Personalized Learning Environments: Adapting Content  and Challenges to Enhance User Experience and  Performance

Nikhila Kathiresetty

doi:https://doi.org/10.14445/23488387/IJCSE-V10I11P103

Research Article | Open Access

Volume 10 | Issue 11 | Year 2023 | Article Id. IJCSE-V10I11P103 | DOI : https://doi.org/10.14445/23488387/IJCSE-V10I11P103

Personalized Learning Environments: Adapting Content and Challenges to Enhance User Experience and Performance

Nikhila Kathiresetty, Bipin Yadav, Aakash Joshi

Citation :

Nikhila Kathiresetty, Bipin Yadav, Aakash Joshi, "Personalized Learning Environments: Adapting Content and Challenges to Enhance User Experience and Performance," International Journal of Computer Science and Engineering , vol. 10, no. 11, pp. 13-23, 2023. Crossref, https://doi.org/10.14445/23488387/IJCSE-V10I11P103

Abstract

Personalized Learning Environments, also known as Dynamic Difficulty Adjustment (DDA), are integral to modern video game design, dynamically adapting the game’s difficulty in real-time to boost player engagement and satisfaction. This project centers on crafting an effective DDA system that balances challenge and accessibility based on individual player skills and preferences. It commences with an analysis of existing DDA techniques, identifying limitations. Through a comprehensive review of the literature and player behavior studies, critical factors influencing player experiences, such as skill level, engagement patterns, and psychological states, are identified. A novel DDA algorithm is proposed, employing machine learning and player modeling. It uses real-time player data, like inputs, performance metrics, and behavioral indicators, to adapt game difficulty accurately. Rigorous testing involving players with varying skill levels provides objective and subjective measurements. Results validate the system’s ability to deliver an engaging, non-frustrating experience. Ethical concerns are addressed through privacy measures. This project contributes to game design by presenting a robust DDA system, enhancing player satisfaction through personalized gameplay. The findings can serve as a foundation for future DDA developments, benefiting both developers and players.

Keywords

Artificial Intelligence, Dynamic Difficulty Adjustment, Human-Computer Interaction, Machine Learning, User interface.

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