Knowledge-Tracing for Estimating the Next Action of Users to Analyze Behavioral Data and Identify Patterns

Due to the increased use of electronic devices for knowledge and skill transfer through games, it is essential to enhance user engagement using artificial intelligence and augmented reality. Users often seek convenience in learning and may lose interest if they face difficulties. This study proposes a new method to track knowledge transfer and predict user actions, aiming to maintain user enthusiasm. By analyzing frequent patterns from user interactions, the intelligent system estimates future events and presents tasks likely to yield correct answers. Tested on the PSPGP dataset from Kaggle, the method outperforms basic estimation techniques, achieving ۸۰% accuracy even with null data.

کلیدواژه ها:

Pattern mining ، Prediction ، knowledge tracing