# Modeling and predicting students' engagement behaviors using mixture Markov models

arXiv: https://arxiv.org/abs/2403.05556 · HTML: https://mlsanity.com/paper/2403.05556/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 102 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 2 of 2)

- [Engaging with Massive Online Courses](https://mlsanity.com/paper/1403.3100/) (2014)
- [Sequence Modelling For Analysing Student Interaction with Educational Systems](https://mlsanity.com/paper/1708.04164/) (2017)

## Abstract (arXiv)

Students' engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A pre-requirement for stimulating student engagement lies in the capability to have an approximate representation model for comprehending students' varied (dis)engagement behaviors. In this paper, we utilized model-based clustering for this purpose which generates K mixture Markov models to group students' traces containing their (dis)engagement behavioral patterns. To prevent the Expectation-Maximization (EM) algorithm from getting stuck in a local maxima, we also introduced a K-means-based initialization method named as K-EM. We performed an experimental work on two real datasets using the three variants of the EM algorithm: the original EM, emEM, K-EM; and, non-mixture baseline models for both datasets. The proposed K-EM has shown very promising results and achieved significant performance difference in comparison with the other approaches particularly using the Dataset. Hence, we suggest to perform further experiments using large dataset(s) to validate our method. Additionally, visualization of the resultant clusters through first-order Markov chains reveals very useful insights about (dis)engagement behaviors depicted by the students. We conclude the paper with a discussion on the usefulness of our approach, limitations and potential extensions of this work.