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.

当前教育数据挖掘和学习分析的主要挑战之一是为特定课程获得的预测模型的可移植性或可转移性，以便它们可以应用于其他不同的课程。为了应对这一挑战，最重要的问题之一是模型过度依赖用于训练它们的低级属性，这降低了模型的可移植性。为了解决这个问题，使用具有更多语义含义的高级属性（例如本体）可能非常有用。沿着这条线，我们建议使用一个本体，该本体使用一种行为分类法来总结学生与 Moodle 学习管理系统的交互。当我们使用直接从 Moodle 日志获得的低级原始属性时，我们将这种建议方法的结果与我们之前的结果进行比较。结果表明，使用所提出的本体提高了模型在预测准确性方面的可移植性。本文的主要贡献是表明在一个源课程中获得的本体模型可以应用于具有相似使用水平的其他不同目标课程，而不会损失预测准确性。