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…

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…

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…

Jim Greer and his colleagues argued that student modelling is essential to provide adaptive instruction in tutoring systems and showed that effective modelling is possible, despite being enormously challenging. Student modelling plays a prominent role in many intelligent tutoring systems (ITSs) that address problem-solving domains. However,…

Face-to-face human tutoring in classroom environments amply facilitates human tutor-learner interactions wherein the tutor gets opportunity to exercise his cognitive intelligence to understand learner's pre-knowledge level, learning pattern, specific learning difficulties, and be able to offer course content well-aligned to the learner's…

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…

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.…

Research in Educational Data Mining could benefit from greater efforts to ensure that models yield reliable, valid, and interpretable parameter estimates. These efforts have especially been lacking for individualized student-parameter models. We collected two datasets from a sizable student population with excellent "depth" -- that is,…

In this study, we are interested to see the impact of self-discipline on students' knowledge and learning. Self-discipline can influence both learning rate as well as knowledge accumulation over time. We used a Knowledge Tracing (KT) model to make inferences about students' knowledge and learning. Based on a widely used questionnaire, we measured…

The 12th iteration of the International Conference on Educational Data Mining (EDM 2019) is organized under the auspices of the International Educational Data Mining Society in Montreal, Canada. The theme of this year's conference is EDM in Open-Ended Domains. As EDM has matured it has increasingly been applied to open-ended and ill-defined tasks…

The Second International Conference on Educational Data Mining (EDM2009) was held at the University of Cordoba, Spain, on July 1-3, 2009. EDM brings together researchers from computer science, education, psychology, psychometrics, and statistics to analyze large data sets to answer educational research questions. The increase in instrumented…