The Faculty of Mechanical Engineering, Czech Technical University in Prague (FME) faces a significant student drop-out in the first-year bachelor programme, which is an actual problem for many higher education institutions. Metacognitive processes play a vital role in self-regulated learning. Students become active participants in their learning, and one critical aspect of higher education studies is planning and time management. The exam taking behaviour is in the context of the FME manifestation of the time management skills of each student; thus, the exam-taking patterns may help identify at-risk students. To evaluate the importance of exam behaviour patterns, we conducted three experiments. Identification of students passing or failing the first study year has been conducted using four different machine learning models. The exam taking behaviour patterns increase the prediction F-measure significantly for the class of failing students (approximately 0.3 increase). Moreover, the approach based on student behaviour enabled us to identify the critical exam-taking patterns, which further helps the lecturers identify at-risk students and improve their time management skills and chances to pass the first academic year.

布拉格捷克技术大学 (FME) 机械工程学院在一年级学士学位课程中面临大量学生辍学，这是许多高等教育机构的实际问题。元认知过程在自我调节学习中起着至关重要的作用。学生成为学习的积极参与者，高等教育研究的一个关键方面是计划和时间管理。应试行为是在 FME 体现每个学生时间管理技能的背景下；因此，应试模式可能有助于识别有风险的学生。为了评估考试行为模式的重要性，我们进行了三个实验。已经使用四种不同的机器学习模型来识别第一年通过或失败的学生。应试行为模式显着增加了不及格学生班级的预测 F 度量（增加约 0.3）。此外，基于学生行为的方法使我们能够识别关键的考试模式，这进一步帮助讲师识别有风险的学生，提高他们的时间管理技能和通过第一学年的机会。