Artificial intelligence-enabled prediction model of student academic performance in online engineering education

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 ar...

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Vydáno v:The Artificial intelligence review Ročník 55; číslo 8; s. 6321 - 6344
Hlavní autoři: Jiao, Pengcheng, Ouyang, Fan, Zhang, Qianyun, Alavi, Amir H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.12.2022
Springer
Springer Nature B.V
Témata:
Academic achievement
Analysis
Artificial Intelligence
CAI
Computation
Computer assisted instruction
Computer Science
Data
Distance learning
Education
Engineering
Engineering education
Engineering schools
Evolutionary computation
Genetic algorithms
Genetics
Internet
Knowledge acquisition
Learning
Online education
Performance evaluation
Performance prediction
Prediction models
Predictions
Students
Teaching
Variables
Online engineering education
Higher education
Online learning
Artificial intelligence
Collaborative learning
Prediction model
ISSN:0269-2821, 1573-7462
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Shrnutí: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.
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ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-022-10155-y