Enhancing Education Through Gamification Using Data-Driven Techniques: A Comprehensive - Narayanan, K. Sankara; Kumaravel, A. (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Modern education is constantly seeking ways to optimize the learning experience. One such way is through the development of sophisticated recommendation systems that are tailored to meet the unique needs and preferences of individual students. In this study, a personalized recommendation system is developed for modern education that uses a large dataset encompassing students’ academic performance, interactions with educational resources, and personal preferences. This recommendation system is powered by the Hybrid Neural Recommendation Algorithm (HyNRA), which combines Collaborative Filtering (CF) and Content-Based Filtering (CBF) methodologies in a neural network model. A robust data security model is also introduced to maintain the security and privacy of student data. Additionally, real-time anomaly detection is a crucial part of this research, with the Isolation Forest Student Performance Anomaly Detection (IF-SPAD) Algorithm at its core. The results of this study are presented through comprehensive performance metrics, visualized engagement trends, real-time anomaly detection outcomes, and the impact of interventions over time. Results show a steady increase in student engagement over time.