The ability to regulate one's own learning is essential for success in online courses. Recent efforts have used clickstream data to create timely, fine-grained, and comprehensive measures of self-regulated learning (SRL) in online courses in an attempt to shed light on the process of SRL and to improve the identification of students who lack SRL skills and are at risk of low achievement. However, key questions remain: to what extent do these clickstream measures correspond to traditional self-reported measures about specific SRL constructs? Do these clickstream measures provide more information than existing self-reported measures in predicting course performance? This study used the clickstream data collected from a learning management system to measure two aspects of SRL: time management and effort regulation. We found that the clickstream measures were significantly associated with students' self-reported time management and effort regulation after the course. In addition, these clickstream measures significantly improved predictions of students' performance in the current and subsequent courses over predictions based on self-reported measures alone. These results provide evidence for the validity of the clickstream measures and guide the use of clickstream data to understand the process of SRL and identify students who might not be well served by taking classes online.

调节自己的学习能力对于在线课程的成功至关重要。最近的努力已使用点击流数据在在线课程中创建及时，细粒度和全面的自我调节学习（SRL）措施，以试图了解SRL的过程并改善对缺乏SRL技能的学生的识别并有成就不佳的风险。但是，关键问题仍然存在：这些点击流指标在多大程度上与针对特定SRL构造的传统自报告指标相对应？这些点击流指标在预测课程效果方面是否比现有的自我报告指标提供更多信息？这项研究使用从学习管理系统收集的点击流数据来衡量SRL的两个方面：时间管理和工作量调节。我们发现，点击流指标与课程结束后学生的自我报告时间管理和工作量调节显着相关。此外，这些点击流指标比仅基于自我报告指标的指标明显提高了对当前和后续课程中学生表现的预测。这些结果为点击流方法的有效性提供了证据，并指导使用点击流数据来了解SRL的过程，并确定在线上课可能没有得到很好服务的学生。当前和后续课程的绩效超出了仅基于自我报告测度的预测。这些结果为点击流方法的有效性提供了证据，并指导使用点击流数据来了解SRL的过程，并确定在线上课可能没有得到很好服务的学生。当前和后续课程的绩效超出了仅基于自我报告测度的预测。这些结果为点击流方法的有效性提供了证据，并指导使用点击流数据来了解SRL的过程，并确定在线上课可能没有得到很好服务的学生。