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.
