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Bahar Radmehr; Tanja Kaser; Adish Singla – Journal of Educational Data Mining, 2025
There has been a growing interest in developing simulated learners to enhance learning and teaching experiences in educational environments. However, existing works have primarily focused on structured environments relying on meticulously crafted representations of tasks, thereby limiting the learner's ability to generalize skills across tasks. In…
Descriptors: Generalization, Reinforcement, Computer Simulation, Artificial Intelligence
Anirudhan Badrinath; Zachary Pardos – Journal of Educational Data Mining, 2025
Bayesian Knowledge Tracing (BKT) is a well-established model for formative assessment, with optimization typically using expectation maximization, conjugate gradient descent, or brute force search. However, one of the flaws of existing optimization techniques for BKT models is convergence to undesirable local minima that negatively impact…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Problem Solving, Audience Response Systems
Narjes Rohani; Behnam Rohani; Areti Manataki – Journal of Educational Data Mining, 2024
The prediction of student performance and the analysis of students' learning behaviour play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behaviour, educators can gain valuable insights into the factors that influence students' academic outcomes and identify areas of…
Descriptors: Mathematics Education, Models, Prediction, Knowledge Level
Shi Pu; Yu Yan; Brandon Zhang – Journal of Educational Data Mining, 2024
We propose a novel model, Wide & Deep Item Response Theory (Wide & Deep IRT), to predict the correctness of students' responses to questions using historical clickstream data. This model combines the strengths of conventional Item Response Theory (IRT) models and Wide & Deep Learning for Recommender Systems. By leveraging clickstream…
Descriptors: Prediction, Success, Data Analysis, Learning Analytics
Yikai Lu; Lingbo Tong; Ying Cheng – Journal of Educational Data Mining, 2024
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…
Descriptors: Models, Intelligent Tutoring Systems, Prediction, Knowledge Level
Mohammad M. Khajah – Journal of Educational Data Mining, 2024
Bayesian Knowledge Tracing (BKT) is a popular interpretable computational model in the educational mining community that can infer a student's knowledge state and predict future performance based on practice history, enabling tutoring systems to adaptively select exercises to match the student's competency level. Existing BKT implementations do…
Descriptors: Students, Bayesian Statistics, Intelligent Tutoring Systems, Cognitive Development
Boxuan Ma; Sora Fukui; Yuji Ando; Shinichi Konomi – Journal of Educational Data Mining, 2024
Language proficiency diagnosis is essential to extract fine-grained information about the linguistic knowledge states and skill mastery levels of test takers based on their performance on language tests. Different from comprehensive standardized tests, many language learning apps often revolve around word-level questions. Therefore, knowledge…
Descriptors: Language Proficiency, Brain Hemisphere Functions, Language Processing, Task Analysis
Avery H. Closser; Anthony F. Botelho; Jenny Yun-Chen Chan – Journal of Educational Data Mining, 2024
Experimental research on perception and cognition has shown that inherent and manipulated visual features of mathematics problems impact individuals' problem-solving behavior and performance. In a recent study, we manipulated the spacing between symbols in arithmetic expressions to examine its effect on 174 undergraduate students' arithmetic…
Descriptors: Undergraduate Students, Arithmetic, Symbols (Mathematics), Equations (Mathematics)
Jeffrey Matayoshi; Shamya Karumbaiah – Journal of Educational Data Mining, 2024
Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have…
Descriptors: Models, Statistical Bias, Data Analysis, Simulation
Md Akib Zabed Khan; Agoritsa Polyzou – Journal of Educational Data Mining, 2024
In higher education, academic advising is crucial to students' decision-making. Data-driven models can benefit students in making informed decisions by providing insightful recommendations for completing their degrees. To suggest courses for the upcoming semester, various course recommendation models have been proposed in the literature using…
Descriptors: Academic Advising, Courses, Data Use, Artificial Intelligence
Chuan Cai; Adam Fleischhacker – Journal of Educational Data Mining, 2024
We propose a novel approach to address the issue of college student attrition by developing a hybrid model that combines a structural neural network with a piecewise exponential model. This hybrid model not only shows the potential to robustly identify students who are at high risk of dropout, but also provides insights into which factors are most…
Descriptors: College Students, Student Attrition, Dropouts, Potential Dropouts
Yang Shi; Robin Schmucker; Keith Tran; John Bacher; Kenneth Koedinger; Thomas Price; Min Chi; Tiffany Barnes – Journal of Educational Data Mining, 2024
Understanding students' learning of knowledge components (KCs) is an important educational data mining task and enables many educational applications. However, in the domain of computing education, where program exercises require students to practice many KCs simultaneously, it is a challenge to attribute their errors to specific KCs and,…
Descriptors: Programming Languages, Undergraduate Students, Learning Processes, Teaching Models
John Stamper; Steven Moore; Carolyn P. Rosé; Philip I. Pavlik Jr.; Kenneth Koedinger – Journal of Educational Data Mining, 2024
LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates…
Descriptors: Learning Analytics, Web Sites, Data Use, Educational Technology
Kerstin Wagner; Agathe Merceron; Petra Sauer; Niels Pinkwart – Journal of Educational Data Mining, 2024
In this paper, we present an extended evaluation of a course recommender system designed to support students who struggle in the first semesters of their studies and are at risk of dropping out. The system, which was developed in earlier work using a student-centered design, is based on the explainable k-nearest neighbor algorithm and recommends a…
Descriptors: At Risk Students, Algorithms, Foreign Countries, Course Selection (Students)
Jacob Whitehill; Jennifer LoCasale-Crouch – Journal of Educational Data Mining, 2024
With the aim to provide teachers with more specific, frequent, and actionable feedback about their teaching, we explore how Large Language Models (LLMs) can be used to estimate "Instructional Support" domain scores of the CLassroom Assessment Scoring System (CLASS), a widely used observation protocol. We design a machine learning…
Descriptors: Artificial Intelligence, Teacher Evaluation, Models, Transcripts (Written Records)