Modeling and predicting students' engagement behaviors using mixture
  Markov models
R. Maqsood P. Ceravolo C. Romero S. Ventura
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.