Personalized behavior recommendation: A case study of applicability to 13 courses on edX

Individualized and personalized learning has taken on different forms in the context of digital learning environments. In intelligent tutoring systems, individualization is focused on estimation of the cognitive mastery of the student and the speed at which the student progresses through the material is conditioned on her individual rate of mastery. In prior work, a recommendation framework based on learner behaviors, rather than learner's cognitive abilities, was proposed and developed. This framework trained a behavior model on millions of previous student actions in order to estimate how a future learner might behave. This behavior model can incorporate the amount of time spent on each course page, such that the model can take into account a learner's previous behaviors and provide a specific course page recommendation to where the learner may want to go next where they can be expected to spend a significant amount of time on. We stipulate that this approach touches on factors more aligned with personalization, since the prediction of behavior is an aggregation of the student's cognitive abilities, affective state, and preferences. This model was applied to a hand-picked pair of MOOC offerings where model results were expected to be favorable. In this paper, we investigate the suitability of this behavioral prediction approach by applying it to an expanded set of 13 UC Berkeley MOOCs run on the edX platform. Preliminary results from applying the time-augmented Recurrent Neural Network (RNN) based behavior model approach are presented and compared to baseline models. These findings contribute to the discussion of when and in what context this form of collaborative based personalized recommendation is appropriate in MOOCs