Personalized Learning Made Simple: A Deep Knowledge Tracing Model for Individual Cognitive Development

Knowledge tracing is the fundamental technology for constructing learner models that dynamically estimate and predict a learner’s knowledge state. While current research on knowledge tracing has improved the predictive capacity of the model by investigating the relationship between learners and problem concepts, these models become static after training. This limits their ability to adapt to the varying developmental stages of learners due to human diversity. Drawing inspiration from the synaptic plasticity that grants lifelong learning capabilities to the biological brain, this study incorporates plasticity weights into the Transformer architecture. This leads to the proposal of a deep knowledge tracing model designed to adapt to individual learner development. Moreover, it demonstrates significant performance improvements compared to the baseline model.