Educational process mining is now a promising method to provide decision-support information for the teaching–learning process via finding useful educational guidance from the event logs recorded in the learning management system. Existing studies mainly focus on mining students' problem-solving skills or behavior patterns and intervening in students' learning processes according to this information in the late course. However, educators often expect to improve the learning outcome in a proactive manner through dynamically designing instructional strategies prior to a course that are more appropriate to students' average ability. Therefore, in this article, we propose a two-stage problem-solving ability modeling approach to obtain students' ability in different learning stages, including the pre-problem-solving ability model and the post-problem-solving ability model. The models are trained with Gradient Boosting Decision Tree (GBDT) on the historical event logs of the prerequisite course and the target course, respectively. With the premodel, we establish the students' pre-problem-solving ability profiles that reflect their average knowledge level before starting a course. Then, the instructional design is dynamically chosen according to the profiles. After a course completes, the post-problem-solving ability profiles are generated by the postmodel to analyze the learning outcome and prompt the learning feedback, in order to complete the closed-loop teaching process. We study the modeling of coding ability in computer programming education to show our teaching strategy. The experimental results show that the generalizable problem-solving ability models yield high classification precision, while most students' abilities have been significantly improved by the proposed approach at the end of the course.

中文翻译：

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通过超扭曲二阶滑模观测器技术对不确定扰动下的车辆编队进行分散鲁棒控制


教育过程挖掘现在是一种很有前途的方法，通过从学习管理系统中记录的事件日志中找到有用的教育指导，为教学过程提供决策支持信息。现有的研究主要集中在挖掘学生解决问题的能力或行为模式，并在后期根据这些信息干预学生的学习过程。然而，教育工作者通常希望通过在课程开始前动态设计更适合学生平均能力的教学策略，以主动的方式提高学习成果。因此，在本文中，我们提出了一种两阶段的问题解决能力建模方法来获取学生在不同学习阶段的能力，包括问题解决前的能力模型和问题解决后的能力模型。这些模型分别使用梯度提升决策树（GBDT）在先决课程和目标课程的历史事件日志上进行训练。通过预模型，我们建立了学生的预解决问题能力档案，反映了他们在开始课程之前的平均知识水平。然后，根据概况动态选择教学设计。课程结束后，后模型生成事后问题解决能力档案，分析学习成果并提示学习反馈，完成教学过程闭环。我们研究计算机编程教育中编码能力的建模，以展示我们的教学策略。 实验结果表明，可推广的问题解决能力模型具有较高的分类精度，并且大多数学生的能力在课程结束时通过所提出的方法得到了显着提高。