![]() ![]() This predictive power suggests that NLP can help us both to understand student retention in MOOCs and to develop automated signals of student success.Ī review is textual feedback provided by a reviewer to the author of a submitted version. The findings indicate that the language produced by students can predict with substantial accuracy (67.8 %) whether students complete the MOOC. The analysis is applied to a subsample of 320 students who completed at least one graded assignment and produced at least 50 words in discussion forums. This study uses natural language processing (NLP) to examine if the language in the discussion forum of an educational data mining MOOC is predictive of successful class completion. Previous research into predicting MOOC completion has focused on click-streams, demographics, and sentiment analysis. By studying students who drop out despite their intent to complete the MOOC, it may be possible to develop interventions to improve retention and learning outcomes. We finally derive practical advice from our extensive empirical and model interpretability results for those interested in key phrase classification from educational reports in the future.Ĭompletion rates for massive open online classes (MOOCs) are notoriously low, but learner intent is an important factor. ![]() Both pretrained language models and simple TFIDF SVM classifiers produce similar results with a former producing average of 0.6 F1 higher than the latter. From this study, we find that the task of classification of key phrases is ambiguous at a human level producing Cohen's kappa of 0.77 on a new data set. As such with the hope of identifying important contents needed for the review, in this work we present a very first work on key phrase classification with a detailed empirical study on traditional and most recent language modeling approaches. With the sheer scale of these programs comes a variety of problems in peer and expert feedback, including rogue reviews. Complex assignments typically consist of open-ended questions with large and diverse content in the context of both classroom and online graduate programs. ![]()
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