Problem Statement: There has recently been interest in educational databases containing a variety of valuable but sometimes hidden data that can be used to help less successful students to improve their academic performance. The extraction of hidden information from these databases often implements aspects of the educational data mining (EDM) theory, which aims to study available data in order to shed light on more valuable, hidden information. Data clustering, classification, and regression methods such as K-means clustering, neural networks (NN), extreme learning machine (ELM), and support vector machines (SVM) can be used for to predict aspects of the educational data. EDM outputs can ultimately identify which students will need additional help to improve their grade point averages (GPAs) at graduation.
Purpose of Study: This study aims to implement several prediction techniques in data mining to assist educational institutions with predicting their students’ GPAs at graduation. If students are predicted to have low GPAs at graduation, then extra efforts can be made to improve their academic performance and, in turn, GPAs.
Methods: NN, SVM, and ELM algorithms are applied to data of computer education and instructional technology students to predict their GPAs at graduation.
Findings and Results: A comparative analysis of the results indicates that the SVM technique yielded more accurate predictions at a rate of 97.98%. By contrast, the ELM method yielded the second most accurate prediction rate (94.92%) evaluated based on the criterion of correlation coefficient. NN reported the least accurate prediction rate (93.76%).
Conclusions and Recommendations:The use of data mining methodologies has recently expanded for a variety of educational purposes. The assessment of students’ needs, dropout liability, performance, and placement test improvement are some important emerging data mining applications in education. Since educational institutions have several seemingly unsolvable domain-related problems, this study’s results reveal that EDM can assist with how educational institutions analyze and solve these problems. Furthermore, ensemble models can be used to obtain improved results, while feature selection algorithms can be used to reduce the computational complexity of the prediction methods.
Keywords: GPA prediction, educational data mining, prediction methods, higher education