arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2403.04424physics.ed-ph
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热力学课程中学生行为与对自适应练习的参与度

Student behaviour and engagement with adaptive exercises on a thermodynamics course

Matti Harjula, Ville Havu, Inkeri Kontro, Kimmo Kulmala, Jarmo Malinen, Petri Salo

AI总结:

本研究基于Moodle新型题型Stateful开发自适应交互式电子学习材料,在大学热力学课程实验中通过学生解题行为算法分类,发现该分类可预测多数学生课程其他部分表现,且与两项物理调查统计一致。

摘要:

本研究在大学热力学课程中开展教学实验,采用自适应交互式电子学习材料,该材料基于扩展了原电子学习平台STACK的新型Moodle题型Stateful开发。系统收集学生数据,依据其解题行为通过算法进行分类。研究发现,对大多数学生而言,该数据分类可预测其在课程其他部分的表现。此外,该分类与Thermodynamic Concept Survey(热力学概念调查)及Maryland Physics Expectation Survey(马里兰物理期望调查)在统计上具有一致性。

英文摘要:

A teaching experiment was carried out in a university-level thermodynamics course using adaptive and interactive e-learning material, created in the new Moodle question type Stateful extending the original e-learning platform STACK. The system collects data about the students that is used to algorithmically classify them according to their behaviour in solving problems. It is observed that the classification of this data predicts students' success in the other parts of the course for a majority of students. Also, the classification is statistically consistent with Thermodynamic Concept Survey and Maryland Physics Expectation Survey.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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