Markov Decision Process for MOOC users behavioral inference
This work addresses the challenge of understanding and classifying student behaviors in MOOCs, which is incremental as it applies an existing MDP framework to a specific educational domain.
The paper tackles the problem of modeling and classifying MOOC user behaviors from log data by framing user intentions as rewards within a Markov Decision Process, resulting in a method to infer and categorize user profiles.
Studies on massive open online courses (MOOCs) users discuss the existence of typical profiles and their impact on the learning process of the students. However defining the typical behaviors as well as classifying the users accordingly is a difficult task. In this paper we suggest two methods to model MOOC users behaviour given their log data. We mold their behavior into a Markov Decision Process framework. We associate the user's intentions with the MDP reward and argue that this allows us to classify them.