Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data
Trace-SRL: A Framework for Analysis of Microlevel Processes of Self-Regulated Learning From Trace Data
复制标题
Trace-SRL：根据跟踪数据分析自我调节学习微观过程的框架
DOI：
--
复制
发表时间：
2020
影响因子：
3.7
通讯作者：
A. Pardo
中科院分区：
文献类型：
--
作者：
John Saint;A. Whitelock;D. Gašević;A. Pardo
最近的重点学习分析（LA）分析学习的时间维度持有的承诺，提供洞察潜在的结构，如学习策略，自我调节学习（SRL）和元认知。这些方法试图提供一个丰富的学习者行为的范围以外的常用的相关或横截面的方法。在这篇文章中，我们提出了一个方法序列的技术，包括：1）学习者类型的战略聚类; 2）使用微观处理原始跟踪数据转换成SRL过程;和3）使用一种新的过程挖掘算法，探索生成的SRL过程。我们称之为“Trace-SRL”框架。通过这个框架，我们探讨了使用微观层次的过程分析和过程挖掘（PM）技术，以确定最佳和次优性状的SRL。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，这些学生参加了一门遵循翻转教室教学法的课程。我们发现，使用理论驱动的方法PM，SRL过程的详细说明出现了，这不能单独从频率的措施。PM，作为一种学习者模式发现的手段，承诺SRL的时间上更细致入微的分析。此外，结果表明，更成功的学生经常从事更多的SRL行为比他们不太成功的同行。这表明，并非所有的学生都有足够的能力来调节他们的学习，这对理论和LA以及支持SRL的未来技术都是一个重要的发现。
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.
影响因子：
3.3
作者：
Bannert, Maria;Reimann, Peter;Sonnenberg, Christoph
通讯作者：
Sonnenberg, Christoph