Early Predicting of Students Performance in Higher Education

Abstract

Students learning performance is one of the core components for assessing any educational systems. Students performance is very crucial in tackling issues of learning process and one of the important matters to measure learning outcomes. The ability to use data knowledge to improve education systems has led to the development of the field of research known as educational data mining (EDM). EDM is the creation of techniques to investigate data gathered from educational settings, allowing for a more thorough and accurate understanding of students and the improvement of educational outcomes for them. The use of machine learning (ML) technology has increased significantly in recent years. Researchers and teachers can use the measurements of success, failure, dropout, and more provided by the discipline of data mining in education to predict and simulate education processes. Therefore, this work presents an analysis of students performance using data mining methods. The paper presents both clustering and classification techniques to identify the impact of students performance at early stage with on the GPA. For the clustering technique, the paper uses dimensionality reduction mechanism by T-SNE algorithm with various factors at early stage such as admission scores and first level courses, academic achievement tests (AAT) and general aptitude tests (GAT) in order to explore the relationship between these factors and GPA's. For the classification technique, the paper presents experiments on different machine learning models on predicting student performance at early stages using different features including courses' grades and admission tests' scores. We use different assessment metrics to evaluate the quality of the models. The results suggest that educational systems can mitigate the risks of students failures at the early stages.

Exploring the role of machine learning in forecasting student performance in education: An in-depth review of literature

“…The number of records assessed for eligibility was 709. After screening, highly relevant studies were identified (n = 50) (Adnan et al, 2021(Adnan et al, , 2022Alamri & Alharbi, 2021;Alhazmi & Sheneamer, 2023;Aljaloud et al, 2022;Alshanqiti & Namoun, 2020;Asthana et al, 2023;Bujang et al, 2021;Chan et al, 2023;Chui et al, 2020;Feng et al, 2022;Gao et al, 2019;Ghorbani & Ghousi, 2020;Hassan et al, 2022;Hussain et al, 2021;Kastrati et al, 2020;Kusumawardani & Alfarozi, 2023;Latif et al, 2023;Liu et al, 2020;Mengash, 2020;Motz et al, 2021;Munshi & Alhindi, 2021;Nabil et al, 2021;Nguyen-Huy et al, 2022;Okoye et al, 2023;Orlando et al, 2020;Pek et al, 2023;Prabowo et al, 2021;Rafique et al, 2021;Rahman et al, 2021.…”

Closing the gap: exploring the untapped potential of machine learning in deaf students and hearing students’ academic performance

“…The preadmission criteria like high school grade average, scholastic achievement admission test score, and General aptitude test score were considered to predict the student's performance before admission [55]. Uses clustering and classification method for the early prediction of performance [33]. The early prediction of drop outs during the admission may help the teachers to introduce suitable pedagogy for those who needed [56,57].…”

Prediction of PTIK students' study success in the first year using the c4.5 algorithm

“…Students are the most important part ofevaluating the success of the implementation of study programsin tertiary institutions [4]. Students learning performance is one of the core components for assessing any educational systems [5]. At the university level, students are actively involved in teaching and learning activities through existing media such as journals, the internet and the library.…”