We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.

Durham Research Online
You are in:

A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts

Alrajhi, Laila and Alharbi, Khulood and Cristea, Alexandra I. (2020) 'A Multidimensional Deep Learner Model of Urgent Instructor Intervention Need in MOOC Forum Posts.', in ITS 2020: Intelligent Tutoring Systems. , pp. 226-236. Lecture Notes in Computer Science., 12149


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.

Item Type:Book chapter
Full text:(AM) Accepted Manuscript
Download PDF
Publisher Web site:
Publisher statement:The final authenticated version is available online at
Date accepted:No date available
Date deposited:03 November 2021
Date of first online publication:03 June 2020
Date first made open access:03 November 2021

Save or Share this output

Look up in GoogleScholar