Enhancing Educational Outcomes Through AI Powered Learning Strategy Recommendation System

In order to develop intelligent learning recommendation systems, the work identifies the employment of artificial intelligence (AI) techniques, particularly in the educational data mining (EDM) field. The aggregation of such educational data into an efficient analytical system could also assist as an interesting means of education for the students. In fact, it could ultimately advance the direction of education. Sophisticated machine learning methods were employed to analyze various data types, including educational, socioeconomic, and demographic data, to predict student success. In this research, Logistic Regression (LR), Random Forest (RF), Support Vector Machines (SVM), CatBoost, and XGBoost algorithms were considered to build prediction models using a dataset encompassing a wide range of student traits. Robust evaluation metrics, including precision, recall, accuracy, and F1-score, were used to gauge model effectiveness. The results highlighted that RF was the best with accuracy, precision, and recall. Then, a rule engine was built to enhance the system by finding the most efficient learning tactics for students based on their expected future performance. The proposed AI-based personalized recommendation tool shows a substantial step towards enhancing educational decisions. This solution facilitates educators in creating student academic assistance interventions by offering individualized, data-driven learning strategies.

@article{Herath2024,
  title     = {Enhancing Educational Outcomes Through AI Powered Learning Strategy Recommendation System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {10},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Daminda Herath and Chanuka Dinuwan and Charith Ihalagedara and Thanuja Ambegoda},
  doi       = {10.14569/IJACSA.2024.0151075},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151075}
}