Massive open online courses (MOOCs) have been an important learning tool in education. In order to reduce the high dropout rate and improve learners' satisfactions, it is urgent for MOOCs platform to provide course recommendation and tutoring service. To achieve it, it is necessary to determine and trace learners' learning state. Cognitive diagnosis in psychometric is a good way to quantify learners' capacities, but it demands explicit learner feedback, which does not always exist in MOOCs platform, such a typical weak-interaction scenario. Therefore, in this article, multidimensional item response theory (MIRT) is exploratively integrated into recommendation models in MOOCs by introducing a time-effectiveness hypothesis to obtain the implicit response on a followed course. To dynamically update learners' capacities by considering real-time and capacity multidimensionality, MIRT is extended to a capacity tracing model. The estimation for learner capacity is treated as attributes and integrated into collaborative filtering framework in course recommendation. To the best of our knowledge, this is the first work to integrate capacity tracing into course recommendation in MOOCs. Extensive experiments are conducted on a real-world dataset, demonstrating that the capacity tracing-enhanced course recommendation has improved effectiveness and explainability in MOOCs.

中文翻译：

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MOOC 中的能力追踪增强课程推荐


大规模开放在线课程（MOOC）已成为教育领域的重要学习工具。为了降低高辍学率、提高学习者满意度，MOOCs平台迫切需要提供课程推荐和辅导服务。为了实现这一目标，需要确定和跟踪学习者的学习状态。心理测量学中的认知诊断是量化学习者能力的好方法，但它需要明确的学习者反馈，而MOOC平台这种典型的弱交互场景并不总是存在这种反馈。因此，在本文中，通过引入时间有效性假设，将多维项目响应理论（MIRT）探索性地集成到 MOOC 的推荐模型中，以获得后续课程的隐式响应。为了通过考虑实时性和能力多维性来动态更新学习者的能力，MIRT 被扩展为能力跟踪模型。对学习者能力的估计被视为属性并集成到课程推荐​​中的协同过滤框架中。据我们所知，这是第一个将能力追踪整合到 MOOC 课程推荐中的工作。在真实数据集上进行了大量实验，证明容量追踪增强型课程推荐提高了 MOOC 的有效性和可解释性。