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

Chuang Liu (), Haojie Wang and Zhonghu Yuan
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Chuang Liu: School of Information Engineering, Shenyang University, Shenyang 110044, China
Haojie Wang: School of Information Engineering, Shenyang University, Shenyang 110044, China
Zhonghu Yuan: School of Information Engineering, Shenyang University, Shenyang 110044, China

Mathematics, 2022, vol. 10, issue 20, 1-19

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.

Keywords: students’ achievement; educational data mining; education reform; machine learning; spiking neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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