Artificial intelligence-enabled prediction model of student academic performance in online engineering education
Pengcheng Jiao, Fan Ouyang, Qianyun Zhang, Amir H. Alavi
Abstract
Abstract Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for student academic performance based on students’ learning process and summative data. The prediction criteria are first predefined to characterize and convert the learning data in an online engineering course. An evolutionary computation technique is then used to explore the best prediction model for the student academic performance. The model is validated using another online course that applies the same pedagogy and technology. Satisfactory agreements are obtained between the course outputs and model prediction results. The main findings indicate that the dominant variables in academic performance are the knowledge acquisition, the participation in class and the summative performance. The prerequisite knowledge tends not to play a key role in academic performance. Based on the results, pedagogical and analytical implications are provided. The proposed evolutionary computation-enabled prediction method is found to be a viable tool to evaluate the learning performance of students in online courses. Furthermore, the reported genetic programming model provides an acceptable prediction performance compared to other powerful artificial intelligence methods.