This study examines whether including more contextual information in data analysis could improve our ability to identify the relation between students’ online learning behavior and overall performance in an introductory physics course. We created four linear regression models correlating students’ pass-fail events in a sequence of online learning modules with their normalized total course score. Each model takes into account an additional level of contextual information than the previous one, such as student learning strategy and duration of assessment attempts. Each of the latter three models is also accompanied by a visual representation of students’ interaction states on each learning module. We found that the best performing model is the one that includes the most contextual information, including instruction condition, internal condition, and learning strategy. The model shows that while most students failed on the most challenging learning module, those with normal learning behavior are more likely to obtain higher total course, whereas students who resorted to guessing on the assessments of subsequent modules tended to receive lower total scores. Our result suggests that considering more contextual information related to each event can be an effective method to improve the quality of learning analytics, leading to more accurate and actionable recommendations for instructors.

中文翻译：

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学生的在线学习行为与课程成绩之间的关系：哪些上下文信息很重要？

这项研究探讨了在数据分析中包括更多的上下文信息是否可以提高我们识别物理入门课程中学生在线学习行为与整体表现之间关系的能力。我们创建了四个线性回归模型，这些模型将一系列在线学习模块中学生的不及格事件与他们的标准化总课程得分相关联。每个模型都考虑了比前一个模型更高的上下文信息级别，例如学生的学习策略和评估尝试的持续时间。后三个模型中的每个模型还伴随着每个学习模块上学生交互状态的直观表示。我们发现，效果最好的模型是包含最多上下文信息的模型，包括指令条件，内部条件，和学习策略。该模型显示，尽管大多数学生在最具挑战性的学习模块上均未通过，但具有正常学习行为的学生更有可能获得更高的总课程，而那些依靠对后续模块的评估进行猜测的学生则倾向于获得较低的总成绩。我们的结果表明，考虑与每个事件相关的更多上下文信息可以是提高学习分析质量的有效方法，从而为教师提供更准确和可操作的建议。