Massive Open Online Courses (MOOCs) are emerging as a popular alternative to traditional education, offering learners the flexibility to access a wide range of courses from various disciplines, anytime and anywhere. Despite this accessibility, a significant number of enrollments in MOOCs result in dropouts. To enhance learner engagement, it is crucial to recommend courses that align with their preferences and needs. Course Recommender Systems (RSs) can play an important role in this by modeling learners' preferences based on their previous interactions within the MOOC platform. Time-to-dropout and time-to-completion in MOOCs, like other time-to-event prediction tasks, can be effectively modeled using survival analysis (SA) methods. In this study, we apply SA methods to improve collaborative filtering recommendation performance by considering time-to-event in the context of MOOCs. Our proposed approach demonstrates superior performance compared to collaborative filtering methods trained based on learners' interactions with MOOCs, as evidenced by two performance measures on three publicly available datasets. The findings underscore the potential of integrating SA methods with RSs to enhance personalization in MOOCs.

翻译：大规模开放在线课程（MOOCs）作为传统教育的流行替代方案正在兴起，为学习者提供了随时随地灵活获取多学科广泛课程的途径。尽管具有这种可访问性，MOOCs中仍有大量注册最终导致退课。为提高学习者参与度，推荐符合其偏好和需求的课程至关重要。课程推荐系统（RSs）可通过基于学习者在MOOC平台上的历史交互行为建模其偏好，在此过程中发挥重要作用。与其它事件时间预测任务类似，MOOCs中的退课时间与完成时间可有效利用生存分析（SA）方法进行建模。本研究通过考虑MOOCs情境中的事件时间，应用SA方法来提升协同过滤推荐性能。我们在三个公开数据集上采用两种性能指标验证表明，相较于仅基于学习者与MOOCs交互行为训练的协同过滤方法，我们提出的方法展现出更优的性能。这些发现强调了将SA方法与RSs相结合以增强MOOCs个性化推荐的潜力。