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Detecting students-at-risk in computer programming classes with learning analytics from students' digital footprints

机译：利用来自学生数字足迹的学习分析来检测计算机编程课程中处于危险中的学生

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摘要

Different sources of data about students, ranging from static demographics to dynamic behavior logs, can be harnessed from a variety sources at Higher Education Institutions. Combining these assembles a rich digital footprint for students, which can enable institutions to better understand student behaviour and to better prepare for guiding students towards reaching their academic potential. This paper presents a newresearch methodology to automatically detect students "at-risk" of failing an assignment in computer programming modules (courses) and to simultaneously support adaptive feedback. By leveraging historical student data, we built predictive models using students' offline (static) information including student characteristics and demographics, and online (dynamic) resources using programming and behaviour activity logs. Predictions are generated weekly during semester. Overall, the predictive and personalised feedback helped to reduce the gap between the lower and higher-performing students. Furthermore, students praised the prediction and the personalised feedback, conveying strong recommendations for future students to use the system. We also found that students who followed their personalised guidance and recommendations performed better in examinations.
机译：可以从高等教育机构的各种来源中利用有关学生的不同数据源，从静态人口统计信息到动态行为日志。将这些结合起来，可以为学生带来丰富的数字足迹，这可以使机构更好地了解学生的行为，并更好地为引导学生发挥学业潜力做准备。本文提出了一种新的研究方法，可以自动检测学生在计算机编程模块（课程）中未通过作业的“风险”，并同时支持自适应反馈。通过利用学生的历史数据，我们使用学生的离线（静态）信息（包括学生特征和人口统计信息）以及在线（动态）资源（使用编程和行为活动日志）建立了预测模型。在学期中每周都会生成预测。总体而言，预测性和个性化反馈有助于缩小成绩较低和成绩较高的学生之间的差距。此外，学生们对预测和个性化反馈表示赞赏，为将来的学生使用该系统提供了强有力的建议。我们还发现，遵循个性化指导和建议的学生在考试中表现更好。

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