Predicting Student Test Performance based on Time Series Data of eBook Reader Behavior Using the Cluster Distance Space Transformation

Abstract

This paper describes our participation in the task of predicting student performance at the learning analytics workshop which is hosted at the ICCE2018 conference. The task provides two datasets consisting of student time series click data behavior from an eBook reader. The goal is to predict the score and to predict whether a student passes the course or not. We transformed the time series data of student eBook actions in different features for the regression and the classification task. Among many feature subsets examined, feature subsets that have emerged through t-test, f-regression, and random forest regression have delivered comparatively better results. After an extensive feature engineering, we tried a new approach, based on k-Means, which transforms the selected features into the cluster-distance space. We evaluated the original and resulting features with different classifiers and regressors. For both datasets and both problems (regression and binary classification), the feature sets created with the cluster-distance space transformation have delivered better results.