Abstract
Focusing on the massive open online course (MOOC) platform, the purpose of this study is to realize personalized adaptive learning according to the needs and abilities of each learner. To this end, the author created a personalized adaptive learning behaviour analysis model, and designed a personalized MOOC platform based on the model. Through the analysis of learning behaviours on the MOOC platform, the model digs deep into the pattern of learning behaviours, and lays the basis for personalized intervention in the learning process. The comparison ex-periments show that our prediction method is more accurate than the other predic-tion algorithms. This research sheds new light on the design of learner-specific MOOC platform.
Highlights
A massive open online course (MOOC) [1] is an online course aimed at unlimited participation and open access
The data created by each learner is part of the "big data", and each learner is a producer and a consumer of big data
Most of the existing studies on MOOC data analytics had focused on predicting the drop-out rate or the learner performance, and overlooked the practical use of personalized MOOC platform
Summary
A massive open online course (MOOC) [1] is an online course aimed at unlimited participation and open access. First introduced in 2006, MOOC has developed into a novel and popular platform of distance learning in recent years [2,3,4]. The open access to content, structure and learning goals is an essential feature of early MOOCs [5], which promotes the reuse and remix of resources. Despite the partial similarity in the learning data, the MOOC platform differs greatly from traditional classroom in behaviour collection and analysis. MOOC platform can record various user operations and capture each submission from the user. The platform contains much greater details on user behaviours than traditional classroom. The gigantic amount of data makes it possible to implement the technology of big data analytics in the platform
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