Knowledge tracing aims to model and predict students' knowledge states during learning activities. Traditional methods like Bayesian Knowledge Tracing (BKT) and logistic regression have limitations in granularity and performance, while deep knowledge tracing (DKT) models often suffer from lacking transparency. This paper proposes a…

Amid the rapid evolution of artificial intelligence (AI), the intricate model structures and opaque decision-making processes of AI-based systems have raised the trustworthy issues in education. We, therefore, first propose a novel three-layer knowledge tracing model designed to address trustworthiness for an intelligent tutoring system. Each…

A text recommendation system helps language learners find suitable reading materials. Similar to graded readers, most systems assign difficulty levels or school grades to the documents in their database, and then identify the documents that best match the language proficiency of the learner. This graded approach has two main limitations. First,…

Knowledge tracing (KT) aims to predict students' future performances based on their former exercises and additional information in educational settings. KT has received significant attention since it facilitates personalized experiences in educational situations. Simultaneously, the autoregressive (AR) modeling on the sequence of former exercises…

A longstanding goal of learner modeling and educational data mining is to improve the domain model of knowledge that is used to make inferences about learning and performance. In this report we present a tool for finding domain models that is built into an existing modeling framework, logistic knowledge tracing (LKT). LKT allows the flexible…

In this study, we developed a classification system of programming errors based on the historical data of 680,540 programming records collected on the Online Judge platform. The classification system described six types of programming errors (i.e., syntax, logical, type, writing, misunderstanding, and runtime errors) and their connections with…

Knowledge tracing (KT), the task of tracking the knowledge state of a student over time, has been assessed actively by artificial intelligence researchers. Recent reports have described that Deep-IRT, which combines item response theory (IRT) with a deep learning method, provides superior performance. It can express the abilities of each student…

To provide personalized support on educational platforms, it is crucial to model the evolution of students' knowledge states. Knowledge tracing is one of the most popular technologies for this purpose, and deep learning-based methods have achieved state-of-the-art performance. Compared to classical models, such as Bayesian knowledge tracing, which…

Bayesian knowledge tracing model (BKT) is a typical student knowledge assessment method. It is widely used in intelligent tutoring systems. In the standard BKT model, all knowledge and skills are independent of each other. However, in the process of student learning, they have a very close relation. A student may understand knowledge B better when…

Knowledge tracing is the task of modeling each student's mastery of knowledge concepts (KCs) as (s)he engages with a sequence of learning activities. Each student's knowledge is modeled by estimating the performance of the student on the learning activities. It is an important research area for providing a personalized learning platform to…

Instruction in many STEM domains heavily relies on visual representations, such as graphs, figures, and diagrams. However, students who lack representational competencies do not benefit from these visual representations. Therefore, students must learn not only content knowledge but also representational competencies. Further, as learning…

In intelligent e-learning systems that adapt a learning and teaching process to student knowledge, it is important to adapt the system as quickly as possible. However, adaptation is not possible until the student model is initialized. In this paper, a new approach to student model initialization using domain knowledge representative subset is…

Knowledge tracing algorithms are embedded in Intelligent Tutoring Systems (ITS) to keep track of students' learning process. While knowledge tracing models have been extensively studied in offline settings, very little work has explored their use in online settings. This is primarily because conducting experiments to evaluate and select knowledge…

"Wheel spinning" is the phenomenon in which a student fails to master a Knowledge Component (KC), despite significant practice. Ideally, an intelligent tutoring system would detect this phenomenon early, so that the system or a teacher could try alternative instructional strategies. Prior work has put forward several criteria for wheel…

The use of learner modelling approaches is critical for providing adaptive support in educational computer games, with predictive learner modelling being among the key approaches. While adaptive supports have been shown to improve the effectiveness of educational games, improperly customized support can have negative effects on learning outcomes.…