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arXiv 2003.08446physics.ed-ph
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通过在数据分析中加入情境信息来探索学生在线学习行为与课程成绩之间的关系

Exploring the relation between students' online learning behavior and course performance by including contextual information in data analysis

Zhongzhou Chen, Mengyu Xu, Geoffrey Garrido, Matthew W. Guthrie

摘要:

本研究探讨在数据分析中加入更多的情境信息是否可以提高我们识别学生在线学习行为与物理入门课程整体表现之间关系的能力。我们创建了四个线性回归模型,将学生在一系列在线学习模块中的及格-不及格事件与他们的标准化总成绩相关联。每个模型都考虑到了比前一个模型更多的上下文信息,例如学生的学习策略和评估尝试的持续时间。后三个模型中的每一个都伴随着学生在每个学习模块上的交互状态的视觉表示。我们发现,表现最好的模型是一个包含最多的上下文信息,包括教学条件,内部条件和学习策略。该模型显示,虽然大多数学生在最具挑战性的学习模块中失败,但那些学习行为正常的学生更有可能获得较高的总成绩,而那些对后续模块的评估采取猜测的学生往往会获得较低的总成绩。我们的研究结果表明,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。

英文摘要:

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 scores, whereas students who resorted to guessing on the assessments of subsequent modules tended to receive lower total scores. Our results suggest 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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