Improving prediction of students' performance in intelligent tutoring
  systems using attribute selection and ensembles of different multimodal data
  sources
W. Chango R. Cerezo M. Sanchez-Santillan R. Azevedo C. Romero
The aim of this study was to predict university students' learning
performance using different sources of data from an Intelligent Tutoring
System. We collected and preprocessed data from 40 students from different
multimodal sources: learning strategies from system logs, emotions from face
recording videos, interaction zones from eye tracking, and test performance
from final knowledge evaluation. Our objective was to test whether the
prediction could be improved by using attribute selection and classification
ensembles. We carried out three experiments by applying six classification
algorithms to numerical and discretized preprocessed multimodal data. The
results show that the best predictions were produced using ensembles and
selecting the best attributes approach with numerical data.