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논문 기본 정보

자료유형
학술저널
저자정보
Jing Pu (Sichuan Agricultural University) Yuke Li (Sichuan Agricultural University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.13 No.5
발행연도
2024.10
수록면
462 - 471 (10page)
DOI
10.5573/IEIESPC.2024.13.5.462

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초록· 키워드

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In the time of boost of the Internet, online education is also thriving, but how to grasp the performance of students in the context of online education has become a major problem in the current online education. In response to the above problems, the study proposes the Principal Component Analysis Algorithm (PCA), Adaptive Network-based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) for secondary school students" performance prediction model, i.e., PCA-GA-ANFIS model, and the performance of PCA-GA-ANFIS, ANFIS and XGBoost were analyzed and compared through experiments. The experiment illustrated that the loss value of PCA-GA-ANFIS model was about 0.15, while the loss values of ANFIS model and XGBoost model were about 0.3 and 0.25, respectively, which were higher than PCA-GA-ANFIS model. The absolute and relative errors of ANFIS model and XGBoost model were about 3.6, 2% and 3.2, 1.8%, respectively; the absolute and relative errors of the PCA-GA-ANFIS model are about 2.2 and 1.6%, respectively, and the minimum absolute error tends to be close to 0, which demonstrated that the forecasting outcomes of the PCA-GA-ANFIS model were closer to the true values. The PCAGA-ANFIS model performed with an average accuracy of 89.4% and precision of 90.3%. The results demonstrated that the PCA-GA-ANFIS model outperformed the other two models in terms of accuracy and precision. Comparatively, the ANFIS and XGBoost models exhibited an average accuracy and precision of approximately 86.8% and 87.8%, and 87.0% and 89.1%, respectively. Therefore, the results showed that the PCA-GA-ANFIS model produced more precise prediction results and better prediction performance than the ANFIS and XGBoost models.
#Achievement prediction #Subtractive clustering #Genetic algorithm #Adaptive fuzzy inference system

목차

Abstract
1. Introduction
2. Literature Review
3. An Optimized Fuzzy Neural Network based Performance Prediction Model for Secondary School Students
4. Fuzzy Neural Network Model Test Results Analysis
5. Conclusion
References

참고문헌 (22)

참고문헌 신청
Abubakari M S, Arifin F, Hungilo G G. Predicting Students' Academic Performance in Educational Data Mining Based on Deep Learning Using TensorFlow. International Journal of Education and Management Engineering, 2020, 10(6):27-33. google schola Ahmed, S. T., Al-Hamdani, R., Croock, M. S. Enhancement of student performance prediction using modified K-nearest neighbor. TELKOMNIKA Indonesian Journal of Electrical Engineering, 2020, 18(4):1777-1783. google schola Asif, M. M., Javed, M. Effect of covid 19 lockdown and online education on health related quality of life in private university students. No conflict of Interest No financial support to disclose. Rawal Medical Journal, 2021, 46(1):3-6. google schola Bag, S., Aich, P., Islam, M. A. Behavioral intention of "digital natives" toward adapting the online education system in higher education. Journal of Applied Research in Higher Education, 2022, 14(1):16-40. google schola Baruah, A. J., Baruah, S. Data Augmentation and Deep Neuro-fuzzy Network for Student Performance Prediction with MapReduce Framework. International Journal of Automation and Computing, 2021, 18(6):981-992. google schola

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