OKLM: Open Knowledge and Learner Model Using Educational Big Data

Abstract

This study proposes the Open Knowledge and Learner Model (OKLM), a novel framework that integrates Learning Analytics (LA) with Digital Twin (DT) technology to model learners' knowledge, internal states, and environments. The OKLM DT framework addresses the limitations of traditional LA systems by enabling accurate estimation of knowledge states and personalized learning strategies. We developed a conceptual framework for the learner DT using LA, verified the accuracy of the OKLM-based DT model, and applied it to a learning support system. Initial experiments in an English literature recommendation system showed that, while the recommendations did not significantly enhance learners' motivation, they were well-received and positively correlated with increased engagement among highly motivated learners. A subsequent study involving Intensive Reading (IR) support for EFL learners further validated the model's effectiveness. Additionally, experiments targeting educators demonstrated that the OKLM's visualization tools were valuable for understanding learner characteristics and tailoring teaching materials. These findings suggest that OKLM can enhance the versatility and accuracy of learner models across various educational contexts, offering a significant advancement in the field of LA.