Exploring the Individual Differences in Multidimensional Evolution of Knowledge States of Learners

The key to the effectiveness of Intelligent Tutoring Systems (ITSs) is to fit the uncertainty of each learner's performance in performing different learning tasks. Throughout the tutoring and learning process, the uncertainty of learners' performance can reflect their varying knowledge states, which can arise from individual differences in learning characteristics and capacities. In this investigation, we proposed a multidimensional representation of the evolution of knowledge states of learners to better understand individual differences among them. This assumption about this representation is verified using the Tensor Factorization (TF) based method, a modern state-of-the-art model for knowledge tracing. The accuracy of the Tensor-based method is evaluated by comparing it to other knowledge-tracing methods, to gain a deeper insight into individual differences among learners and their learning of diverse contents. The experimental data under focus in our investigation is derived from the AutoTutor lessons that were developed for the Center for the Study of Adult Literacy (CSAL), which employs a trialogue design comprising of a virtual tutor, a virtual companion and a human learner. A broader merit of our proposed approach lies in its capability to capture individual differences more accurately, without requiring any changes in the real-world implementation of ITSs.