Title
Modeling and predicting students’ engagement behaviors using mixture Markov models
Abstract
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 Dataset1. 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.
Year
DOI
Venue
2022
10.1007/s10115-022-01674-9
Knowledge and Information Systems
Keywords
DocType
Volume
Student engagement behavior, Mixture Markov models, Model-based clustering, Expectation–Maximization algorithm, K-means clustering, Sequential traces, Categorical data
Journal
64
Issue
ISSN
Citations 
5
0219-1377
0
PageRank 
References 
Authors
0.34
16
4
Name
Order
Citations
PageRank
Rabia Maqsood100.34
Paolo Ceravolo200.34
Cristóbal Romero32226148.97
S. Ventura42318158.44