The purpose of this study was to investigate whether students' self-reported SRL align with their digital trace data collected from the learning management system. This study took place in an upper-level college agriculture course delivered in an asynchronous online format. By comparing online students' digital trace data with their self-reported data, this study found that digital trace data from LMS could predict students' performance more accurately than self-reported SRL data. Through cluster analysis, students were classified into three levels based on their self-regulatory ability and the characteristics of each group were analyzed. By incorporating qualitative data, we explored possible explanations for the differences between students' self-reported SRL data and the digital trace data. This study challenges us to question the validity of existing self-reported SRL instruments. The three-cluster division of students' learning behaviors provides practical implications for online teaching and learning.

本研究的目的是调查学生自我报告的 SRL 是否与从学习管理系统收集的数字跟踪数据一致。这项研究是在以异步在线格式提供的高级大学农业课程中进行的。通过将在线学生的数字跟踪数据与他们自我报告的数据进行比较，本研究发现来自 LMS 的数字跟踪数据可以比自我报告的 SRL 数据更准确地预测学生的表现。通过聚类分析，根据学生的自我调节能力将学生分为三个层次，并分析各组的特点。通过结合定性数据，我们探索了学生自我报告的 SRL 数据和数字跟踪数据之间差异的可能解释。这项研究挑战我们质疑现有自我报告的 SRL 工具的有效性。学生学习行为的三类划分为在线教学提供了实际意义。