MOOC中的人群：大规模学习模式研究
The Crowd in MOOCs: A Study of Learning Patterns at Scale
AI总结：
针对MOOC学习者行为，本文基于3.51亿条活动数据，利用互信息和序列模式挖掘分析时间与选课模式，并据此构建快200倍且性能具竞争力的课程推荐模型。
摘要：
大规模开放在线课程（MOOCs）中学习活动数据日益丰富，使我们能够对学习者的学习行为进行大规模分析。本文分析了一个包含3.51亿次学习活动的数据集，这些活动来自两年内注册超过1600门课程的80万名独立学习者。具体而言，我们利用互信息理论和序列模式挖掘方法，从时间和课程注册两个视角挖掘并识别人群的学习模式。从时间视角看，我们发现学习者连续学习活动之间的时间间隔呈现幂律分布与周期性余弦函数分布的混合。通过量化课程对之间的关系，我们观察到最常被共同注册的课程通常属于同一类别或同一所大学。我们证明这些发现可促进包括课程推荐任务在内的多种应用。一个利用课程注册模式的简单推荐模型与基线模型具有竞争力，同时训练速度快200倍。
英文摘要：
The increasing availability of learning activity data in Massive Open Online Courses (MOOCs) enables us to conduct a large-scale analysis of learners' learning behavior. In this paper, we analyze a dataset of 351 million learning activities from 0.8 million unique learners enrolled in over 1.6 thousand courses within two years. Specifically, we mine and identify the learning patterns of the crowd from both temporal and course enrollment perspectives leveraging mutual information theory and sequential pattern mining methods. From the temporal perspective, we find that the time intervals between consecutive learning activities of learners exhibit a mix of power-law and periodic cosine function distribution. By qualifying the relationship between course pairs, we observe that the most frequently co-enrolled courses usually fall in the same category or the same university. We demonstrate these findings can facilitate manifold applications including recommendation tasks on courses. A simple recommendation model utilizing the course enrollment patterns is competitive to the baselines with 200$\times$ faster training time.
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。
