Self-regulated Learning with MetaTutor: Advancing the Science of Learning with MetaCognitive Tools
Self-regulated Learning with MetaTutor: Advancing the Science of Learning with MetaCognitive Tools
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DOI：
10.1007/978-1-4419-5716-0_11
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发表时间：
2010-01-01
期刊：
影响因子：
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通讯作者：
Burkett, Candice
中科院分区：
文献类型：
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作者：
Azevedo, Roger;Johnson, Amy;Burkett, Candice
用先进的学习技术(如超媒体)理解复杂学习的关键在于我们理解学生认知、元认知、动机和情感过程的时间部署的能力。我们的章节将集中于批判性地分析在超媒体学习中使用混合方法来分析自我调节学习(SRL)的复杂性。我们将引用我们自己的研究中的例子(例如Azevedo 2008，促进学生学习的教育技术的最新创新(pp.127-156)；Azevedo和Witherspoon出版社，教育中的元认知手册)以及其他人(例如Biswas等人，2005；Schwartz等人，出版社；Winne和Nesbitt出版社，教育中的元认知手册)的例子，来展示和讨论在非线性、多表征的计算机化环境中使用混合方法来捕获、建模、跟踪和推断在学习过程中展开的SRL过程的优缺点。本章将重点介绍用于融合学习期间收集的产品数据(例如，学习结果)、过程数据(例如，有声思考数据)和日志文件数据的方法以及定量和定性分析，开发用于分类和推断SRL过程部署的编码方案，以及使用计算工具检查学习者的行为和导航路径。最后，我们将提出一个理论模型，该模型整合了本章中提出的各种主题，将指导未来的研究和教育实践，以培养学生在超媒体环境下的SRL。
The key to understanding complex learning with advanced learning technologies (e.g., hypermedia) lies in our ability to comprehend the temporal deployment of students’ cognitive, metacognitive, motivational, and affective processes. Our chapter will focus on critically analyzing the use of mixed-method approaches to analyze the complex nature of self-regulated learning (SRL) during hypermedia learning. We will use examples from our own research (e.g., Azevedo 2008,Recent innovations in educational technology that facilitate student learning(pp. 127–156); Azevedo & Witherspoon, in press,Handbook of metacognition in education) and that of others (e.g., Biswas et al., 2005; Schwartz et al., in press; Winne & Nesbitt, in press,Handbook of metacognition in education) to present and discuss the strengths and weaknesses in using mixed methods to capture, model, trace, and infer the unfolding SRL processes during learning with nonlinear, multirepresentational computerized environments. The chapter will focus on the methods, and quantitative and qualitative analyses used to converge product data (e.g., learning outcomes), process data (e.g., think-aloud data), and log-file data collected during learning, develop coding schemes to categorize and infer the deployment of SRL processes, and the use of computational tools to examine learners’ behaviors and navigation paths. Lastly, we will present a theoretical model that integrates the various topics presented in this chapter that will guide future research and educational practices for fostering students’ SRL with hypermedia environments.