A Method for Predicting the Academic Performances of College Students Based on Education System Data

Abstract

With the development of university campus informatization, effective information mined from fragmented data can greatly improve the management levels of universities and the quality of student training. Academic performances are important in campus life and learning and are important indicators reflecting school administration, teaching level, and learning abilities. As the number of college students increases each year, the quality of teaching in colleges and universities is receiving widespread attention. Academic performances measure the learning ’effects’ of college students and evaluate the educational levels of colleges and universities. Existing studies related to academic performance prediction often only use a single data source, and their prediction accuracies are often not ideal. In this research, the academic performances of students will be predicted using a feedforward spike neural network trained on data collected from an educational administration system and an online learning platform. Finally, the performance of the proposed prediction model was validated by predicting student achievements on a real dataset (involving a university in Shenyang). The experimental results show that the proposed model can effectively improve the prediction accuracies of student achievements, and its prediction accuracy could reach 70.8%. Using artificial intelligence technology to deeply analyze the behavioral patterns of students and clarify the deep-level impact mechanisms of the academic performances of students can help college educators manage students in a timely and targeted manner, and formulate effective learning supervision plans.

Deep Learning Techniques for Predicting Student’s Academic Performance on Virtual Learning Environments: A Review

“…Research by Liu et al (2022a), used a feedforward spike neural network trained on information gathered from an online learning platform (involving a university in Shenyang) and an educational administration system to predict student grades. Using pertinent student data and course information, this study investigated the prediction of follow-up grades.…”

Fulfilling K-12 Students' Mathematics Needs: Near Peer Teaching and Scientific Intelligent Clustering Tutor Approach

“…The use of grouping, classification, and regression algorithms like decision trees, SVM, and artificial neural networks on variables such as online student performance, face-to-face student performance, and blended learning outcomes can reflect the achievement of good performance [24]. The effectiveness of curriculum characteristics and learning performance prediction models can be obtained by spiking feedforward neural algorithms [25]. The application of feature engineering and instance engineering techniques can detect over 72% of students at risk of dropping out [26].…”

The Information Age for Education via Artificial Intelligence and Machine Learning: A Bibliometric and Systematic Literature Analysis

“…In this collection, we investigate a variety of facets of AI's impact on the education sector. In Liu's study [10], a robust neural network model is used to predict student grades using grade and course data. The transformational potential of AI is illustrated in [11], which revolutionizes school management and enhances the educational experiences of students.…”

“…The outcomes of these studies give a comprehensive understanding of the implications that Artificial Intelligence (AI) will have on the educational system. While most students face obstacles in their individual educational environments, for instance, Liu [10] highlights the challenges connected with data availability and interpretability in prediction tasks. According to Singh [11], new possibilities and limitations in higher education are modifying institutional governance and design.…”