# Markov Decision Process for MOOC users behavioral inference

## TL;DR

This paper proposes modeling MOOC users' behavior using Markov Decision Processes to classify users based on their log data and inferred intentions, addressing the challenge of defining typical behaviors.

## Contribution

Introduces two methods to model MOOC user behavior with MDPs, linking user intentions to rewards for classification purposes.

## Key findings

- Successful modeling of user behavior using MDPs
- Effective classification of users based on behavior models
- Framework links user intentions with behavior patterns

## Abstract

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.