Title: 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.