Intelligent campus score analysis method based on association rules and neural network

Shihao Li; Ze Yang; Xiaoyun He

doi:10.1117/12.2644566

10 November 2022 Intelligent campus score analysis method based on association rules and neural network

Shihao Li, Ze Yang, Xiaoyun He

Proceedings Volume 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022); 123011Z (2022) https://doi.org/10.1117/12.2644566
Event: 6th International Conference on Mechatronics and Intelligent Robotics, 2022, Kunming, China

Abstract

In order to solve the problem of insufficient ability of campus achievement analysis, this paper puts forward a complete set of intelligent campus achievement analysis methods. Firstly, the method collects and preprocesses the students' achievement data of each subject, uses the Apriori association rule algorithm to select the main pre courses that affect the students' comprehensive assessment results, and filters the irrelevant pre courses; Then, the neural network algorithm is used to construct the score prediction model, convert the scores corresponding to the main pre courses into feature vectors, take the students' comprehensive examination scores as labels, and use the feature vectors and labels to train the score prediction model; Third, use the trained model to predict the students' comprehensive examination results; Finally, if the predicted comprehensive assessment result fails, send an early warning notice of the failure of the comprehensive assessment result to the student in advance, and remind the student to strengthen the study of the main pre courses. Experiments show that the method proposed in this paper performs well in achievement analysis and prediction, and is conducive to promoting the improvement of students' achievement.

Citation Download Citation

Shihao Li, Ze Yang, and Xiaoyun He "Intelligent campus score analysis method based on association rules and neural network", Proc. SPIE 12301, 6th International Conference on Mechatronics and Intelligent Robotics (ICMIR2022), 123011Z (10 November 2022); https://doi.org/10.1117/12.2644566

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