Scholarlink

<p>Theoretical models of self-regulated learning emphasize the role of metacognitive monitoring and regulation in deploying accurate cognitive, affective, metacognitive, motivational, and social processes to improve learning outcomes. Many of these models identify metacognition as a dynamic, recursive, conditional, and contextual process. However, empirical work has largely neglected how past metacognitive judgments influence future judgments. Furthermore, prior work has primarily focused on describing what learners did during learning but has not advanced toward predicting future behavior on an individual level. Our work addresses this gap by comparing three modeling approaches to predict the accuracy of ease-of-learning (EOL) and retrospective confidence judgments (RCJs) from 55 undergraduates based on the accuracy of previous metacognitive judgments as participants learned about the human circulatory system using Metatutor-IVH a multimedia environment. We found that when fit on the individual level, the most frequently fit models were random-walk models, followed by ARIMAs, and then moving average models. Additionally, we observed consistency in the types of model fits across judgment types within individuals. When examining the accuracy in the predictions our individually fit models, ARIMA models outperformed the others in predicting RCJs. Moreover, they achieved higher accuracy that the traditional group-level models in which one model was trained on the entire sample and then fit to each individual, that our individual approach outperformed the traditional method. Finally, individuals who showed autoregressive tendencies in their EOL judgment accuracies outperformed their peers. Overall, our results make a strong argument for an individualized time-series modeling approach over traditional aggregate measures. Additionally, we found evidence that future EOL judgment accuracy may be temporally dependent on past judgments, and those that show autoregressive behaviors may be indicative of more efficient learning and metacognitive monitoring. We conclude by discussing the theoretical and practical implications for designing intelligent adaptive learning environments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Modeling the Dynamics and Autoregressive Tendencies of Metacognitive Judgment Accuracy during Complex Learning

  • 期刊论文

  • 发表日期：

    2025年10月21日

查看全文
  • Megan D. Wiedbusch,
  • Daryn Dever,
  • Tara Delgado,
  • Roger Azevedo

摘要

Theoretical models of self-regulated learning emphasize the role of metacognitive monitoring and regulation in deploying accurate cognitive, affective, metacognitive, motivational, and social processes to improve learning outcomes. Many of these models identify metacognition as a dynamic, recursive, conditional, and contextual process. However, empirical work has largely neglected how past metacognitive judgments influence future judgments. Furthermore, prior work has primarily focused on describing what learners did during learning but has not advanced toward predicting future behavior on an individual level. Our work addresses this gap by comparing three modeling approaches to predict the accuracy of ease-of-learning (EOL) and retrospective confidence judgments (RCJs) from 55 undergraduates based on the accuracy of previous metacognitive judgments as participants learned about the human circulatory system using Metatutor-IVH a multimedia environment. We found that when fit on the individual level, the most frequently fit models were random-walk models, followed by ARIMAs, and then moving average models. Additionally, we observed consistency in the types of model fits across judgment types within individuals. When examining the accuracy in the predictions our individually fit models, ARIMA models outperformed the others in predicting RCJs. Moreover, they achieved higher accuracy that the traditional group-level models in which one model was trained on the entire sample and then fit to each individual, that our individual approach outperformed the traditional method. Finally, individuals who showed autoregressive tendencies in their EOL judgment accuracies outperformed their peers. Overall, our results make a strong argument for an individualized time-series modeling approach over traditional aggregate measures. Additionally, we found evidence that future EOL judgment accuracy may be temporally dependent on past judgments, and those that show autoregressive behaviors may be indicative of more efficient learning and metacognitive monitoring. We conclude by discussing the theoretical and practical implications for designing intelligent adaptive learning environments.