Outputs of the feature selection process.

We present results from a small-scale randomized controlled trial that evaluates the impact of just-in-time interventions on the academic outcomes of N = 65 undergraduate students in a STEM course. Intervention messaging content was based on machine learning forecasting models of data collected from 537 students in the same course over the preceding 3 years. Trial results show that the intervention produced a statistically significant increase in the proportion of students that achieved a passing grade. The outcomes point to the potential and promise of just-in-time interventions for STEM learning and the need for larger fully-powered randomized controlled trials.本研究报道了一项小型随机对照试验（randomized controlled trial）的结果，该试验旨在评估即时干预（just-in-time interventions）对某理工科（STEM）课程中共计65名本科生学业成果的影响。本次干预的信息内容基于机器学习预测模型，该模型依托该课程前三年间收集的537名学生的相关数据。试验结果显示，干预措施使获得及格成绩的学生比例出现了统计学意义上的显著提升。本研究结果既凸显了即时干预在STEM学习中的应用潜力与发展前景，同时也表明有必要开展更大规模、统计效力充足的随机对照试验。