A data mining approach for predicting academic success – a case study

The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure.This work was supported by the Portuguese Foundation for Science and Technology (FCT) under Project UID/EEA/04131/2013. The authors would also like to thank the Polytechnic Institute of Bragan¸ca for making available the data analysed in this study.info:eu-repo/semantics/publishedVersio

Students' performance prediction model using meta-classifier approach

Students’ performance is vitally important at all stages of education, particularly for Higher Education Institutions. One of the most important issues is to improve the performance and quality of students enrolled. The initial symptom of at-risks’ students need to be observed and earlier preventive measures are required to be carried out so as to determine the cause of students’ dropout rate. Hence, the purpose of this research is to identify factors influencing students’ performance using educational data mining techniques. In order to achieve this, data from different sources is employed into a single platform for pre-processing and modelling. The design of the study is divided into 6 different phases (data collection, data integration, data pre-processing such as cleaning, normalization, and transformation, feature selection, patterns extraction and model optimization as well as evaluation. The datasets were collected from a students’ information system and e-learning system from a public university in Malaysia, while sample data from the Faculty of Engineering were used accordingly. This study also employed the use of academic, demographical, economical and behaviour e-learning features, in which 8 different group models were developed using 3 base-classifiers; Decision Tree, Artificial Neural Network and Support Vector Machine, and 5 multi-classifiers; Random Forest, Bagging, AdaBoost, Stacking and Majority Vote classifier. Finally, the highest accuracy of the classifier model was optimized. At the end, new Students’ Performance Prediction Model was developed. The result proves that combination demographics with behaviour using a meta-classifier model with optimized hyper parameter produced better accuracy to predict students’ performance