A Classification Method of the Learners’ Queries in the Discussion Forum of MOOC to Enhance the Effective Response Rate from Instructors

Abstract

AbstractAlthough the quantity of online learning content in massive open online courses (MOOCs) has dramatically increased, there has been little focus on the discussion between learners and instructors regarding the course content itself.This study proposes a classification method of the learners’ queries in the discussion forum (DF) for enhancing the effective responses from instructors in video-based lectures. Different type of queries along with repetition causes confusion among learners and instructors. Therefore, analyzing the different types of queries was the basic task in this research.We collected data from theoretical and practical courses in computer science with a duration of 12 weeks. The number of registered learners on the theoretical course was 11,973, while for the practical course this figure was 15,645. The number of participants in the DF was 11,555 on the theoretical course and 15,524 on the practical course. Following analysis, we conclude that there is a large number of enrolments on the courses. It can be very challenging for the instructors to answer all the queries. We found that the interest of learners varied and the ratio of content to non-content queries was significantly unbalanced between practical and theoretical courses.To reduce the number of redundant tasks for the instructors and prevent them from having to answer repeated queries, we adopted a filtering method to identify non-content-related queries. This method uses the linguistic features from the queries of DF. Linguistic features showed a considerable role in extracting assignment-related (AR) and time-related (TR) queries. Classification of queries helps the instructor to give more attention to the main subject of study.KeywordsMassive open online coursesDiscussion forumClassification method