectiveness of a metacognitive training program on the analytical reasoning and cognitive bias of high- and low-achieving undergraduate students in Kuwait. The findings provide strong evidence for the program's effectiveness, demonstrating a significant inverse relationship between analytical reasoning and cognitive bias and showing that the intervention successfully enhanced skills and narrowed the achievement gap. Relationship Between Analytical Reasoning and Cognitive Bias An initial analysis of pre-intervention data from all 120 participants was conducted to test the relationship between students' analytical reasoning abilities and their susceptibility to cognitive bias. As hypothesized, a strong, statistically significant negative correlation was observed between the total scores on the Analytical Reasoning Scale and the Cognitive Bias Scale, r(118) = − .71, p < .001. This robust inverse relationship indicates that students with stronger analytical reasoning skills were significantly less prone to cognitive biases. The pattern held across all sub-dimensions, with total cognitive bias negatively correlated with Deconstructive Analysis (r = − .69, p < .01), Synthetic Reasoning (r = − .65, p < .01), and Applied Reasoning (r = − .74, p < .01). Impact of Metacognitive Training on Analytical Reasoning To assess the training program's effectiveness, a 2 (Intervention: Experimental, Control) x 2 (Achievement Level: High, Low) x 3 (Time: Pre-test, Post-test, Follow-up) mixed-model ANOVA was conducted on the analytical reasoning scores. The results revealed a significant three-way interaction between Time, Intervention, and Achievement Level, F(2, 232) = 8.92, p < .001, ηp² = .071. This finding indicates that the intervention's impact on analytical reasoning development over time was significantly different for high- and low-achieving students. As shown in Table 1 , the low-achieving experimental group demonstrated the most substantial gains, effectively narrowing the initial gap between them and their high-achieving peers. This powerful interaction effect highlights the program's success in promoting cognitive equity, whereas the control groups showed no significant changes over time. Table 1 Mean Scores and Standard Deviations for Analytical Reasoning and Cognitive Bias by Group and Time Measure and Group Analytical Reasoning High-Achieving Low-Achieving M (SD) M (SD) Pre-Test Experimental (n = 30) 75.2 (6.1) 54.1 (7.3) Control (n = 30) 74.9 (6.5) 54.8 (7.0) Post-Test Experimental (n = 30) 85.7 (5.5) 72.4 (6.8) Control (n = 30) 75.5 (6.3) 55.1 (7.1) Follow-Up Experimental (n = 30) 85.1 (5.8) 71.9 (7.0) Control (n = 30) 75.2 (6.6) 54.9 (7.2) Cognitive Bias Pre-Test Experimental (n = 30) 32.5 (5.4) 55.8 (6.9) Control (n = 30) 33.1 (5.8) 56.2 (7.2) Post-Test Experimental (n = 30) 21.3 (4.9) 34.1 (6.2) Control (n = 30) 32.8 (5.5) 55.9 (7.0) Follow-Up Experimental (n = 30) 22.0 (5.1) 35.2 (6.5) Control (n = 30) 33.4 (5.9) 56.5 (7.4) Note. For Analytical Reasoning, higher scores indicate better performance. For Cognitive Bias, lower scores indicate better performance (i.e., less bias). The results detailed in Table 1 clearly illustrate the dual impact of the metacognitive intervention: it not only enhanced cognitive skills for all participating students but also served as a powerful tool for promoting educational equity. The most dramatic gains are visible in the low-achieving experimental group, whose post-intervention scores in both analytical reasoning and cognitive bias shifted significantly closer to those of their high-achieving peers. This convergence demonstrates that the program was particularly effective for students who started with the greatest need, successfully narrowing the initial cognitive achievement gap and highlighting the intervention's potential to foster more equitable learning outcomes. Impact of Metacognitive Training on Cognitive Bias A corresponding 2 (Intervention) x 2 (Achievement Level) x 3 (Time) mixed-model ANOVA was conducted to examine the program's impact on students' cognitive bias scores. While there were significant main and two-way interaction effects, these were qualified by a significant three-way interaction between Time, Intervention, and Achievement Level, F(2, 232) = 7.54, p < .001, ηp² = .061. This higher-order interaction confirms that the intervention's success in reducing cognitive bias was moderated by the students' initial achievement level. As detailed in Table 1 , the improvement was most dramatic for the low-achieving experimental group, whose scores decreased to a level much closer to their high-achieving counterparts post-intervention. This differential effect provides strong evidence that the program was particularly effective for students who began with the highest levels of cognitive bias, thereby successfully narrowing the cognitive achievement gap. The full results of the ANOVA are summarized in Table 2 . Table 2 Results of Mixed-Model ANOVA for Cognitive Bias Scores Source df F p ηp² Between-Subjects Effects Intervention 1 98.65 < .001 .459 Achievement Level 1 152.31 < .001 .568 Intervention × Achievement 1 4.88 .029 .040 Error 116 (215.44) Within-Subjects Effects Time 2 115.72 < .001 .500 Time × Intervention 2 89.91 < .001 .437 Time × Achievement 2 5.33 .006 .044 Time × Intervention × Achievement 2 7.54 < .001 .061 Error 232 (12.87) Note. Values in parentheses represent mean square errors. ηp² = partial eta-squared. The ANOVA results in Table 3 provide a detailed statistical breakdown of the factors influencing cognitive bias scores. The significant three-way interaction ( Time × Intervention × Achievement ) is the most critical finding, confirming that the program's effect was not uniform but was instead uniquely powerful for different student groups over time. This interaction statistically validates the trend observed in the descriptive data: the metacognitive intervention was most impactful for the low-achieving students, who demonstrated the greatest reduction in cognitive bias. This underscores the program's targeted effectiveness and its potential to address specific cognitive deficits where they are most pronounced. Durability of Intervention Effects To determine if the cognitive gains were retained, paired-samples t-tests were conducted on the scores of the experimental groups, comparing their post-intervention performance with their six-week follow-up performance. As detailed in Table 3 , the analysis revealed no statistically significant differences between post-test and follow-up scores for either the high- or low-achieving experimental groups on both measures ( p > .05 for all comparisons). This absence of significant decay indicates that the improvements in analytical reasoning and the reductions in cognitive bias were successfully maintained, demonstrating the lasting impact of the training. Table 3 Paired-Samples T-Test Results for Post-Intervention and Follow-Up Scores for Experimental Groups (n = 30 per group) Measure and Group Post-Test M ( SD ) Follow-Up M ( SD ) t df p Analytical Reasoning High-Achieving 85.7 (5.5) 85.1 (5.8) 1.15 29 .260 Low-Achieving 72.4 (6.8) 71.9 (7.0) 0.88 29 .386 Cognitive Bias High-Achieving 21.3 (4.9) 22.0 (5.1) -1.45 29 .158 Low-Achieving 34.1 (6.2) 35.2 (6.5) -1.89 29 .069 Note. A negative t -value for Cognitive Bias indicates a slight increase in bias from post-test to follow-up, though this change was not statistically significant. To determine if the cognitive gains were retained, paired-samples t-tests were conducted on the scores of the experimental groups, comparing their post-intervention performance with their six-week follow-up performance. As detailed in Table 3 , the analysis revealed no statistically significant differences between post-test and follow-up scores for either the high- or low-achieving experimental groups on both measures (p > .05 for all comparisons). This absence of significant decay indicates that the improvements in analytical reasoning and the reductions in cognitive bias were successfully maintained, demonstrating the lasting impact of the training. Practical Significance of Intervention Effects The practical significance of the findings was evaluated by calculating Cohen’s d effect sizes, comparing the post-intervention scores of the experimental groups against their respective control groups. As shown in Table 4 , the intervention had a powerful effect across all conditions. The effect size for the improvement in analytical reasoning was large for the high-achieving group and very large for the low-achieving group. The program's impact on reducing cognitive bias was even more pronounced, with very large effect sizes observed for both groups. Table 4 Cohen’s d Effect Sizes for Post-Intervention Scores Comparing Experimental and Control Groups Measure Achievement Level Cohen’s d Interpretation Analytical Reasoning High-Achieving 1.63 Large Low-Achieving 2.45 Very Large Cognitive Bias High-Achieving -2.18 Very Large Low-Achieving -3.27 Very Large Note. Cohen's d conventions: 0.2 = small, 0.5 = medium, 0.8 = large. A negative d value for Cognitive Bias indicates a reduction in bias for the experimental group compared to the control group. Practical Significance of Intervention Effects The practical significance of the findings was evaluated by calculating Cohen’s d effect sizes, comparing the post-intervention scores of the experimental groups against their respective control groups. As shown in Table 4 , the intervention had a powerful effect across all conditions. The effect size for the improvement in analytical reasoning was large for the high-achieving group and very large for the low-achieving group. The program's impact on reducing cognitive bias was even more pronounced, with very large effect sizes observed for both groups. Discussion The large and very large effect sizes detailed in the results underscore the clear practical significance of the metacognitive intervention. These values move beyond statistical significance to show that the program produced substantial, real-world changes in students' cognitive abilities. This study provides strong evidence that a targeted metacognitive training program can serve as a valuable tool for enhancing higher-order thinking skills and actively narrowing the cognitive achievement gap. The findings contribute to a growing body of literature highlighting the critical role of metacognitive strategies in fostering more equitable educational outcomes. The central finding is that the intervention was most effective for the low-achieving students, a result that corroborates research showing that making thinking processes explicit is particularly beneficial for closing achievement gaps [ 15 , 43 ]. While high-achieving students refined their existing skills, low-achieving students experienced a transformative impact. For them, the explicit instruction in planning, self-monitoring, and bias identification provided a cognitive toolkit they may have previously lacked [ 18 ]. The intervention effectively served as a "bridge" across the cognitive divide, making the implicit processes of effective learning accessible and allowing the low-achieving group to make substantial leaps [ 21 , 22 ]. Furthermore, the strong inverse relationship found between analytical reasoning and cognitive bias suggests that these skills are deeply intertwined. By equipping students with the ability to deconstruct arguments and reflect on their own thinking, the program helped them build a cognitive defense against the flawed judgments that stem from cognitive biases [ 30 ], positioning analytical reasoning as a protective mechanism. A crucial aspect of these findings is their durability. The follow-up data showed that the significant gains in analytical reasoning and reduced cognitive bias were retained six weeks after the program concluded. This suggests the skills were not merely memorized but were internalized, becoming a stable part of the students' approach to learning [ 34 ]. The lasting nature of these improvements highlights the value of investing in metacognitive training as a sustainable method for fostering long-term cognitive development. However, it is important to interpret the exceptionally large effect sizes with caution. Such magnitudes, while encouraging, may be specific to a population with underdeveloped baseline skills, and future research is essential to determine if these effects are replicable in other contexts. Implications for Educational Practice The findings of this study have significant implications for educational practice in Kuwait and beyond. The results strongly advocate for embedding metacognitive training not as an enrichment activity, but as a core component of the university curriculum for all students. To effectively close achievement gaps, faculty should prioritize teaching students how to learn, plan, and self-reflect [ 14 ]. Furthermore, the program serves as an effective model for targeted interventions that support struggling learners by addressing the root cognitive causes of underachievement, rather than just its symptoms. Limitations and Future Directions This study, while providing valuable insights, has several limitations that must be acknowledged. The sample was drawn from a single public college in Kuwait, which may limit the generalizability of the findings to other institutions or cultural contexts. The quasi-experimental design, while practical, cannot rule out selection bias. While practical, this design cannot entirely rule out the possibility of selection bias or other confounding variables that a true randomized controlled trial could mitigate. Additionally, the eight-week duration and six-week follow-up may not capture the full long-term effects. Future research should employ randomized controlled trials, explore the program’s impact in diverse educational settings, and assess its sustainability over extended timeframes. Investigating the integration of technology, such as AI-powered tools that provide personalized metacognitive prompts, could also offer a promising avenue for enhancing and scaling such interventions [ 28 , 29 ]. Conclusion The primary contribution of this research is its empirical demonstration that a targeted metacognitive intervention can act as a valuable cognitive lever for educational equity. The program did not simply elevate performance across the board; it most profoundly impacted the low-achieving students, enabling them to significantly narrow the pre-existing cognitive gap. This outcome offers a strong rebuttal to the notion that higher-order thinking is beyond the grasp of struggling learners. Instead, it confirms that when the implicit strategies of effective thinking are made explicit and accessible, all students can thrive. Ultimately, these findings suggest a re-evaluation of educational priorities. A pedagogical shift away from an over-reliance on content memorization toward the explicit cultivation of metacognitive skills is supported by this evidence . Metacognitive instruction should not be viewed as a remedial or enrichment activity but as a universal and essential component of modern education. For any nation committed to developing the full potential of its future generations, a strategic investment in teaching young people how to become more insightful, reflective, and discerning thinkers represents a clear and evidence-based pathway to a more capable and equitable society. Abbreviations ANOVA: Analysis of Variance ARS: Analytical Reasoning Scale CBS: Cognitive Bias Scale CGPA: Cumulative Grade Point Average d: Cohen’s d (effect size measure) df: Degrees of Freedom M: Mean ηp²: Partial eta-squared SD: Standard Deviation SPSS: Statistical Package for the Social Sciences t: t-statistic Declarations Ethics approval and consent to participate Approval for the study was obtained from the Research Ethics Committee of the College of Basic Education, The Public Authority for Applied Education and Training (Protocol #CBE-2023-017). All participants, being of legal age (19–22), provided their own written informed consent prior to participation after being fully informed of the research objectives, procedures, and their right to withdraw at any time without penalty. All collected data were anonymized to ensure confidentiality. Consent for publication Not applicable. This manuscript does not contain any individual person’s data in any form (including individual details, images, or videos). Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Competing Interests The author declares that they have no competing interests. Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Authors' contributions HA was responsible for the conception and design of the study, data collection and analysis, and the drafting and revision of the manuscript. Acknowledgements Not applicable. References Kania, N., & Kusumah, Y. S. (2025). The measurement of higher-order thinking skills: A systematic literature review. Malaysian Journal of Learning and Instruction, 22 (1), 1–30. Alhadoor, Z. A. N., Aldbyani, A., Al-mahasneh, O., Al-smadi, R. W., & Al-shboul, Q. (2023). A meta-analysis on the effectiveness of strategies and programs used to address the mathematics learning difficulties. Eurasian Journal of Mathematics, Science and Technology Education, 19 (7), Article em2288. https://doi.org/10.29333/ejmste/13317 Raoof, K., Shokri, O., Fathabadi, J., & Panaghi, L. (2024). Unpacking the underachievement of gifted students: A systematic review of internal and external factors. Heliyon, 10 (5), Article e26892. https://doi.org/10.1016/j.heliyon.2024.e26892 Sternberg, R. J., Ghahremani, M., & Ehsan, H. (2023). Combating myside bias in scientific thinking: A special challenge for the gifted. Roeper Review, 45 (2), 121–131. https://doi.org/10.1080/02783193.2023.2180424 Lau, J. Y. F. (2015). Metacognitive education: Going beyond critical thinking. In M. Davies & R. Barnett (Eds.), The Palgrave handbook of critical thinking in higher education (pp. 373–390). Palgrave Macmillan. https://doi.org/10.1057/9781137378057_22 Anthonysamy, L., Sugendran, P., & Wei, L. O. (2024). An improved metacognitive competency framework to inculcate analytical thinking among university students. Education and Information Technologies, 29 (1), 163–193. https://doi.org/10.1007/s10639-023-11910-z Walker, A., & Kettler, T. (2020). Developing critical thinking skills in high ability adolescents: Effects of a debate and argument analysis curriculum. Talent, 2 (1), 21–38. https://doi.org/10.47614/talent.2020.2.1.21-38 Hasan, B. (2024). Self-regulated learning in gifted students: A case study of a simulation-based engineering design environment [Doctoral dissertation, Middle East Technical University]. METU Open Access. http://etd.lib.metu.edu.tr/upload/12630346/index.pdf Saleh, A. I. (1997). The nexus of brain hemisphericity, personality types, temperaments, learning styles, learning strategies, gender, majors, and cultures [Doctoral dissertation, University of Northern Colorado]. ProQuest Dissertations & Theses Global. Ergen, B., & Kanadlı, S. (2017). The effect of self-regulated learning strategies on academic achievement: A meta-analysis study. Eurasian Journal of Educational Research, 17 (69), 55-74. Zimmerman, B. J., & Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, & A. C. Graesser (Eds.), Handbook of metacognition in education (pp. 299–315). Routledge. Mitsea, E., Drigas, A., & Mantas, P. (2021). Soft skills & metacognition as inclusion amplifiers in the 21st century. International Journal of Online and Biomedical Engineering, 17 (10), 115–129. https://doi.org/10.3991/ijoe.v17i10.24523 Harris, A. H., Hill, D. V., & Lenard, M. A. (2022). Promises, pitfalls, and tradeoffs in identifying gifted learners: Evidence from a curricular experiment (EdWorkingPaper No. 22-603). Annenberg Institute for School Reform at Brown University. https://doi.org/10.26300/2c2m-nn18 Nachbauer, M. (2024). All-day schools affect equity in education: Teaching factors and extended day programs associated with average achievement and socioeconomic achievement gaps. Studies in Educational Evaluation, 80 , Article 101332. https://doi.org/10.1016/j.stueduc.2023.101332 Peng, J., Sun, M., Yuan, B., Lim, C. P., & Divaharan, S. (2024). Visible thinking to support online project-based learning: Narrowing the achievement gap between high- and low-achieving students. Education and Information Technologies, 29 (1), 101–125. https://doi.org/10.1007/s10639-023-11855-3 Alanazi, A. A., Osman, K., & Halim, L. (2024). Effect of scaffolding strategies and guided discovery on higher-order thinking skills in physics education. Eurasian Journal of Mathematics, Science and Technology Education, 20 (4), Article em2425. https://doi.org/10.29333/ejmste/14352 Khalil, A., & Hellalet, N. (2024). The effect of explicit critical thinking instructions on argumentative writing achievement: An experimental investigation. International Journal of Academic and Applied Research, 8 (7), 1–10. Leferink, J. (2024). Exploring the relationship between a growth mindset and self-regulated learning: A literature review [Master’s thesis, Open University of the Netherlands]. OUNL Repository. Anantua, E. M. (2024). Exploring New York City educators’ perceptions of teaching and learning in a high-stakes environment [Doctoral dissertation, Northeastern University]. ProQuest Dissertations & Theses Global. Wandler, J., & Imbriale, W. (2017). Promoting undergraduate student self-regulation in online learning environments. Online Learning, 21 (2), 162–174. https://doi.org/10.24059/olj.v21i2.881 Chu, X., Wang, M., Spector, J. M., Chen, N. S., & Chai, C. S. (2025). Enhancing the flipped classroom model with generative AI and metaverse technologies: Insights from lag sequential and epistemic network analysis. Educational Technology Research and Development . Advance online publication. https://doi.org/10.1007/s11423-024-10368-z Myers, J. A., Arsenault, T. L., & Powell, S. R. (2025). Considerations for intensifying word-problem interventions for students with MD: A qualitative umbrella review of relevant meta-analyses. Journal of Learning Disabilities . Advance online publication. https://doi.org/10.1177/00222194241279962 Bhijakkanarin, J., & Kenaphoom, S. (2024). Enhancing analytical thinking skills in elementary students. In Sustainable Development, Humanities, and Social Sciences Conference Proceedings (pp. 20–29). Xu, W. (2024). Metacognitive strategy use in Chinese (L1) and English (L2) writing: A within-subject comparison [Doctoral dissertation, The Hong Kong Polytechnic University]. PolyU Institutional Research Archive. https://theses.lib.polyu.edu.hk/handle/200/13382 Biber, M. (2024). Pre-service mathematics teachers' views on an inquiry-based learning environment emphasising mathematical communication. Mathematics Teacher Education & Development, 26 (2), 134-148. https://doi.org/10.33578/jpfkip-v14i2.p134-148 Akmal, S., & Yuwita, F. (2024). “It’s simple and practical!”: EFL learners’ views on learning strategies in essay writing. International Journal of Research in English Education, 9 (2), 1–14. https://doi.org/10.29252/ijree.9.2.1 Smith, R. A. (2024). The influence of student voice and self-regulated learning on state testing performance [Doctoral dissertation, Northeastern University]. ProQuest Dissertations & Theses Global. Alarabi, K., Alabidi, S., Tairab, H., & Pagani, L. S. (2025). AI’s impact on science education: A study of ant and bee mindsets in UAE science classrooms. Frontiers in Education, 10 , Article 1425833. https://doi.org/10.3389/feduc.2025.1425833 Du, X., Du, M., Zhou, Z., & Bai, Y. (2025). Facilitator or hindrance? The impact of AI on university students’ higher-order thinking skills in complex problem solving. International Journal of Educational Technology in Higher Education, 22 , Article 4. https://doi.org/10.1186/s41239-024-00499-7 McDonald, S., & Fotakopoulou, O. (2024). Narrowing the digital divide in early maths: How different modes of assessment influence young children’s mathematical test scores. Early Education and Development, 35 (1), 1–21. https://doi.org/10.1080/10409289.2022.2138721 Autry, F., Brodeur, K., Doerr-Stevens, C., Goff, B., Jiménez, L. M., Lammert, J., & Wilson, A. A. (2024). Annotated bibliography of research in the teaching of English. Research in the Teaching of English, 58 (3), AB1–AB41. Schuetze, B. A. (2024). A computational model of school achievement. Educational Psychology Review, 36 (1), Article 2. https://doi.org/10.1007/s10648-023-09825-9 Formosa, M. R. (2024). Learning to learn: The transfer of metacognitive skills from a non-formal to a formal context: A student’s perspective [Master’s thesis, University of Malta]. OAR@UM. https://www.um.edu.mt/library/oar/handle/123456789/129345 Rickey, N. H. (2025). Towards an interaction theory of student self-assessment: Mapping the cognitive and affective, metacognitive, and emotional dimensions via multimodal data [Doctoral dissertation, Queen’s University]. ProQuest Dissertations & Theses Global. Naiman, J. (2025). Self-regulated learning and the formation of metacognitive skills in pre-licensure nursing students [Doctoral dissertation, The University of Arizona Global Campus]. ProQuest Dissertations & Theses Global. Krall, G. (2024). The experience of students engaging in mathematical portfolios: Contributions to mathematical identity and metacognition [Doctoral dissertation, University of Montana]. ProQuest Dissertations & Theses Global. Wu, X. Y. (2024). Unveiling the dynamics of self-regulated learning in project-based learning environments. Heliyon, 10 (5), Article e26532. https://doi.org/10.1016/j.heliyon.2024.e26532 Cahyono, B. Y., Nurisma, R. A., Ratnawati, R., & Zakiyah, Z. (2024). Teachers' perceptions of the use of self-regulated strategy development in online EFL reading instruction to foster students’ autonomy in online reading. Computer-Assisted Language Learning Electronic Journal, 25 (3), 102-123. Cañeda, M. E., Mata, L. P., & Bedrijo, R. W. (2024). Investigating students’ lived experiences in mathematics classroom activities. International Journal of Academic and Applied Research, 8 (6), 1–10. Elsner, S. (2025). Accuracy of self-evaluation in biology education: Investigating potential effects of skill practice, prompting, and physical exercise [Doctoral dissertation, University of Cologne]. KUPS. Ige, M. M. O. (2025). Enhancing self-efficacy among civil engineering undergraduates using hands-on pedagogy. International Journal of Academic and Applied Research, 9 (1), 1–10. Zhang, P. (2024). Effects of highlights and annotations on EFL learners’ reading comprehension: An application of computer-assisted interactive reading model. Computer Assisted Language Learning . Advance online publication. https://doi.org/10.1080/09588221.2024.2323568 Siller, H. S., & Ahmad, S. (2024). Analyzing the impact of collaborative learning approach on grade six students’ mathematics achievement and attitude towards mathematics. Eurasian Journal of Mathematics, Science and Technology Education, 20 (2), Article em2395. https://doi.org/10.29333/ejmste/14166 Evans, C., & Waring, M. (2024). Prioritising a self-regulatory assessment and feedback approach in higher education. In Elgar Encyclopedia of Teaching and Learning in Higher Education (pp. 305–322). Edward Elgar Publishing. https://doi.org/10.4337/9781800886254.ch25 Facione, P. A. (1990). The California Critical Thinking Skills Test: College Level (Technical Report No. 1). The California Academic Press. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185 (4157), 1124–1131. https://doi.org/10.1126/science.185.4157.1124 Additional Declarations No competing interests reported. Supplementary Files MS2Instruments.docx 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-7408588","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534743341,"identity":"29f72b10-d8d1-4a1f-99a8-70f5b1a5cafb","order_by":0,"name":"Halimah Ibrahim Alfailakawy","email":"data:image/png;base64,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","orcid":"","institution":"College of Basic Education, Kuwait","correspondingAuthor":true,"prefix":"","firstName":"Halimah","middleName":"Ibrahim","lastName":"Alfailakawy","suffix":""}],"badges":[],"createdAt":"2025-08-19 12:23:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7408588/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7408588/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":94663541,"identity":"7cfba201-9729-4747-af3a-4ed71c48b6c1","added_by":"auto","created_at":"2025-10-29 12:11:53","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":52827,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript2editedforBMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/b9471e7853d71b2b2ff6f98a.docx"},{"id":94673473,"identity":"470ea163-8f66-4715-b717-2dd8985e4725","added_by":"auto","created_at":"2025-10-29 13:41:25","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":3519,"visible":true,"origin":"","legend":"","description":"","filename":"8984751bb4d54685839b0ff51e0334b2.json","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/012b7de6847e4ff9dadebcbb.json"},{"id":94672563,"identity":"1fd4a7e7-eb12-46d0-a8a6-b07b9cccab95","added_by":"auto","created_at":"2025-10-29 13:40:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":28961,"visible":true,"origin":"","legend":"","description":"","filename":"MS2Instruments.docx","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/c27a0b0a46f3542f8bc7fc01.docx"},{"id":94663543,"identity":"04ffa2d4-dbe7-4157-8c72-8e76599f288d","added_by":"auto","created_at":"2025-10-29 12:11:54","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":121184,"visible":true,"origin":"","legend":"","description":"","filename":"8984751bb4d54685839b0ff51e0334b21enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/d4729425b44a6a07646b948e.xml"},{"id":94663546,"identity":"e92bef9c-9f1d-426b-b5c5-ce076999298b","added_by":"auto","created_at":"2025-10-29 12:11:54","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":118988,"visible":true,"origin":"","legend":"","description":"","filename":"8984751bb4d54685839b0ff51e0334b21structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/f6984d49f891fa619bf08b36.xml"},{"id":94663547,"identity":"84db9c5f-3b93-4e12-8199-eb5ec0e66308","added_by":"auto","created_at":"2025-10-29 12:11:54","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":131290,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/56c084ac12789779b0f40944.html"},{"id":96366916,"identity":"9933ac9a-9c0a-4a12-9d86-d9b4387b8e4e","added_by":"auto","created_at":"2025-11-20 10:12:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1617775,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/3a744fd2-5d16-4776-bb1c-6ca27d4efca5.pdf"},{"id":94663544,"identity":"e6afa9c5-1c61-4d4b-ac6d-764e93dcc521","added_by":"auto","created_at":"2025-10-29 12:11:54","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":28961,"visible":true,"origin":"","legend":"","description":"","filename":"MS2Instruments.docx","url":"https://assets-eu.researchsquare.com/files/rs-7408588/v1/988792ece71b69c028700494.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bridging the Achievement Gap: A Metacognitive Intervention to Enhance Analytical Reasoning and Reduce Cognitive Bias in High- and Low-Achieving Students","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe persistent achievement gap between high- and low-achieving students remains one of the most significant challenges in modern education. This gap is typically measured by grades and test scores, but its roots lie deeper, in disparities in higher-order thinking skills, including the ability to think critically and analytically \u003cstrong\u003e[1]\u003c/strong\u003e. While low-achieving students may struggle with foundational cognitive strategies \u003cstrong\u003e[2]\u003c/strong\u003e, even high-achieving students are not immune to cognitive pitfalls. High academic performance does not automatically confer sophisticated reasoning; in fact, it can mask an underlying vulnerability to cognitive biases \u003cstrong\u003e[3, 4]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eCognitive biases are systematic patterns of deviation from rational judgment that affect all thinkers. For instance, confirmation bias\u0026mdash;the tendency to favor information that supports existing beliefs\u0026mdash;can entrench a high-achiever\u0026rsquo;s overconfidence or reinforce a low-achiever\u0026rsquo;s sense of academic futility \u003cstrong\u003e[5]\u003c/strong\u003e. Such biases prevent objective analysis and impede the development of authentic analytical reasoning: the skill of systematically deconstructing problems, evaluating evidence, and forming logical conclusions \u003cstrong\u003e[6, 7]\u003c/strong\u003e. Addressing these biases is crucial for cultivating robust intellectual habits in all students.\u003c/p\u003e\n\u003cp\u003eMetacognition, or \u0026quot;thinking about thinking,\u0026quot; offers a powerful pedagogical framework to address these challenges. Metacognitive strategies are self-regulatory skills that enable individuals to plan, monitor, and evaluate their own thinking processes \u003cstrong\u003e[8, 9]\u003c/strong\u003e. For low-achieving students, explicit instruction provides a toolkit for approaching complex problems \u003cstrong\u003e[10]\u003c/strong\u003e. For high-achieving students, it serves to refine existing skills and challenge biases \u003cstrong\u003e[11]\u003c/strong\u003e. By fostering self-regulation, metacognition acts as an \u0026quot;inclusion amplifier,\u0026quot; equipping all students with the tools to manage their own learning \u003cstrong\u003e[12]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eWhile many interventions focus on content-knowledge gaps, few have examined metacognitive training as a direct mechanism for closing the gap in thinking skills by comparing its effects on both high- and low-achieving students\u0026nbsp;\u003cstrong\u003e[13]\u003c/strong\u003e. This study addresses this void by first establishing the relationship between analytical reasoning and cognitive bias. It then investigates the effectiveness of a targeted metacognitive program in enhancing analytical reasoning and reducing cognitive bias across both achievement groups. A central aim is to determine if the intervention can significantly narrow the pre-existing cognitive gap, thereby providing evidence for a pedagogical approach that not only enhances high performers\u0026rsquo; skills but also elevates the capabilities of those who struggle, fostering a more equitable educational landscape.\u003cbr\u003e\u003cstrong\u003eResearch Questions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study sought to answer the following research questions:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eWhat is the nature of the relationship between analytical reasoning and cognitive bias across high- and low-achieving undergraduate students?\u003c/li\u003e\n \u003cli\u003eHow effective is a metacognitive training program in cultivating analytical reasoning skills in high-achieving versus low-achieving students?\u003c/li\u003e\n \u003cli\u003eTo what extent does a metacognitive training program reduce cognitive bias in high-achieving and low-achieving students, and does the intervention succeed in narrowing the initial gap in these skills between the two groups?\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Literature Review","content":"\u003cp\u003eThis literature review examines the theoretical and empirical foundations for the current study. It explores the complex interplay between the academic achievement gap, the development of higher-order thinking, the pervasive influence of cognitive bias, and the potential of metacognitive interventions to foster cognitive equity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Achievement Gap and the Imperative for Higher-Order Thinking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe persistent gap in academic outcomes between high- and low-achieving students represents one of the most pressing challenges in contemporary education \u003cstrong\u003e[14, 15]\u003c/strong\u003e. This disparity, while often quantified through standardized test scores and grades, is fundamentally a reflection of deeper inequalities in the development of higher-order thinking skills \u003cstrong\u003e[16]\u003c/strong\u003e. Historically, educational systems have sometimes operated under the flawed assumption that advanced cognitive processes are the exclusive domain of high-achievers, while low-achieving students require a focus on rote memorization \u003cstrong\u003e[17]\u003c/strong\u003e. This belief perpetuates a cycle of underachievement by denying struggling students the very tools they need to overcome their academic challenges \u003cstrong\u003e[18]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eRecent pedagogical research strongly refutes this outdated view, emphasizing that all students, regardless of their current achievement level, possess the capacity to develop sophisticated thinking skills when provided with appropriate instructional support \u003cstrong\u003e[19]\u003c/strong\u003e. Educational initiatives aimed at closing the achievement gap must therefore move beyond remedial content delivery and instead focus on cultivating these essential cognitive and metacognitive competencies \u003cstrong\u003e[20]\u003c/strong\u003e. Doing so empowers low-achieving students to become active, engaged, and capable learners.\u003c/p\u003e\n\u003cp\u003eThis necessary instructional support is embodied in evidence-based strategies designed to make complex cognition accessible. Methodologies such as scaffolding and cognitive activation have proven effective in developing higher-order thinking in all learners \u003cstrong\u003e[14, 16]\u003c/strong\u003e. For instance, the \u0026apos;Visible Thinking\u0026apos; approach externalizes thought processes, allowing low-achieving students to internalize the sophisticated strategies of their peers \u003cstrong\u003e[15]\u003c/strong\u003e. These techniques function as a critical \u0026quot;bridge,\u0026quot; connecting a student\u0026rsquo;s current abilities to more advanced cognitive processes and effectively narrowing the performance gap\u0026nbsp;\u003cstrong\u003e[21, 22]\u003c/strong\u003e. Investing in such pedagogies is therefore a direct investment in educational equity, creating tangible pathways for students to cultivate the robust thinking skills essential for future success.\u003cbr\u003e\u003cstrong\u003eThe Centrality of Analytical Thinking\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnalytical thinking stands as a cornerstone of higher-order cognition: the systematic process of breaking down complex problems, identifying logical structures, and evaluating information to arrive at reasoned judgments \u003cstrong\u003e[23]\u003c/strong\u003e. Mastery of analytical thinking is a foundational skill for lifelong learning and effective problem-solving \u003cstrong\u003e[24]\u003c/strong\u003e. For low-achieving students, developing these skills is critical, as it equips them with a structured approach to tackle difficult material and overcome learning obstacles \u003cstrong\u003e[16]\u003c/strong\u003e. Educational strategies that explicitly teach and scaffold analytical reasoning have demonstrated significant success in improving outcomes for all students, particularly those who struggle academically \u003cstrong\u003e[15, 25]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eBuilding this cognitive bridge requires intentional pedagogical design that fosters the metacognitive skills underpinning effective analysis \u003cstrong\u003e[26]\u003c/strong\u003e. Students develop analytical proficiency not just by learning procedures, but by actively monitoring their thought processes, questioning assumptions, and evaluating their strategies \u003cstrong\u003e[27]\u003c/strong\u003e. A key challenge, particularly for struggling learners, is managing cognitive load. Emerging research suggests that technologies like AI can play a supportive role by automating routine tasks, thereby freeing students to engage more deeply in analytical thinking\u0026nbsp;\u003cstrong\u003e[28, 29]\u003c/strong\u003e. Ultimately, the cultivation of analytical thinking guides students to become active constructors of knowledge, capable of both dissecting and synthesizing information in meaningful ways.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe Universal Challenge of Cognitive Bias\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA formidable barrier to clear analytical thinking is the universal human susceptibility to cognitive bias\u0026mdash;inherent mental shortcuts and systematic errors that can lead to irrational conclusions. These biases are not a sign of low intelligence; they affect high- and low-achievers alike, often operating outside of conscious awareness \u003cstrong\u003e[30]\u003c/strong\u003e. For instance, confirmation bias can lead any student to uncritically accept information that aligns with their beliefs while dismissing contradictory evidence \u003cstrong\u003e[31]\u003c/strong\u003e. Computational models suggest these biases can significantly influence a student\u0026rsquo;s learning trajectory, reinforcing negative self-perceptions in low-achievers and intellectual arrogance in high-achievers \u003cstrong\u003e[32]\u003c/strong\u003e. Therefore, any effective educational intervention must include strategies to help students recognize and mitigate these biases \u003cstrong\u003e[33, 34]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eCountering ingrained cognitive biases requires cultivating metacognitive awareness\u0026mdash;the ability to turn one\u0026apos;s analytical lens inward. This self-regulation enables students to question their assumptions and actively search for disconfirming evidence, mitigating the pull of biases \u003cstrong\u003e[20, 34]\u003c/strong\u003e. While crucial for students who have not developed these habits, it is equally vital for high-achievers, whose expertise can lead to overconfidence \u003cstrong\u003e[32, 35]\u003c/strong\u003e. Explicitly teaching metacognitive strategies is thus a fundamental component of developing analytical rigor, allowing learners to override automatic, biased thinking and engage in more objective, evidence-based reasoning. Indeed, research demonstrates the pervasive and detrimental impact of biases on academic performance \u003cstrong\u003e[3, 4, 12]\u003c/strong\u003e, reinforcing the need for interventions that build these metacognitive defenses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMetacognition as a Bridge for the Cognitive Divide\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMetacognition, or \u0026ldquo;thinking about thinking,\u0026rdquo; has emerged as a high-impact pedagogical approach for addressing the cognitive dimensions of the achievement gap \u003cstrong\u003e[36, 37]\u003c/strong\u003e. It involves metacognitive knowledge (awareness of one\u0026rsquo;s cognition) and regulation (the ability to plan, monitor, and evaluate one\u0026apos;s thinking) \u003cstrong\u003e[20, 38]\u003c/strong\u003e. By making these internal processes explicit, metacognitive instruction provides a transparent roadmap for learning that research overwhelmingly shows is powerful for low-achieving students \u003cstrong\u003e[39, 40]\u003c/strong\u003e. When struggling learners are explicitly taught how to plan, monitor, and reflect, they gain a sense of agency that is transformative \u003cstrong\u003e[18, 35, 41]\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eCrucially, metacognitive strategies serve as a powerful bridge to narrow the cognitive gap between high- and low-achievers \u003cstrong\u003e[21, 28, 42]\u003c/strong\u003e. While high-achieving students benefit from refining their skills, the relative gains are often greatest for their lower-performing peers, reducing performance disparities \u003cstrong\u003e[43]\u003c/strong\u003e. The deliberate cultivation of metacognitive awareness is thus a fundamental tool for creating more equitable learning environments \u003cstrong\u003e[22, 44]\u003c/strong\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAlthough the literature highlights metacognition\u0026apos;s efficacy in promoting self-regulated learning \u003cstrong\u003e[10, 11, 33]\u003c/strong\u003e, few studies have implemented culturally specific interventions or directly compared outcomes across achievement levels within a single program\u0026nbsp;\u003cstrong\u003e[6, 15]\u003c/strong\u003e. The present study extends this work by implementing and evaluating a targeted metacognitive program in a Kuwaiti undergraduate setting, with the specific aim of enhancing analytical reasoning and reducing cognitive bias among both high- and low-achieving students to examine its potential to foster cognitive equity.\u003cbr\u003e\u003cstrong\u003eHypothese\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the literature review, the following hypotheses were formulated for this study:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eA significant inverse relationship exists between analytical reasoning skills and susceptibility to cognitive bias across both high- and low-achieving college students in the State of Kuwait.\u003c/li\u003e\n \u003cli\u003eThe metacognitive training program will lead to a significant improvement in analytical reasoning skills for both high- and low-achieving college students. It is further hypothesized that the magnitude of improvement will be greater for the low-achieving group, thereby narrowing the initial achievement gap in this skill.\u003c/li\u003e\n \u003cli\u003eFollowing the intervention, both high- and low-achieving students will demonstrate a significant reduction in cognitive bias. The reduction is expected to be more pronounced in the low-achieving group, leading to a convergence in scores between the two achievement groups.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eParticipants and Design\u003c/h2\u003e\u003cp\u003eThis study employed a quasi-experimental, pre-test-post-test control group design with academic achievement level (high vs. low) as a between-subjects factor. Participants were 120 undergraduate students (60 male, 60 female; ages 19\u0026ndash;22) from the College of Basic Education in Kuwait. Students were first stratified into two groups based on their cumulative grade point average (CGPA): high-achieving (n\u0026thinsp;=\u0026thinsp;60; top quartile) and low-achieving (n\u0026thinsp;=\u0026thinsp;60; bottom quartile). Within each stratum, participants were randomly assigned to either an experimental group receiving the metacognitive intervention or a control group attending their regular tutorial sessions. This process resulted in four groups of 30 students each: High-Achieving Experimental, Low-Achieving Experimental, High-Achieving Control, and Low-Achieving Control.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eInstruments\u003c/h2\u003e\u003cp\u003eAll study materials were translated into Arabic and subsequently back-translated to ensure cultural and linguistic appropriateness.\u003c/p\u003e\u003cp\u003e\u003cb\u003eAnalytical Reasoning Scale (ARS).\u003c/b\u003e The ARS is a 30-item multiple-choice instrument adapted from established frameworks of critical thinking assessment [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] to assess analytical reasoning across three subscales: Deconstructive Analysis, Synthetic Reasoning, and Applied Reasoning. Scores range from 0 to 30. A pilot study confirmed strong psychometric properties, including excellent internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.87), strong test-retest reliability (r\u0026thinsp;=\u0026thinsp;.85), and evidence of content, construct, and criterion validity. \u003cb\u003eThis scale has been published elsewhere, and the appropriate reference is provided in the manuscript\u003c/b\u003e [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cb\u003eCognitive Bias Scale (CBS).\u003c/b\u003e Developed for this study based on the seminal work on cognitive heuristics [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], the CBS is a 15-item instrument measuring susceptibility to Confirmation, Anchoring, and Overconfidence biases. Items are scored on a 4-point scale, with total scores ranging from 0 to 45 (higher scores indicate greater bias). Validation studies confirmed good internal consistency (Cronbach\u0026rsquo;s α\u0026thinsp;=\u0026thinsp;.81), strong test-retest reliability (r\u0026thinsp;=\u0026thinsp;.82), and evidence of content, construct, and criterion validity. \u003cb\u003eAs this scale was developed for the current study, an English language version has been included as a supplementary file and is cited here as Supplementary File 1.\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eMetacognitive Training Program.\u003c/b\u003e The intervention was a structured, eight-week Metacognitive Training Program consisting of weekly 60-minute sessions. Grounded in self-regulated learning theory, the program was designed to enhance analytical reasoning and reduce cognitive bias by teaching explicit metacognitive strategies. The curriculum focused on four core, scaffolded skill areas: goal setting and planning, which involved deconstructing complex tasks; self-monitoring and reflection to assess understanding and question thinking processes; cognitive bias awareness, where students learned to identify and challenge common biases; and strategy evaluation, guiding them to reflect on and adapt their learning approaches.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eProcedure and Data Analysis Plan\u003c/h2\u003e\u003cp\u003e Following institutional approval, all participants provided written informed consent. Data were collected at three time points: pre-intervention, post-intervention (immediately after the 8-week program), and a six-week follow-up.\u003c/p\u003e\u003cp\u003eAll quantitative data were analyzed using IBM SPSS Statistics (Version 28) with an alpha level of α\u0026thinsp;=\u0026thinsp;.05. To test the first hypothesis regarding the relationship between analytical reasoning and cognitive bias, a Pearson correlation coefficient was calculated using pre-intervention data. To test the second and third hypotheses on the intervention's effectiveness and its impact on the achievement gap, a 2 (Intervention: experimental, control) x 2 (Achievement: high, low) x 3 (Time: pre-test, post-test, follow-up) mixed-model Analysis of Variance (ANOVA) was conducted for both the ARS and CBS scores. This analysis allowed for the examination of the crucial three-way interaction to determine if the program's effectiveness differed between high- and low-achieving students. Significant effects were followed by post-hoc tests with Bonferroni correction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEthical Considerations\u003c/h2\u003e\u003cp\u003e This study was conducted in strict adherence to established ethical principles. Full approval was obtained from the institutional review board at the College of Basic Education (Protocol #CBE-2023-017). All participants, being of legal age, provided written informed consent after being briefed on the study's purpose and their right to withdraw at any time without penalty. To maintain confidentiality, participants were assigned pseudonyms, and all data were coded and stored securely. Findings are reported only in aggregate form.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study investigated the effectiveness of a metacognitive training program on the analytical reasoning and cognitive bias of high- and low-achieving undergraduate students in Kuwait. The findings provide strong evidence for the program's effectiveness, demonstrating a significant inverse relationship between analytical reasoning and cognitive bias and showing that the intervention successfully enhanced skills and narrowed the achievement gap.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eRelationship Between Analytical Reasoning and Cognitive Bias\u003c/h2\u003e\u003cp\u003eAn initial analysis of pre-intervention data from all 120 participants was conducted to test the relationship between students' analytical reasoning abilities and their susceptibility to cognitive bias. As hypothesized, a strong, statistically significant negative correlation was observed between the total scores on the Analytical Reasoning Scale and the Cognitive Bias Scale, r(118)\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.71, p\u0026thinsp;\u0026lt;\u0026thinsp;.001. This robust inverse relationship indicates that students with stronger analytical reasoning skills were significantly less prone to cognitive biases. The pattern held across all sub-dimensions, with total cognitive bias negatively correlated with Deconstructive Analysis (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.69, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), Synthetic Reasoning (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.65, p\u0026thinsp;\u0026lt;\u0026thinsp;.01), and Applied Reasoning (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;.74, p\u0026thinsp;\u0026lt;\u0026thinsp;.01).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eImpact of Metacognitive Training on Analytical Reasoning\u003c/h2\u003e\u003cp\u003eTo assess the training program's effectiveness, a 2 (Intervention: Experimental, Control) x 2 (Achievement Level: High, Low) x 3 (Time: Pre-test, Post-test, Follow-up) mixed-model ANOVA was conducted on the analytical reasoning scores. The results revealed a significant three-way interaction between Time, Intervention, and Achievement Level, F(2, 232)\u0026thinsp;=\u0026thinsp;8.92, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, ηp\u0026sup2; = .071. This finding indicates that the intervention's impact on analytical reasoning development over time was significantly different for high- and low-achieving students. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the low-achieving experimental group demonstrated the most substantial gains, effectively narrowing the initial gap between them and their high-achieving peers. This powerful interaction effect highlights the program's success in promoting cognitive equity, whereas the control groups showed no significant changes over time.\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\u003e\u003cem\u003eMean Scores and Standard Deviations for Analytical Reasoning and Cognitive Bias by Group and Time\u003c/em\u003e\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eMeasure and Group\u003c/p\u003e\u003cp\u003eAnalytical Reasoning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-Achieving\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow-Achieving\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eM (SD)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM (SD)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.2 (6.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.1 (7.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74.9 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.8 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.7 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.4 (6.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.5 (6.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.1 (7.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFollow-Up\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.1 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.9 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e75.2 (6.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e54.9 (7.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCognitive Bias\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePre-Test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.5 (5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.8 (6.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.1 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.2 (7.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003ePost-Test\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.3 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e34.1 (6.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.8 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e55.9 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cem\u003eFollow-Up\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExperimental (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e22.0 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.2 (6.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eControl (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33.4 (5.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e56.5 (7.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote.\u003c/em\u003e For Analytical Reasoning, higher scores indicate better performance. For Cognitive Bias, lower scores indicate better performance (i.e., less bias).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe results detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e clearly illustrate the dual impact of the metacognitive intervention: it not only enhanced cognitive skills for all participating students but also served as a powerful tool for promoting educational equity. The most dramatic gains are visible in the low-achieving experimental group, whose post-intervention scores in both analytical reasoning and cognitive bias shifted significantly closer to those of their high-achieving peers. This convergence demonstrates that the program was particularly effective for students who started with the greatest need, successfully narrowing the initial cognitive achievement gap and highlighting the intervention's potential to foster more equitable learning outcomes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eImpact of Metacognitive Training on Cognitive Bias\u003c/h2\u003e\u003cp\u003eA corresponding 2 (Intervention) x 2 (Achievement Level) x 3 (Time) mixed-model ANOVA was conducted to examine the program's impact on students' cognitive bias scores. While there were significant main and two-way interaction effects, these were qualified by a significant three-way interaction between Time, Intervention, and Achievement Level, F(2, 232)\u0026thinsp;=\u0026thinsp;7.54, p\u0026thinsp;\u0026lt;\u0026thinsp;.001, ηp\u0026sup2; = .061. This higher-order interaction confirms that the intervention's success in reducing cognitive bias was moderated by the students' initial achievement level.\u003c/p\u003e\u003cp\u003eAs detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the improvement was most dramatic for the low-achieving experimental group, whose scores decreased to a level much closer to their high-achieving counterparts post-intervention. This differential effect provides strong evidence that the program was particularly effective for students who began with the highest levels of cognitive bias, thereby successfully narrowing the cognitive achievement gap. The full results of the ANOVA are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eResults of Mixed-Model ANOVA for Cognitive Bias Scores\u003c/em\u003e\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=\"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\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\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eηp\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBetween-Subjects Effects\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e98.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.459\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAchievement Level\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e152.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIntervention \u0026times; Achievement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.040\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(215.44)\u003c/p\u003e\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\u003e\u003cb\u003eWithin-Subjects Effects\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\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\u003eTime\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.500\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime \u0026times; Intervention\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e89.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime \u0026times; Achievement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.044\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTime \u0026times; Intervention \u0026times; Achievement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.061\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e(12.87)\u003c/p\u003e\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\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Values in parentheses represent mean square errors. ηp\u0026sup2; = partial eta-squared.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe ANOVA results in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e provide a detailed statistical breakdown of the factors influencing cognitive bias scores. The significant three-way interaction (\u003cem\u003eTime \u0026times; Intervention \u0026times; Achievement\u003c/em\u003e) is the most critical finding, confirming that the program's effect was not uniform but was instead uniquely powerful for different student groups over time. This interaction statistically validates the trend observed in the descriptive data: the metacognitive intervention was most impactful for the low-achieving students, who demonstrated the greatest reduction in cognitive bias. This underscores the program's targeted effectiveness and its potential to address specific cognitive deficits where they are most pronounced.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eDurability of Intervention Effects\u003c/h2\u003e\u003cp\u003eTo determine if the cognitive gains were retained, paired-samples t-tests were conducted on the scores of the experimental groups, comparing their post-intervention performance with their six-week follow-up performance. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the analysis revealed no statistically significant differences between post-test and follow-up scores for either the high- or low-achieving experimental groups on both measures (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;.05 for all comparisons). This absence of significant decay indicates that the improvements in analytical reasoning and the reductions in cognitive bias were successfully maintained, demonstrating the lasting impact of the training.\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\u003e\u003cem\u003ePaired-Samples T-Test Results for Post-Intervention and Follow-Up Scores for Experimental Groups (n\u0026thinsp;=\u0026thinsp;30 per group)\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMeasure and Group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePost-Test\u0026nbsp;\u003cem\u003eM\u003c/em\u003e\u0026nbsp;(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFollow-Up\u0026nbsp;\u003cem\u003eM\u003c/em\u003e\u0026nbsp;(\u003cem\u003eSD\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003edf\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnalytical Reasoning\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e85.7 (5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e85.1 (5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.260\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.4 (6.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e71.9 (7.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.386\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCognitive Bias\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e21.3 (4.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.0 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.158\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34.1 (6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e35.2 (6.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-1.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNote.\u003c/em\u003e A negative \u003cem\u003et\u003c/em\u003e-value for Cognitive Bias indicates a slight increase in bias from post-test to follow-up, though this change was not statistically significant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTo determine if the cognitive gains were retained, paired-samples t-tests were conducted on the scores of the experimental groups, comparing their post-intervention performance with their six-week follow-up performance. As detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the analysis revealed no statistically significant differences between post-test and follow-up scores for either the high- or low-achieving experimental groups on both measures (p\u0026thinsp;\u0026gt;\u0026thinsp;.05 for all comparisons). This absence of significant decay indicates that the improvements in analytical reasoning and the reductions in cognitive bias were successfully maintained, demonstrating the lasting impact of the training.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePractical Significance of Intervention Effects\u003c/h2\u003e\u003cp\u003eThe practical significance of the findings was evaluated by calculating Cohen\u0026rsquo;s d effect sizes, comparing the post-intervention scores of the experimental groups against their respective control groups. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the intervention had a powerful effect across all conditions. The effect size for the improvement in analytical reasoning was large for the high-achieving group and very large for the low-achieving group. The program's impact on reducing cognitive bias was even more pronounced, with very large effect sizes observed for both groups.\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\u003e\u003cem\u003eCohen\u0026rsquo;s d Effect Sizes for Post-Intervention Scores Comparing Experimental and Control Groups\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003eMeasure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAchievement Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCohen\u0026rsquo;s\u0026nbsp;\u003cem\u003ed\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eInterpretation\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnalytical Reasoning\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLarge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery Large\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCognitive Bias\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery Large\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow-Achieving\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVery Large\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"1\" nameend=\"c5\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote.\u003c/em\u003e Cohen's \u003cem\u003ed\u003c/em\u003e conventions: 0.2\u0026thinsp;=\u0026thinsp;small, 0.5\u0026thinsp;=\u0026thinsp;medium, 0.8\u0026thinsp;=\u0026thinsp;large. A negative \u003cem\u003ed\u003c/em\u003e value for Cognitive Bias indicates a reduction in bias for the experimental group compared to the control group.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003ePractical Significance of Intervention Effects\u003c/h2\u003e\u003cp\u003eThe practical significance of the findings was evaluated by calculating Cohen\u0026rsquo;s \u003cem\u003ed\u003c/em\u003e effect sizes, comparing the post-intervention scores of the experimental groups against their respective control groups. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the intervention had a powerful effect across all conditions. The effect size for the improvement in analytical reasoning was large for the high-achieving group and very large for the low-achieving group. The program's impact on reducing cognitive bias was even more pronounced, with very large effect sizes observed for both groups.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe large and very large effect sizes detailed in the results underscore the \u003cb\u003eclear\u003c/b\u003e practical significance of the metacognitive intervention. These values move beyond statistical significance to show that the program produced substantial, real-world changes in students' cognitive abilities. This study provides \u003cb\u003estrong\u003c/b\u003e evidence that a targeted metacognitive training program can serve as a \u003cb\u003evaluable\u003c/b\u003e tool for enhancing higher-order thinking skills and actively narrowing the cognitive achievement gap. The findings contribute to a growing body of literature highlighting the critical role of metacognitive strategies in fostering more equitable educational outcomes.\u003c/p\u003e\u003cp\u003eThe central finding is that the intervention was most effective for the low-achieving students, a result that corroborates research showing that making thinking processes explicit is particularly beneficial for closing achievement gaps [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. While high-achieving students refined their existing skills, low-achieving students experienced a transformative impact. For them, the explicit instruction in planning, self-monitoring, and bias identification provided a cognitive toolkit they may have previously lacked [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe intervention effectively served as a \"bridge\" across the cognitive divide, making the implicit processes of effective learning accessible and allowing the low-achieving group to make substantial leaps [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Furthermore, the strong inverse relationship found between analytical reasoning and cognitive bias suggests that these skills are deeply intertwined. By equipping students with the ability to deconstruct arguments and reflect on their own thinking, the program helped them build a cognitive defense against the flawed judgments that stem from cognitive biases [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], positioning analytical reasoning as a protective mechanism.\u003c/p\u003e\u003cp\u003eA crucial aspect of these findings is their durability. The follow-up data showed that the significant gains in analytical reasoning and reduced cognitive bias were retained six weeks after the program concluded. This suggests the skills were not merely memorized but were internalized, becoming a stable part of the students' approach to learning [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The lasting nature of these improvements highlights the value of investing in metacognitive training as a sustainable method for fostering long-term cognitive development.\u003c/p\u003e\u003cp\u003e\u003cb\u003eHowever, it is important to interpret the exceptionally large effect sizes with caution.\u003c/b\u003e Such magnitudes, while encouraging, may be specific to a population with underdeveloped baseline skills, and future research is essential to determine if these effects are replicable in other contexts.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eImplications for Educational Practice\u003c/h2\u003e\u003cp\u003eThe findings of this study have significant implications for educational practice in Kuwait and beyond. The results strongly advocate for embedding metacognitive training not as an enrichment activity, but as a core component of the university curriculum for all students. To effectively close achievement gaps, faculty should prioritize teaching students how to learn, plan, and self-reflect [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Furthermore, the program serves as an effective model for targeted interventions that support struggling learners by addressing the root cognitive causes of underachievement, rather than just its symptoms.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e\u003cp\u003e\u003cb\u003eThis study, while providing valuable insights, has several limitations that must be acknowledged.\u003c/b\u003e The sample was drawn from a single public college in Kuwait, which may limit the generalizability of the findings to other institutions or cultural contexts. The quasi-experimental design, while practical, cannot rule out selection bias. \u003cb\u003eWhile practical, this design cannot entirely rule out the possibility of selection bias or other confounding variables that a true randomized controlled trial could mitigate.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdditionally, the eight-week duration and six-week follow-up may not capture the full long-term effects. Future research should employ randomized controlled trials, explore the program\u0026rsquo;s impact in diverse educational settings, and assess its sustainability over extended timeframes. Investigating the integration of technology, such as AI-powered tools that provide personalized metacognitive prompts, could also offer a promising avenue for enhancing and scaling such interventions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003e\u003cb\u003eThe primary contribution of this research is its empirical demonstration that a targeted metacognitive intervention can act as a valuable cognitive lever for educational equity.\u003c/b\u003e The program did not simply elevate performance across the board; it most profoundly impacted the low-achieving students, enabling them to significantly narrow the pre-existing cognitive gap. This outcome offers a \u003cb\u003estrong\u003c/b\u003e rebuttal to the notion that higher-order thinking is beyond the grasp of struggling learners. Instead, it confirms that when the implicit strategies of effective thinking are made explicit and accessible, all students can thrive.\u003c/p\u003e\u003cp\u003eUltimately, these findings \u003cb\u003esuggest\u003c/b\u003e a re-evaluation of educational priorities. A pedagogical shift away from an over-reliance on content memorization toward the explicit cultivation of metacognitive skills is \u003cb\u003esupported by this evidence\u003c/b\u003e. Metacognitive instruction should not be viewed as a remedial or enrichment activity but as a universal and essential component of modern education. For any nation committed to developing the full potential of its future generations, a strategic investment in teaching young people how to become more insightful, reflective, and discerning thinkers represents a clear and evidence-based pathway to a more capable and equitable society.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eANOVA: Analysis of Variance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eARS: Analytical Reasoning Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCBS: Cognitive Bias Scale\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCGPA: Cumulative Grade Point Average\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ed: Cohen\u0026rsquo;s d (effect size measure)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003edf: Degrees of Freedom\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eM: Mean\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026eta;p\u0026sup2;: Partial eta-squared\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSD: Standard Deviation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSPSS: Statistical Package for the Social Sciences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003et: t-statistic\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Approval for the study was obtained from the Research Ethics Committee of the College of Basic Education, The Public Authority for Applied Education and Training (Protocol #CBE-2023-017). All participants, being of legal age (19\u0026ndash;22), provided their own written informed consent prior to participation after being fully informed of the research objectives, procedures, and their right to withdraw at any time without penalty. All collected data were anonymized to ensure confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable. This manuscript does not contain any individual person\u0026rsquo;s data in any form (including individual details, images, or videos).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The author declares that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;HA was responsible for the conception and design of the study, data collection and analysis, and the drafting and revision of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col start=\"1\" type=\"1\"\u003e\n\u003cli\u003eKania, N., \u0026amp; Kusumah, Y. S. (2025). The measurement of higher-order thinking skills: A systematic literature review. \u003cem\u003eMalaysian Journal of Learning and Instruction, 22\u003c/em\u003e(1), 1\u0026ndash;30.\u003c/li\u003e\n\u003cli\u003eAlhadoor, Z. A. N., Aldbyani, A., Al-mahasneh, O., Al-smadi, R. W., \u0026amp; Al-shboul, Q. (2023). A meta-analysis on the effectiveness of strategies and programs used to address the mathematics learning difficulties. \u003cem\u003eEurasian Journal of Mathematics, Science and Technology Education, 19\u003c/em\u003e(7), Article em2288. https://doi.org/10.29333/ejmste/13317\u003c/li\u003e\n\u003cli\u003eRaoof, K., Shokri, O., Fathabadi, J., \u0026amp; Panaghi, L. (2024). Unpacking the underachievement of gifted students: A systematic review of internal and external factors. \u003cem\u003eHeliyon, 10\u003c/em\u003e(5), Article e26892. https://doi.org/10.1016/j.heliyon.2024.e26892\u003c/li\u003e\n\u003cli\u003eSternberg, R. J., Ghahremani, M., \u0026amp; Ehsan, H. (2023). Combating myside bias in scientific thinking: A special challenge for the gifted. \u003cem\u003eRoeper Review, 45\u003c/em\u003e(2), 121\u0026ndash;131. https://doi.org/10.1080/02783193.2023.2180424\u003c/li\u003e\n\u003cli\u003eLau, J. Y. F. (2015). Metacognitive education: Going beyond critical thinking. In M. Davies \u0026amp; R. Barnett (Eds.), \u003cem\u003eThe Palgrave handbook of critical thinking in higher education\u003c/em\u003e (pp. 373\u0026ndash;390). Palgrave Macmillan. https://doi.org/10.1057/9781137378057_22\u003c/li\u003e\n\u003cli\u003eAnthonysamy, L., Sugendran, P., \u0026amp; Wei, L. O. (2024). An improved metacognitive competency framework to inculcate analytical thinking among university students. \u003cem\u003eEducation and Information Technologies, 29\u003c/em\u003e(1), 163\u0026ndash;193. https://doi.org/10.1007/s10639-023-11910-z\u003c/li\u003e\n\u003cli\u003eWalker, A., \u0026amp; Kettler, T. (2020). Developing critical thinking skills in high ability adolescents: Effects of a debate and argument analysis curriculum. \u003cem\u003eTalent, 2\u003c/em\u003e(1), 21\u0026ndash;38. https://doi.org/10.47614/talent.2020.2.1.21-38\u003c/li\u003e\n\u003cli\u003eHasan, B. (2024). \u003cem\u003eSelf-regulated learning in gifted students: A case study of a simulation-based engineering design environment\u003c/em\u003e [Doctoral dissertation, Middle East Technical University]. METU Open Access. http://etd.lib.metu.edu.tr/upload/12630346/index.pdf\u003c/li\u003e\n\u003cli\u003eSaleh, A. I. (1997). \u003cem\u003eThe nexus of brain hemisphericity, personality types, temperaments, learning styles, learning strategies, gender, majors, and cultures\u003c/em\u003e [Doctoral dissertation, University of Northern Colorado]. ProQuest Dissertations \u0026amp; Theses Global.\u003c/li\u003e\n\u003cli\u003eErgen, B., \u0026amp; Kanadlı, S. (2017). The effect of self-regulated learning strategies on academic achievement: A meta-analysis study. \u003cem\u003eEurasian Journal of Educational Research, 17\u003c/em\u003e(69), 55-74.\u003c/li\u003e\n\u003cli\u003eZimmerman, B. J., \u0026amp; Moylan, A. R. (2009). Self-regulation: Where metacognition and motivation intersect. In D. J. Hacker, J. Dunlosky, \u0026amp; A. C. Graesser (Eds.), \u003cem\u003eHandbook of metacognition in education\u003c/em\u003e (pp. 299\u0026ndash;315). Routledge.\u003c/li\u003e\n\u003cli\u003eMitsea, E., Drigas, A., \u0026amp; Mantas, P. (2021). Soft skills \u0026amp; metacognition as inclusion amplifiers in the 21st century. \u003cem\u003eInternational Journal of Online and Biomedical Engineering, 17\u003c/em\u003e(10), 115\u0026ndash;129. https://doi.org/10.3991/ijoe.v17i10.24523\u003c/li\u003e\n\u003cli\u003eHarris, A. H., Hill, D. V., \u0026amp; Lenard, M. A. (2022). \u003cem\u003ePromises, pitfalls, and tradeoffs in identifying gifted learners: Evidence from a curricular experiment\u003c/em\u003e (EdWorkingPaper No. 22-603). Annenberg Institute for School Reform at Brown University. https://doi.org/10.26300/2c2m-nn18\u003c/li\u003e\n\u003cli\u003eNachbauer, M. (2024). All-day schools affect equity in education: Teaching factors and extended day programs associated with average achievement and socioeconomic achievement gaps. \u003cem\u003eStudies in Educational Evaluation, 80\u003c/em\u003e, Article 101332. https://doi.org/10.1016/j.stueduc.2023.101332\u003c/li\u003e\n\u003cli\u003ePeng, J., Sun, M., Yuan, B., Lim, C. P., \u0026amp; Divaharan, S. (2024). Visible thinking to support online project-based learning: Narrowing the achievement gap between high- and low-achieving students. \u003cem\u003eEducation and Information Technologies, 29\u003c/em\u003e(1), 101\u0026ndash;125. https://doi.org/10.1007/s10639-023-11855-3\u003c/li\u003e\n\u003cli\u003eAlanazi, A. A., Osman, K., \u0026amp; Halim, L. (2024). Effect of scaffolding strategies and guided discovery on higher-order thinking skills in physics education. \u003cem\u003eEurasian Journal of Mathematics, Science and Technology Education, 20\u003c/em\u003e(4), Article em2425. https://doi.org/10.29333/ejmste/14352\u003c/li\u003e\n\u003cli\u003eKhalil, A., \u0026amp; Hellalet, N. (2024). The effect of explicit critical thinking instructions on argumentative writing achievement: An experimental investigation. \u003cem\u003eInternational Journal of Academic and Applied Research, 8\u003c/em\u003e(7), 1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eLeferink, J. (2024). \u003cem\u003eExploring the relationship between a growth mindset and self-regulated learning: A literature review\u003c/em\u003e [Master\u0026rsquo;s thesis, Open University of the Netherlands]. OUNL Repository.\u003c/li\u003e\n\u003cli\u003eAnantua, E. M. (2024). \u003cem\u003eExploring New York City educators\u0026rsquo; perceptions of teaching and learning in a high-stakes environment\u003c/em\u003e [Doctoral dissertation, Northeastern University]. ProQuest Dissertations \u0026amp; Theses Global.\u003c/li\u003e\n\u003cli\u003eWandler, J., \u0026amp; Imbriale, W. (2017). Promoting undergraduate student self-regulation in online learning environments. \u003cem\u003eOnline Learning, 21\u003c/em\u003e(2), 162\u0026ndash;174. https://doi.org/10.24059/olj.v21i2.881\u003c/li\u003e\n\u003cli\u003eChu, X., Wang, M., Spector, J. M., Chen, N. S., \u0026amp; Chai, C. S. (2025). Enhancing the flipped classroom model with generative AI and metaverse technologies: Insights from lag sequential and epistemic network analysis. \u003cem\u003eEducational Technology Research and Development\u003c/em\u003e. Advance online publication. https://doi.org/10.1007/s11423-024-10368-z\u003c/li\u003e\n\u003cli\u003eMyers, J. A., Arsenault, T. L., \u0026amp; Powell, S. R. (2025). Considerations for intensifying word-problem interventions for students with MD: A qualitative umbrella review of relevant meta-analyses. \u003cem\u003eJournal of Learning Disabilities\u003c/em\u003e. Advance online publication. https://doi.org/10.1177/00222194241279962\u003c/li\u003e\n\u003cli\u003eBhijakkanarin, J., \u0026amp; Kenaphoom, S. (2024). Enhancing analytical thinking skills in elementary students. In \u003cem\u003eSustainable Development, Humanities, and Social Sciences Conference Proceedings\u003c/em\u003e (pp. 20\u0026ndash;29).\u003c/li\u003e\n\u003cli\u003eXu, W. (2024). \u003cem\u003eMetacognitive strategy use in Chinese (L1) and English (L2) writing: A within-subject comparison\u003c/em\u003e [Doctoral dissertation, The Hong Kong Polytechnic University]. PolyU Institutional Research Archive. https://theses.lib.polyu.edu.hk/handle/200/13382\u003c/li\u003e\n\u003cli\u003eBiber, M. (2024). Pre-service mathematics teachers\u0026apos; views on an inquiry-based learning environment emphasising mathematical communication. \u003cem\u003eMathematics Teacher Education \u0026amp; Development, 26\u003c/em\u003e(2), 134-148. https://doi.org/10.33578/jpfkip-v14i2.p134-148\u003c/li\u003e\n\u003cli\u003eAkmal, S., \u0026amp; Yuwita, F. (2024). \u0026ldquo;It\u0026rsquo;s simple and practical!\u0026rdquo;: EFL learners\u0026rsquo; views on learning strategies in essay writing. \u003cem\u003eInternational Journal of Research in English Education, 9\u003c/em\u003e(2), 1\u0026ndash;14. https://doi.org/10.29252/ijree.9.2.1\u003c/li\u003e\n\u003cli\u003eSmith, R. A. (2024). \u003cem\u003eThe influence of student voice and self-regulated learning on state testing performance\u003c/em\u003e [Doctoral dissertation, Northeastern University]. ProQuest Dissertations \u0026amp; Theses Global.\u003c/li\u003e\n\u003cli\u003eAlarabi, K., Alabidi, S., Tairab, H., \u0026amp; Pagani, L. S. (2025). AI\u0026rsquo;s impact on science education: A study of ant and bee mindsets in UAE science classrooms. \u003cem\u003eFrontiers in Education, 10\u003c/em\u003e, Article 1425833. https://doi.org/10.3389/feduc.2025.1425833\u003c/li\u003e\n\u003cli\u003eDu, X., Du, M., Zhou, Z., \u0026amp; Bai, Y. (2025). Facilitator or hindrance? The impact of AI on university students\u0026rsquo; higher-order thinking skills in complex problem solving. \u003cem\u003eInternational Journal of Educational Technology in Higher Education, 22\u003c/em\u003e, Article 4. https://doi.org/10.1186/s41239-024-00499-7\u003c/li\u003e\n\u003cli\u003eMcDonald, S., \u0026amp; Fotakopoulou, O. (2024). Narrowing the digital divide in early maths: How different modes of assessment influence young children\u0026rsquo;s mathematical test scores. \u003cem\u003eEarly Education and Development, 35\u003c/em\u003e(1), 1\u0026ndash;21. https://doi.org/10.1080/10409289.2022.2138721\u003c/li\u003e\n\u003cli\u003eAutry, F., Brodeur, K., Doerr-Stevens, C., Goff, B., Jim\u0026eacute;nez, L. M., Lammert, J., \u0026amp; Wilson, A. A. (2024). Annotated bibliography of research in the teaching of English. \u003cem\u003eResearch in the Teaching of English, 58\u003c/em\u003e(3), AB1\u0026ndash;AB41.\u003c/li\u003e\n\u003cli\u003eSchuetze, B. A. (2024). A computational model of school achievement. \u003cem\u003eEducational Psychology Review, 36\u003c/em\u003e(1), Article 2. https://doi.org/10.1007/s10648-023-09825-9\u003c/li\u003e\n\u003cli\u003eFormosa, M. R. (2024). \u003cem\u003eLearning to learn: The transfer of metacognitive skills from a non-formal to a formal context: A student\u0026rsquo;s perspective\u003c/em\u003e [Master\u0026rsquo;s thesis, University of Malta]. OAR@UM. https://www.um.edu.mt/library/oar/handle/123456789/129345\u003c/li\u003e\n\u003cli\u003eRickey, N. H. (2025). \u003cem\u003eTowards an interaction theory of student self-assessment: Mapping the cognitive and affective, metacognitive, and emotional dimensions via multimodal data\u003c/em\u003e [Doctoral dissertation, Queen\u0026rsquo;s University]. ProQuest Dissertations \u0026amp; Theses Global.\u003c/li\u003e\n\u003cli\u003eNaiman, J. (2025). \u003cem\u003eSelf-regulated learning and the formation of metacognitive skills in pre-licensure nursing students\u003c/em\u003e [Doctoral dissertation, The University of Arizona Global Campus]. ProQuest Dissertations \u0026amp; Theses Global.\u003c/li\u003e\n\u003cli\u003eKrall, G. (2024). \u003cem\u003eThe experience of students engaging in mathematical portfolios: Contributions to mathematical identity and metacognition\u003c/em\u003e [Doctoral dissertation, University of Montana]. ProQuest Dissertations \u0026amp; Theses Global.\u003c/li\u003e\n\u003cli\u003eWu, X. Y. (2024). Unveiling the dynamics of self-regulated learning in project-based learning environments. \u003cem\u003eHeliyon, 10\u003c/em\u003e(5), Article e26532. https://doi.org/10.1016/j.heliyon.2024.e26532\u003c/li\u003e\n\u003cli\u003eCahyono, B. Y., Nurisma, R. A., Ratnawati, R., \u0026amp; Zakiyah, Z. (2024). Teachers\u0026apos; perceptions of the use of self-regulated strategy development in online EFL reading instruction to foster students\u0026rsquo; autonomy in online reading. \u003cem\u003eComputer-Assisted Language Learning Electronic Journal, 25\u003c/em\u003e(3), 102-123.\u003c/li\u003e\n\u003cli\u003eCa\u0026ntilde;eda, M. E., Mata, L. P., \u0026amp; Bedrijo, R. W. (2024). Investigating students\u0026rsquo; lived experiences in mathematics classroom activities. \u003cem\u003eInternational Journal of Academic and Applied Research, 8\u003c/em\u003e(6), 1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eElsner, S. (2025). \u003cem\u003eAccuracy of self-evaluation in biology education: Investigating potential effects of skill practice, prompting, and physical exercise\u003c/em\u003e [Doctoral dissertation, University of Cologne]. KUPS.\u003c/li\u003e\n\u003cli\u003eIge, M. M. O. (2025). Enhancing self-efficacy among civil engineering undergraduates using hands-on pedagogy. \u003cem\u003eInternational Journal of Academic and Applied Research, 9\u003c/em\u003e(1), 1\u0026ndash;10.\u003c/li\u003e\n\u003cli\u003eZhang, P. (2024). Effects of highlights and annotations on EFL learners\u0026rsquo; reading comprehension: An application of computer-assisted interactive reading model. \u003cem\u003eComputer Assisted Language Learning\u003c/em\u003e. Advance online publication. https://doi.org/10.1080/09588221.2024.2323568\u003c/li\u003e\n\u003cli\u003eSiller, H. S., \u0026amp; Ahmad, S. (2024). Analyzing the impact of collaborative learning approach on grade six students\u0026rsquo; mathematics achievement and attitude towards mathematics. \u003cem\u003eEurasian Journal of Mathematics, Science and Technology Education, 20\u003c/em\u003e(2), Article em2395. https://doi.org/10.29333/ejmste/14166\u003c/li\u003e\n\u003cli\u003eEvans, C., \u0026amp; Waring, M. (2024). Prioritising a self-regulatory assessment and feedback approach in higher education. In \u003cem\u003eElgar Encyclopedia of Teaching and Learning in Higher Education\u003c/em\u003e (pp. 305\u0026ndash;322). Edward Elgar Publishing. https://doi.org/10.4337/9781800886254.ch25\u003c/li\u003e\n\u003cli\u003eFacione, P. A. (1990). \u003cem\u003eThe California Critical Thinking Skills Test: College Level\u003c/em\u003e (Technical Report No. 1). The California Academic Press.\u003c/li\u003e\n\u003cli\u003eTversky, A., \u0026amp; Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. \u003cem\u003eScience, 185\u003c/em\u003e(4157), 1124\u0026ndash;1131. https://doi.org/10.1126/science.185.4157.1124\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Metacognition, Analytical Reasoning, Cognitive Bias, Achievement Gap, Higher Education, Cognitive Intervention","lastPublishedDoi":"10.21203/rs.3.rs-7408588/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7408588/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study tested a targeted metacognitive intervention designed to enhance analytical reasoning, reduce cognitive bias, and subsequently narrow the cognitive achievement gap. A quasi-experimental, pre-test-post-test-follow-up design was employed with 120 Kuwaiti undergraduate students, who were stratified by prior academic performance (high-achieving, n\u0026thinsp;=\u0026thinsp;60; low-achieving, n\u0026thinsp;=\u0026thinsp;60) and then randomly assigned to an eight-week intervention or a control group. Analytical reasoning and susceptibility to cognitive bias were measured at three time points. A mixed-model ANOVA revealed a significant three-way interaction (p\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating the intervention\u0026rsquo;s effects were moderated by achievement level. While all students in the intervention group demonstrated significant gains, improvements were most pronounced for the low-achieving students, whose post-intervention scores in both domains converged significantly with those of their high-achieving peers. These \u003cb\u003enotable\u003c/b\u003e gains were marked by very large effect sizes for the low-achieving group (d\u0026thinsp;=\u0026thinsp;2.45 for analytical reasoning; d = -3.27 for cognitive bias) and were successfully retained at a six-week follow-up. Findings provide \u003cb\u003ecompelling\u003c/b\u003e evidence that metacognitive training is a \u003cb\u003epromising\u003c/b\u003e and durable tool for fostering cognitive equity. This research offers a scalable, evidence-based model for supporting struggling learners and cultivating more robust, less-biased reasoning across the student population.\u003c/p\u003e","manuscriptTitle":"Bridging the Achievement Gap: A Metacognitive Intervention to Enhance Analytical Reasoning and Reduce Cognitive Bias in High- and Low-Achieving Students","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 12:11:49","doi":"10.21203/rs.3.rs-7408588/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3596baf2-34c5-41e8-a94b-c052d1d204f2","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-20T07:54:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-29 12:11:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7408588","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7408588","identity":"rs-7408588","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}