# Markov Decision Process for MOOC users behavioral inference

arXiv: https://arxiv.org/abs/1907.04723 · HTML: https://mlsanity.com/paper/1907.04723/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jul 2019.

## Built on by (most cited first, 2 of 2)

- [A Generalised Inverse Reinforcement Learning Framework](https://mlsanity.com/paper/2105.11812/) (2021)
- [Unsupervised Neural Hidden Markov Models with a Continuous latent state space](https://mlsanity.com/paper/2106.06536/) (2021)

## Abstract (arXiv)

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.