Fuzzy Bayesian Knowledge Tracing

A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges

“…The probabilistic approaches have advantages in tracking students' cognition so that they can establish strong interpretation skills, but they have limitations in terms of accuracy [2]. While the deep learning approaches have advantages in predicting future student performance through a hidden layer that can retain relevant information from the learning logs entered, there are still challenges related to interpretability [18].…”

“…Liu et al [2] proposed Fuzzy Bayesian Knowledge Tracing (FBKT) and Type-2 fuzzy Bayesian Knowledge Tracing (T2FBKT). The experiments were conducted with 5-fold cross-validation for more stable results.…”

“…On the probabilistic approach, the BKT models have lower accuracy than the DKT models. Several ways can be done to improve accuracy based on prior studies include applying a deep learning approach to improve interpretation ability [2], exploration of the probability matrix to show the relationship between different abilities or knowledge [6], exploring the possibility of learning skill hierarchy from data [13]. Moreover, the implementation of the proposed model in real-world applications can also be added.…”

“…In order to address this issue, researchers have paid more attention to knowledge tracing on student modeling. Knowledge tracing is aimed to model students' knowledge state over time based on their previous performance in a series of exercises [1], [2]. The mastery level of skills in the learning process is represented by the knowledge state [1].…”

“…The mastery level of skills in the learning process is represented by the knowledge state [1]. Estimation of students' knowledge state can be used to evaluate the learning effectiveness of students, predict students' future performance and support the personalization of adaptive learning content [2], [3].…”