Knowledge tracing is a well-known problem in AI for education, consisting of monitoring how the knowledge state of students changes during the learning process and accurately predicting their performance in future exercises. In recent years, many advances have been made thanks to various machine learning and deep learning techniques. Despite their…

Transfer motivation is an important factor influencing transfer of training. However, earlier research often did not investigate transfer motivation as a multidimensional construct. The unified model of task-specific motivation (UMTM) takes into account that (transfer) motivation is multidimensional by including both affective and cognitive…

For offering adaptive learning to learners in intelligent tutoring systems, one of the fundamental tasks is knowledge tracing (KT), which aims to assess learners' learning states and make prediction for future performance. However, there are two crucial issues in deep learning-based KT models. First, the knowledge concepts are used to predict…

We propose a cognitive-psychological model of linguistic intuitions about copredication statements. In copredication statements, like "The book is heavy and informative," the nominal denotes two ontologically distinct entities at the same time. This has been considered a problem for standard truth-conditional semantics. In this paper, we…

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…

Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners' performance in…

We investigated the effects of a learning analytics-driven prediction modeling platform and a brief digital self-regulated learning skill training program targeted to support undergraduate biology students identified as likely to perform poorly in the course. A prediction model comprising prior knowledge scores and learning management system log…

Recent years have seen a growing interest in block-based programming environments for computer science education. While these environments hold significant potential for novice programmers, they lack the adaptive support necessary to accommodate students exhibiting a wide range of initial capabilities and dispositions toward computing. A promising…

Intelligent behavior of adaptive educational systems is based on student models. Most research in student modeling focuses on student learning (acquisition of skills). We focus on prior knowledge, which gets much less attention in modeling and yet can be highly varied and have important consequences for the use of educational systems. We describe…

Executive functions enable flexible thinking, something young children are notoriously bad at. For instance, in the dimensional change card sort (DCCS) task, 3-year-olds can sort cards by one dimension (shape), but continue to sort by this dimension when asked to switch (to color). This study tests a prediction of a dynamic neural field model that…

Bayesian Knowledge Tracing (BKT) is a commonly used approach for student modeling, and Long Short Term Memory (LSTM) is a versatile model that can be applied to a wide range of tasks, such as language translation. In this work, we directly compared three models: BKT, its variant Intervention-BKT (IBKT), and LSTM, on two types of student modeling…

In this paper, we propose a computational approach to model the Zone of Proximal Development (ZPD) using predicted probabilities of correctness and engaging students in reflective dialogue. To that end, we employ a predictive model that uses a linear function of a variety of parameters, including difficulty and student knowledge and we analyze the…

In this paper, we propose a computational approach to modeling the Zone of Proximal Development of students who learn using a natural language tutoring system for physics. We employ a student model that predicts students' performance based on their prior knowledge and their activity when using a dialogue tutor to practice with conceptual,…

While Educational Data Mining research has traditionally emphasized the practical aspects of learner modeling, such as predictive modeling, estimating students knowledge, and informing adaptive instruction, in the current study, we argue that Educational Data Mining can also be used to test and improve our fundamental theories of human learning.…

Some individual difference factors are more strongly correlated with performance on postreading questions when the text is not available than when it is. The present study explores if similar interactions occur with bridging skill, which refers to a reader's propensity to establish connections between explicit text during reading. Undergraduates…