Abstract: Intelligent tutoring systems serve as tools capable of providing personalized learning experiences, with their efficacy significantly contingent upon the performance of recommendation models. For long-term instructional plans, these systems necessitate the provision of highly accurate, enduring recommendations. However, numerous existing recommendation models adopt a static perspective, disregarding the sequential decision-making nature of recommendations, rendering them often incapable of adapting to novel contexts. While some recent studies have delved into sequential recommendations, their emphasis predominantly centers on short-term predictions, neglecting the objectives of long-term recommendations. To surmount these challenges, this paper introduces a novel recommendation approach based on deep reinforcement learning. We conceptualize the recommendation process as a Markov Decision Process, employing recurrent neural networks to simulate the interaction between the recommender system and the students. Test results demonstrate that our model not only significantly surpasses traditional Top-N methods in hit rate and NDCG concerning the enhancement of long-term recommendations but also adeptly addresses scenarios involving cold starts. Thus, this model presents a new avenue for enhancing the performance of intelligent tutoring systems.

Wei Wang, “Long-Term Recommendation Model for Online Education Systems: A Deep Reinforcement Learning Approach” International Journal of Advanced Computer Science and Applications(IJACSA), 16(2), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160237

@article{Wang2025,
title = {Long-Term Recommendation Model for Online Education Systems: A Deep Reinforcement Learning Approach},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160237},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160237},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {2},
author = {Wei Wang}
}