Knowledge interaction enhanced sequential modeling for interpretable learner knowledge diagnosis in intelligent tutoring systems

Abstract

One of the fundamental tasks when providing personalized tutoring services to learners in online learning systems, such as intelligent tutoring systems and massive open online courses, is the learner knowledge diagnosis (LKD). LKD obtains the learner knowledge proficiency on skills by modeling their learning performance. Learners’ knowledge construction process is not static, but evolves overtime; hence, the evolution of learners’ knowledge proficiency must be dynamically traced. Moreover, considering the wide usage of online learning systems by large numbers of learners, the LKD task also needs to meet the requirements of large-scale assessment and interpretability to explain the diagnosed results. The existing models are either designed for static scenarios or find it difficult to explain the causality between learner performance and knowledge proficiency, as well as the item characteristics. To solve these issues, we propose herein a novel model, called the knowledge interaction-enhanced dynamic LKD (KIEDLKD), to develop learner performance, and hence, dynamically diagnose and trace the evolution of each learner’s knowledge proficiency during the exercising activities. We first propose a dynamic LKD framework by unifying the strength of the memory capacity of the key-value memory network to enhance the representation of the knowledge state during learner performance modeling and the interpretability of the Item Response Theory (IRT) to explain the learner performance in terms of knowledge proficiency and item characteristics (i.e., item difficulty and discrimination). In this framework, we diagnose and trace each learner’s knowledge proficiency on each knowledge concept (KC) over time and store them into an auxiliary memory using the key-value memory network. We further infer their general proficiencies and the IRT-based item characteristics using another neural network. Moreover, we propose the knowledge interaction concept among KCs and incorporate it into the LKD procedure to further exploit the long-term dependencies in the exercising sequences, thereby devising the KIEDLKD model. We also incorporate the learner-oriented cognitive item difficulty into our model, based on each learner’s exercising history, to adaptively model the item difficulty. Based on these factors, our KIEDLKD model can not only output the learners’ knowledge proficiency in a multi-granularity manner but also output the item characteristics, making it possible to interpret the learner performances in terms of their current knowledge states and item characteristics. Extensive experiments are conducted from six perspectives on five real-world datasets to test our model.The results of learner performance prediction demonstrate the superiority of our model on the LKD task. It can also automatically discover the underlying interaction between each pair of latent KCs, and the underlying concepts for each exercise. The ablation study verifies the contributions of each component in our model. Moreover, it can depict the evolution of learner knowledge proficiency in a multi-granularity manner and provide additional information for skill domain analysis, which enables the interpretability of our model.