An Effective and Robust Self-Training Algorithm Using k-Means and Random Forest Models for Program-Level Student Classification

In educational data mining, program-level student classification is a popular task focusing on predicting the final study status of a student at the end of the permitted period of study time with a program. In a practical context where the task is applied to predict students early in their learning path, two issues have to be addressed. The first issue is the completeness of data in both model construction and prediction of new data, while the latter refers to the number of labeled data in model construction. Nonetheless, a few works considered this practical context thoroughly. In order to manage these issues, we propose a self-training algorithm using
$k$
-means and random forest models, named
$k$
-meansTriForest. Our algorithm is regarded as a solution to the program-level student classification task in the aforementioned practical context.
$k$
-means helps us form a cluster-based incomplete data imputation method. Random forests play an important role of base classifiers of an ensemble in our algorithm. Besides, the Tri-training styled unlabeled instance selection is defined so that our algorithm can remove the burden of probability threshold settings from its users. Moreover, in our algorithm, the diversity of the base classifiers can be preserved when the unlabeled data set can be exploited and the labeled data set can be enhanced over time. As a result, our algorithm outperforms the previous ones in the related works on three real educational data sets, in order to classify students into the groups with different final study status at three different points in time. In particular, our improvements in Accuracy are ranged from about 1.5% to about 7%. Such higher accuracy in an empirical study with many various incomplete data and unlabeled data percentages has confirmed our algorithm's effectiveness and robustness.