- author = "Nor Faezah Mohamad Razi and Suraya Masrom and Norhayati Baharun and Anis Zafirah Azmi",
- title = "An Integrated Model for Predicting Student Achievement Efficiency Using Data Envelopment Analysis and Genetic Programming Approach",
- booktitle = "2024 5th International Conference on Artificial Intelligence and Data Sciences (AiDAS)",
- year = "2024",
- pages = "245--255",
- month = sep,
- keywords = "genetic algorithms, genetic programming, Surveys, Adaptation models, Analytical models, Accuracy, Pipelines, Predictive models, Data models, Robustness, Data envelopment analysis, Machine Learning, Predictive model, Efficiency measurement",
-
DOI = "
doi:10.1109/AiDAS63860.2024.10729908",
- abstract = "Data Envelopment Analysis (DEA) is effective in evaluating efficiency across various domains, including education. However, traditional DEA methods face challenges such as computational complexity and limited predictive capabilities. This study addresses these limitations by integrating Automated Machine Learning (AutoML) using Genetic Programming (GP) with DEA to enhance the prediction and efficiency analysis of academic performance among final year diploma students. The objective is to develop and evaluate an integrated framework that leverages AutoML and DEA to improve the accuracy and generalisation of efficiency predictions. The study investigates the impact of varying population sizes, mutation rates, and crossover rates within the AutoML framework on model performance. Methodology involves collecting data from 1,099 final year diploma students through surveys measuring their CGPA, satisfaction, and various competencies. Four CCR and BCC models were constructed using different input-output combinations, with Model BCC1 identified as the best model. Cross-validation was employed to mitigate overfitting. Result revealed increasing population sizes generally improved performance, with error values decreasing for both datasets. The best-performing pipelines included RandomForestRegressor and XGBRegressor model by highlighting their robustness and adaptability. In conclusion, integrating GP-based AutoML with DEA provides a promising approach to enhancing efficiency analysis of academic performance. Future research should focus on addressing overfitting through other strategies such as regularization and extending this methodology to other domains for further validation and improvement.",
-
notes = "Also known as \cite{10729908}
School of Mathematical Sciences, College of Computing, Informatic and Mathematics, Universiti Teknologi MARA, Tapah Road, Malaysia",
