Ability boosted knowledge tracing

Knowledge tracing (KT) has become an increasingly relevant problem in intelligent education services, which estimates and traces the degree of learner’s mastery of concepts based on students’ responses to learning resources. The existing mainstream KT models, only attribute learners’ feedback to the degree of knowledge mastery and leave the influence of mental ability factors out of consideration. Although ability is an essential component of the problem-solving process, these knowledge-centered models cause a contradiction between data fitting and rationalization of the model decision-making process, making it difficult to achieve high precision and readability simultaneously. In this paper, an innovative KT model, ability boosted knowledge tracing (ABKT)1 is proposed, which introduces the ability factor into learning feedback attribution to enable the model to analyze the learning process from two perspectives, knowledge and ability, simultaneously. Based on constructive learning theory, continuous matrix factorization (CMF) model is proposed to simulate the knowledge internalization process, following the initiative growth and stationarity principles. In addition, the linear graph latent ability (LGLA) model is proposed to construct learner and item latent ability features, from graph-structured learner interaction data. Then, the knowledge and ability dual-tracing framework is constructed to integrate the knowledge and ability modules. Experimental results on four public databases indicate that the proposed methods perform better than state-of-the-art knowledge tracing algorithms in terms of prediction accuracy in quantitative assessments, displaying some advantages in model interpretability and intelligibility.