Unfolding Sentimental and Behavioral Tendencies of Learners' Concerned Topics From Course Reviews in a MOOC

Abstract

Course reviews, which is designed as an interactive feedback channel in Massive Open Online Courses, has promoted the generation of large-scale text comments. These data, which contain not only learners' concerns, opinions and feelings toward courses, instructors, and platforms but also learners' interactions (e.g., post, reply), are generally subjective and extremely valuable for online instruction. The purpose of this study is to automatically reveal these potential information from 50 online courses by an improved unified topic model Behavior-Sentiment Topic Mixture, which is validated and effective for detecting frequent topics learners discuss most, topics-oriented sentimental tendency as well as how learners interact with these topics. The results show that learners focus more on the topics about course-related content with positive sentiment, as well as the topics about course logistics and video production with negative sentiment. Moreover, the distributions of behaviors associated with these topics have some differences.

The Motives and Challenges of developing and delivering MOOCs courses

“…McDonald and Ahern (2015) described MOOC as a unique type of online educational course which is popularized. Liu et al (2019) added that MOOCs provide new sources of information and opportunities for large-scale experiments that can advance teaching and learning.…”

Academic Performance Prediction Based on Multisource, Multifeature Behavioral Data

“…• Lifestyle Behaviors (e.g., eating, physical activity, sleep patterns, social tie, and time management) ; and • Learning Behaviors (e.g., class attendance, study duration, library entry, and online learning) ( [7,8,[23][24][25][26][28][29][30][31][32][33][34][35][36][37][38]). For example, [2] investigated the incremental validity of the Big Five personality traits in predicting college GPA.…”

“…By analyzing the effect of the factors influencing academic performance, many systems using data to predict academic performance have been developed in the literature [1][2][3][4]7,8,[13][14][15][16][17][18][19][22][23][24][25][26][27][29][30][31]33,34,[37][38][39][40][41]. For instance, in [8], academic performance was predicted based on passive sensing data and self-reports from students' smart phones.…”

“…In recent years, compared with primary and secondary education (i.e. K12) [6,10,12,17], more and more attentions have been paid to the academic performance prediction for higher education [7][8][9]14,15,[22][23][24][25]27,28,[30][31][32][36][37][38]. The reasons contributing to this phenomenon warrant further investigation and might include the following.…”

“…Therefore, it is relatively easy to track college students' behaviors, e.g. online learning behaviors captured from massive open online courses (MOOC) and small private online courses (SPOC) platforms [30][31][32][36][37][38]. Second, given the diverse range of activities listed above, it could be difficult for college students to maintain balanced, self-discipline, well-being university experiences, including excellent academic performance.…”