MONITORING OF THE EDUCATION QUALITY AND IMPLEMENTING OF INDIVIDUAL LEARNING: DEMONSTRATION OF APPROACHES AND EDUCATIONAL DATA MINING ALGORITHMS

Abstract

The quality monitoring system for traditional and distance education requires the development
of machine learning classification and quantification techniques necessary to predict individual
and collective student performance. This article theoretically and experimentally shows that
the most promising approach that simultaneously solves both forecast tasks is to create heterogeneous
ensembles consisting of an odd number of different base classifiers, such as decision trees,
simple neural networks, naive Bayesian classifier and others. By training and testing 11 different
binary classifiers on six different samples of educational data, we show that the individual determined
forecast of such ensembles exceeds the accuracy of forecasts of both individual base classifiers
and homogeneous ensembles created by bagging and busting technologies. The advantage of
heterogeneous ensembles is decisive when we deal with the imbalance of sample characteristic ofeducational data. In these cases, only the forecasts with accuracies exceeding the relative frequency
of the class of objects dominating in the sample of data can be considered as useful forecasts.
The main advantage of the heterogeneous ensemble is the ability to transform the deterministic
forecast into a probabilistic forecast, when instead of referring the object to a particular class, the
probability of its belonging to individual classes is given. On this basis, we have proposed a new
method of binary quantification, where individual probabilities of belonging to each of the classes
of objects are summed up separately, and the resulting total probabilities are interpreted as relative
frequencies of objects in the sample. As a result of experiments, it is shown that such ensemble
binary quantification is significantly superior to the traditional "classify and count" method.