ABSTRACT

This study applies a time-driven approach to model self-regulated learning (SRL) on the basis of elapsed time metrics in the context of open-ended learning environments (OELEs), specifically, network-based tutors. In doing so, we examine how students allocated attentional resources to distinct phases of SRL as a measure of depth of information processing. Student teachers (N=68) were assigned to two different versions of nBrowser: a static version where the network did not converge on the basis of student interactions and a dynamic version where the network was continually updated by the system. Students designed a lesson plan and completed pre- and post-test self-report measures of knowledge gains. In both the experimental conditions, the results show four distinct SRL profiles that are relatively consistent and can be detected on the basis of behavioral patterns logged by the system across behaviors, namely, planning, requesting hints, studying examples, and monitoring. Although students who allocated more attentional resources to studying examples performed more poorly, their efforts to engage in planning, requesting hints, and monitoring were found to predict knowledge gains and design skills. Furthermore, students assigned to the dynamic version of the system outperformed those assigned to the static version in pedagogical knowledge gains.

摘要

本研究采用时间驱动的方法，在开放式学习环境（OELE）（特别是基于网络的导师）的背景下，基于经过时间度量来对自我调节学习（SRL）进行建模。在此过程中，我们研究了学生如何将注意力资源分配到 SRL 的不同阶段，作为信息处理深度的衡量标准。学生教师（N = 68）被分配到两个不同版本的nBrowser：静态版本，网络不会根据学生交互进行收敛；动态版本系统不断更新网络。学生设计了课程计划，并完成了测试前和测试后的知识增益自我报告。在这两种实验条件下，结果显示了四种不同的 SRL 配置文件，这些配置文件相对一致，可以根据系统记录的跨行为模式（即计划、请求提示、学习示例和监控）来检测。尽管分配更多注意力资源来学习示例的学生表现较差，但他们参与计划、请求提示和监控的努力被发现可以预测知识增益和设计技能。此外，分配到动态版本系统的学生在教学知识增益方面优于分配到静态版本系统的学生。