Context-Embedded Knowledge Tracing and Latent Concept Detection in a Reading Game

This study investigates the application of knowledge tracing to the domain of reading comprehension, a complex field characterized by rich contextual data and interrelated concepts. We propose adapting the Dynamic Key-Value Memory Networks (DKVMN) model to incorporate sentence embeddings to better capture the semantic richness of reading tasks, naming our new model Context-embedded DKVMN (CDKVMN). The study employs an extant dataset of 405 students that each completed the reading game "Map Conquest."This game was designed to evaluate students' mastery and use of key reading strategies, such as paraphrasing and bridging. Our findings indicate that CDKVMN outperforms Deep Knowledge Tracing and performs similarly or better than DKVMN in predicting students' performance. This research underscores the potential of advanced, context-sensitive knowledge tracing models to track students' mastery of reading strategies, which can be used to provide support and adapt learning activities to the user. Future work will focus on refining the contextual embeddings, expanding the dataset to a variety of reading games, and interpreting the detected latent concepts.