Wong Thai Ling, Hope David, Jaap Alan
Medical Education, The University of Edinburgh, Edinburgh, GBR.
Cureus. 2024 Aug 6;16(8):e66278. doi: 10.7759/cureus.66278. eCollection 2024 Aug.
Introduction Identifying students at risk of failure before they experience difficulties may considerably improve their outcomes. However, identification techniques can be costly, time-intensive, and of unknown efficacy. Medical educators need accessible and cost-effective ways of identifying at-risk students. The aim of this study was to investigate the relationship between student engagement in an online classroom and academic performance given the transition of many courses from in-person to online learning. Methods A retrospective study was conducted on a group of 235 students from the University of Edinburgh Bachelor of Medicine and Surgery (MBChB) in Year One for eight weeks from the start of term, September 2020. Purposive sampling was used. Data were collected on total test submissions, total discussion board submissions, engagement scores, and overall exam scores. Learning analytics on discussion board engagement were collected for new medical students before they had sat any summative assessment. Tests completed, discussion board posts made, and their total engagement score were correlated with their first summative assessment scores at the end of semester one. Results We found a statistically significant correlation between total test submissions, total discussion board submissions, engagement scores, and overall exam scores, with small-medium effects (r = 0.281, p<0.001) (r = 0.241, p<0.001), and (r = 0.202, p<0.001). Students with more test submissions, total discussion board submissions, and total engagement had a higher overall exam score. There was a statistically significant moderate correlation between total submissions and overall exam scores (r = 0.324, p<0.001). Conclusions Students who had a higher number of submissions were more likely to perform better on assessments. Early engagement correlates with performance. Learning analytics can help identify student underperformance before they undertake any assessment, and this can be done very inexpensively and with minimal staff resources if properly planned.
引言 在学生遇到困难之前识别出有失败风险的学生，可能会显著改善他们的学习成果。然而，识别技术可能成本高昂、耗时且效果未知。医学教育工作者需要便捷且经济高效的方法来识别有风险的学生。鉴于许多课程从面对面教学转向在线学习，本研究的目的是调查学生在在线课堂中的参与度与学业成绩之间的关系。
方法 对爱丁堡大学医学与外科学士学位（MBChB）一年级的235名学生进行了一项回顾性研究，从2020年9月学期开始为期八周。采用目的抽样法。收集了关于总测试提交量、总讨论板提交量、参与度得分和整体考试成绩的数据。在新医学学生参加任何总结性评估之前，收集了他们在讨论板参与度方面的学习分析数据。学期末，完成的测试、发布的讨论板帖子及其总参与度得分与他们的第一次总结性评估成绩相关。
结果 我们发现总测试提交量、总讨论板提交量、参与度得分和整体考试成绩之间存在统计学上的显著相关性，影响程度为中等到小（r = 0.281，p<0.001）（r = 0.241，p<0.001），以及（r = 0.202，p<0.001）。提交测试更多、总讨论板提交量更多和总参与度更高的学生整体考试成绩更高。总提交量与整体考试成绩之间存在统计学上的显著中度相关性（r = 0.324，p<0.001）。
结论 提交量较高的学生在评估中表现更好的可能性更大。早期参与度与成绩相关。学习分析可以在学生进行任何评估之前帮助识别学业表现不佳的学生，如果计划得当，这可以以非常低的成本和最少的人员资源完成。
