用语义化知识图谱提升学生表现预测模型的跨课程迁移能力
Improving the portability of predicting students performance models by using ontologies
- 基于学生在Moodle上的操作行为构建语义化知识图谱
- 跨课程迁移时预测准确率保持稳定，优于原始日志特征
- 适合教育数据挖掘中需跨课程部署模型的研究者
当前教育数据挖掘与学习分析面临的主要挑战之一是预测模型在特定课程中训练后难以迁移到其他不同课程。核心问题在于模型过度依赖低层级原始属性，导致可迁移性差。为此，本文提出使用具有语义意义的高层属性——基于Moodle学习管理系统学生交互行为的行动分类知识图谱。通过对比先前使用Moodle日志原始低层属性的方法，实验表明该知识图谱显著提升了模型的可迁移性。主要贡献在于证明：在源课程中构建的本体模型，可在目标课程使用水平相似的情况下直接应用，且不损失预测精度。
原文摘要 · Abstract (English)
One of the main current challenges in Educational Data Mining and Learning Analytics is the portability or transferability of predictive models obtained for a particular course so that they can be applied to other different courses. To handle this challenge, one of the foremost problems is the models excessive dependence on the low-level attributes used to train them, which reduces the models portability. To solve this issue, the use of high level attributes with more semantic meaning, such as ontologies, may be very useful. Along this line, we propose the utilization of an ontology that uses a taxonomy of actions that summarises students interactions with the Moodle learning management system. We compare the results of this proposed approach against our previous results when we used low-level raw attributes obtained directly from Moodle logs. The results indicate that the use of the proposed ontology improves the portability of the models in terms of predictive accuracy. The main contribution of this paper is to show that the ontological models obtained in one source course can be applied to other different target courses with similar usage levels without losing prediction accuracy.
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