A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts
| Autor: | Laila Alrajhi, Alexandra I. Cristea, Khulood Alharbi |
|---|---|
| Přispěvatelé: | Kumar, Vivekanandan, Troussas, Christos |
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Computer science business.industry media_common.quotation_subject Deep learning 05 social sciences 02 engineering and technology Data science Intelligent tutoring system Feeling Intervention (counseling) 0202 electrical engineering electronic engineering information engineering ComputingMilieux_COMPUTERSANDEDUCATION Subject areas 020201 artificial intelligence & image processing 0501 psychology and cognitive sciences Statistical analysis Artificial intelligence business Raw data Dropout (neural networks) media_common |
| Zdroj: | Kumar, Vivekanandan & Troussas, Christos (Eds.). ITS 2020: Intelligent Tutoring Systems. : Springer, pp. 226-236, Lecture Notes in Computer Science, Vol.12149 Intelligent Tutoring Systems ISBN: 9783030496623 ITS |
| Popis: | In recent years, massive open online courses (MOOCs) have become one of the most exciting innovations in e-learning environments. Thousands of learners around the world enroll on these online platforms to satisfy their learning needs (mostly) free of charge. However, despite the advantages MOOCs offer learners, dropout rates are high. Struggling learners often describe their feelings of confusion and need for help via forum posts. However, the often-huge numbers of posts on forums make it unlikely that instructors can respond to all learners and many of these urgent posts are overlooked or discarded. To overcome this, mining raw data for learners’ posts may provide a helpful way of classifying posts where learners require urgent intervention from instructors, to help learners and reduce the current high dropout rates. In this paper we propose, a method based on correlations of different dimensions of learners’ posts to determine the need for urgent intervention. Our initial statistical analysis found some interesting significant correlations between posts expressing sentiment, confusion, opinion, questions, and answers and the need for urgent intervention. Thus, we have developed a multidimensional deep learner model combining these features with natural language processing (NLP). To illustrate our method, we used a benchmark dataset of 29598 posts, from three different academic subject areas. The findings highlight that the combined, multi-dimensional features model is more effective than the text-only (NLP) analysis, showing that future models need to be optimised based on all these dimensions, when classifying urgent posts. |
| Databáze: | OpenAIRE |
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