Personalized Thread Recommendation for MOOC Discussion Forums

Abstract

Social learning, i.e., students learning from each other through social interactions, has the potential to significantly scale up instruction in online education. In many cases, such as in massive open online courses (MOOCs), social learning is facilitated through discussion forums hosted by course providers. In this paper, we propose a probabilistic model for the process of learners posting on such forums, using point processes. Different from existing works, our method integrates topic modeling of the post text, timescale modeling of the decay in post activity over time, and learner topic interest modeling into a single model, and infers this information from user data. Our method also varies the excitation levels induced by posts according to the thread structure, to reflect typical notification settings in discussion forums. We experimentally validate the proposed model on three real-world MOOC datasets, with the largest one containing up to 6,000 learners making 40,000 posts in 5,000 threads. Results show that our model excels at thread recommendation, achieving significant improvement over a number of baselines, thus showing promise of being able to direct learners to threads that they are interested in more efficiently. Moreover, we demonstrate analytics that our model parameters can provide, such as the timescales of different topic categories in a course.

Innovative Tools to Assess a Large Number of Students in the Open Distance and e-Learning MOOCs

“…However, some researchers do not associate discussion forum with assessment. Lan, Spencer, Chen, Brinton and Chiang [23] posit that discussion forums are tools to facilitate social learning in MOOCs. Similarly, Onah, Sinclair, and Boyatt [24] view discussion forums as a primary means of interaction among students and teachers in MOOCs.…”

A Systematic Mapping Review on MOOC Recommender Systems

“…Furthermore, Gusmão et al [166] presented a model of a custom forum activity that uses the ontology of tags to classify posts. Similarly, Lan et al [132] proposed point process while Zhang et al [152] used self-attention mechanism for thread recommendation, while Yang et. al [61] used an adaptive matrix factorization approach combined with content level modeling.…”

“…Machine learning algorithms have also played role in Social recommendation as Williams et al [77] used thomas sampling for email recommendation, Rahma and Kouthe air [139] proposed random forest for forum answer recommendation, Bouzayane and Saad [121] utilized dominance-based rough set approach (DBRSA) for leader recommendation. Similarly, Mi and Faltings [101] used context tree for MOOC forum recommendation, Lan et al [132] proposed point process and Zhang et al [152] used self-attention mechanism for thread recommendation. Apart from that, ML algorithms are adopted for Learning resource recommendation as well.…”

Automated Scoring System for Online Discussion Forum Using Machine Learning and Similarity Measure

“…The multinomial Naïve Bayes classifier was found to give the most accurate result. Andrew et al [9] use point processes to recommend threads to learners on forums. It models the probability that a learner contributes to a thread based on four factors, viz.…”