Process mining techniques for analysing patterns and strategies in students' self-regulated learning
Process mining techniques for analysing patterns and strategies in students' self-regulated learning
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DOI：
10.1007/s11409-013-9107-6
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发表时间：
2014-08-01
影响因子：
3.3
通讯作者：
Sonnenberg, Christoph
中科院分区：
文献类型：
--
作者：
Bannert, Maria;Reimann, Peter;Sonnenberg, Christoph
在借鉴当前自我调节学习研究的基础上，我们从一系列特定的调节活动序列来分析个体调节。成功的学生在学习过程中进行调节活动，如分析、计划、监控和评估认知和动机方面，不仅比不成功的学生更频繁，而且以不同的顺序--至少我们假设是这样的。虽然大多数研究都集中在频率分析上，但到目前为止，人们对学生的监管活动是如何随着时间的推移而展开的知之甚少。因此，我们的方法的目的也是分析自发的个体调节活动的时间顺序。在本文中，我们展示了在过程挖掘研究中开发的各种方法如何应用于识别言语协议中捕获的自我调节学习事件的过程模式。我们还展示了如何使用流程挖掘方法来测试理论SRL流程模型。一项有38名参与者以自我调节方式从超媒体学习的研究中的大声思考数据被用来说明方法论观点。
Referring to current research on self-regulated learning, we analyse individual regulation in terms of a set of specific sequences of regulatory activities. Successful students perform regulatory activities such as analysing, planning, monitoring and evaluating cognitive and motivational aspects during learning not only with a higher frequency than less successful learners, but also in a different order-or so we hypothesize. Whereas most research has concentrated on frequency analysis, so far, little is known about how students' regulatory activities unfold over time. Thus, the aim of our approach is to also analyse the temporal order of spontaneous individual regulation activities. In this paper, we demonstrate how various methods developed in process mining research can be applied to identify process patterns in self-regulated learning events as captured in verbal protocols. We also show how theoretical SRL process models can be tested with process mining methods. Thinking aloud data from a study with 38 participants learning in a self-regulated manner from a hypermedia are used to illustrate the methodological points.