About: The use of Massive Open Online Courses (MOOCs) is rapidly increasing due to the convenience and ease that provide to learners. However, MOOCs suffer from high drop out rate owing mostly to the confusion and frustration going with the learning process. Based on MOOCs discussion forums, this paper aims to explore different levels of confusion in specific concept using prerequisite based ontology for extracting relevant posts, and Bidirectional Encoder Representations from Transformers (BERT) classification algorithm to describe the degree of confusion for each post. The analysis of discussion posts from Stanford University dataset affirms the effectiveness of our model. BERT achieve good classification accuracy; this will help in early drop out detection and also facilitate future support for learners in confusion state.   Goto Sponge  NotDistinct  Permalink

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  • The use of Massive Open Online Courses (MOOCs) is rapidly increasing due to the convenience and ease that provide to learners. However, MOOCs suffer from high drop out rate owing mostly to the confusion and frustration going with the learning process. Based on MOOCs discussion forums, this paper aims to explore different levels of confusion in specific concept using prerequisite based ontology for extracting relevant posts, and Bidirectional Encoder Representations from Transformers (BERT) classification algorithm to describe the degree of confusion for each post. The analysis of discussion posts from Stanford University dataset affirms the effectiveness of our model. BERT achieve good classification accuracy; this will help in early drop out detection and also facilitate future support for learners in confusion state.
subject
  • E-learning
  • Classification algorithms
  • Statistical classification
  • Higher education
  • Educational technology
  • Free software
  • Computer-related introductions in 2008
  • Open educational resources
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