Predictive Modeling in Higher Education: Determining Factors of Academic Performance

Abstract

For several decades in the field of data mining in education (EDM), predictive learning has remained one of the most popular and internationally discussed research topics. Specifically, data mining is used to predict educational outcomes such as academic performance, retention, success, satisfaction, achievement and dropout rates. In the management practice of higher education institutions, on the basis of an operational forecast, measures are developed and implemented to support those students who fall into the risk group.Our study is aimed at substantiating a model for predicting the early departure of students using an artificial neural network and analyzing predictors that increase the accuracy of predicting successful graduation from a Russian university. This work will expand the international practice of comparative research in higher education.The paper confirms the already existing hypotheses about the influence of a number of factors on the prediction of academic performance and suggests the need to test their universality or specificity in a particular institution of higher education. We also proved that an artificial neural network model with a certain set of attributes can be applied in the context of a single higher education institution, regardless of specialization. To determine the potential risk group of students, a binary classification prediction model is used. The overall prediction accuracy of a neural network with combined data reaches 88%. For this neural network model, the basic predictors that affect the accuracy of the forecast are the cumulative average level of achievement (CGPA) and the year of admission to the university.

Hybrid Approach to Predicting Learning Success Based on Digital Educational History for Timely Identification of At-Risk Students

“…For example, in reference [17], the task of forecasting overall academic performance is addressed using data on students' socio-economic status and entrance exam results at a Chinese university, with a four-layer Artificial Neural Network (ANN) being used as the forecasting model. In the study [18], three-layer ANNs are also employed solely for predicting completion of an educational program (forecasting the risk of long-term dropout). The Graduate Grade Point Average (GGPA), the average performance score for the first semester of study, and the year of enrollment at the university were identified as the most important predictors of academic performance.…”