揭示隐藏的模式--MOOC学生亚群的比较研究
Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students
摘要：
大规模开放式在线课程（MOOC）显示出学生的显著异质性。来自MOOC平台的复杂“大数据”的出现是一个具有挑战性但有益的机会，可以深入了解学生如何参与MOOC。过去的研究主要关注整体行为，可能错过了与学生多样性有关的模式。使用FutureLearn提供的MOOC中的大型数据集，我们深入研究了一种通过机器学习和统计建模来研究隐藏模式的新方法。在本文中，我们报告的聚类分析的学生活动和比较分析的行为模式和人口统计学模式的学生亚群之间的MOOC。我们的方法可以更深入地了解MOOC学生的行为和成就。我们的研究结果可用于设计增强MOOC体验的自适应策略
英文摘要：
Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex "big data" from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research, looking mainly into overall behavior, may have missed patterns related to student diversity. Using a large dataset from a MOOC offered by FutureLearn, we delve into a new way of investigating hidden patterns through both machine learning and statistical modelling. In this paper, we report on clustering analysis of student activities and comparative analysis on both behavioral patterns and demographical patterns between student subpopulations in the MOOC. Our approach allows for a deeper understanding of how MOOC students behave and achieve. Our findings may be used to design adaptive strategies towards an enhanced MOOC experience
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。
