Learning Analytics at Low Cost: At-risk Student Prediction with Clicker Data and Systematic Proactive Interventions
Learning Analytics at Low Cost: At-risk Student Prediction with Clicker Data and Systematic Proactive Interventions
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发表时间：
2018-04
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通讯作者：
Samuel P. M. Choi;S. S. Lam-S.;K. Li;B. Wong
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作者：
Samuel P. M. Choi;S. S. Lam-S.;K. Li;B. Wong
虽然学习分析（LA）实践已被证明是实用和有效的，但其中大多数都需要大量的数据和努力。本文报告了一个案例研究，该案例研究证明了教师在本科商业定量方法课程中以低成本实践LA以识别风险学生的可行性。本研究没有使用来自学习管理系统的跟踪数据作为预测变量，而是使用点击反应作为形成性评估，同时使用学生人口统计数据和总结性评估。这种LA实践利用免费的云服务，谷歌表单和谷歌表单，特别是收集和分析点击器数据。尽管使用的数据集很小，但洛杉矶的实施在早期识别有风险的学生方面是有效的。基于预测模型估算的学生风险概率，提出了一种系统的主动咨询方法作为干预策略。结果表明，干预成功率随干预次数的增加而相应增加，对同伴群体的干预效果远高于对学生个体的干预效果。总体而言，该研究学生的通过率比整门课程的通过率高出7%。本文还讨论了使用线性回归和逻辑回归进行分类的实用建议和问题。
While learning analytics (LA) practices have been shown to be practical and effective, most of them require a huge amount of data and effort. This paper reports a case study which demonstrates the feasibility of practising LA at a low cost for instructors to identify at-risk students in an undergraduate business quantitative methods course. Instead of using tracking data from a learning management system as predictive variables, this study utilised clicker responses as formative assessments, together with student demographic data and summative assessments. This LA practice makes use of free cloud services, Google Forms and Google Sheets in particular for collecting and analysing clicker data. Despite a small dataset being used, the LA implementation was effective in identifying at-risk students at an early stage. A systematic proactive advising approach is proposed as an intervention strategy based on students’ at-risk probability estimated by a prediction model. The result shows that the intervention success rate increases correspondingly with the number of interventions and the intervention effects on peer groups are far more successful than on individual students. Overall, the students’ pass rate in the study was 7% higher than that for the whole course. Practical recommendations and concerns about using linear regression and logistic regression for classification are also discussed.