Predictive analytics of student performance: Multi-method and code

The maintenance of a high level of education in universities can be a challenging task due to low academic performance. Despite the significant amount of collected diagnostic data, education managers underutilize machine learning methods to improve the accuracy of predicting academic performance. Authors apply a multi-method approach for data analysis using simple logistic and linear regressions, k-means clustering, that all together gave a synergetic effect. The proposed approach differs from known analogs in that, firstly, the dimensionality of the feature space increases due to the normalization of scores onto a single scale and the creation of new features: the index and rank of students, as well as the changes in performance across various activities for each student. Secondly, students at academic risk are forecasted, and the statistical significance of the features included in the model is evaluated. Thirdly, for each student, the final score for the semester is forecasted using an linear regressive model of academic performance. Fourthly, groups of students with similar learning trajectories are identified for customization of consultations. The authors managed to achieve a high predictive ability of models based on historical training data: binary prediction of exam passing in 90% of cases, prediction of individual assessment in 70% of cases.