The recent focus on learning analytics (LA) to analyze temporal dimensions of learning holds the promise of providing insights into latent constructs, such as learning strategy, self-regulated learning (SRL), and metacognition. These methods seek to provide an enriched view of learner behaviors beyond the scope of commonly used correlational or cross-sectional methods. In this article, we present a methodological sequence of techniques that comprises: 1) the strategic clustering of learner types; 2) the use of microlevel processing to transform raw trace data into SRL processes; and 3) the use of a novel process mining algorithm to explore the generated SRL processes. We call this the “Trace-SRL” framework. Through this framework, we explored the use of microlevel process analysis and process mining (PM) techniques to identify optimal and suboptimal traits of SRL. We analyzed trace data collected from online activities of a sample of nearly 300 computer engineering undergraduate students enrolled on a course that followed a flipped class-room pedagogy. We found that using a theory-driven approach to PM, a detailed account of SRL processes emerged, which could not be obtained from frequency measures alone. PM, as a means of learner pattern discovery, promises a more temporally nuanced analysis of SRL. Moreover, the results showed that more successful students regularly engage in a higher number of SRL behaviors than their less successful counterparts. This suggests that not all students are sufficiently able to regulate their learning, which is an important finding for both theory and LA, and future technologies that support SRL.

中文翻译：

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Trace-SRL：根据跟踪数据分析自我调节学习微观过程的框架


最近对学习分析（LA）的关注，用于分析学习的时间维度，有望提供对潜在结构的洞察，例如学习策略、自我调节学习（SRL）和元认知。这些方法旨在提供超出常用相关或横截面方法范围的学习者行为的丰富视图。在本文中，我们提出了一系列技术方法，包括：1）学习者类型的策略聚类； 2）使用微观处理将原始跟踪数据转换为SRL过程； 3) 使用新颖的流程挖掘算法来探索生成的 SRL 流程。我们称之为“Trace-SRL”框架。通过这个框架，我们探索了使用微观过程分析和过程挖掘（PM）技术来识别 SRL 的最佳和次优特征。我们分析了从近 300 名计算机工程本科生的在线活动中收集的跟踪数据，这些学生参加了翻转课堂教学法的课程。我们发现，使用理论驱动的 PM 方法可以对 SRL 过程进行详细说明，而这无法仅从频率测量中获得。 PM 作为学习者模式发现的一种手段，有望对 SRL 进行更及时细致的分析。此外，结果表明，较成功的学生比不太成功的学生经常从事更多的 SRL 行为。这表明并非所有学生都有足够的能力调节他们的学习，这对于理论和 LA 以及支持 SRL 的未来技术来说都是一个重要发现。