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Aditya Upadhyayula; Neil Cohn – Cognitive Science, 2025
Theories of visual narrative comprehension have advocated for a hierarchical grammar-based comprehension mechanism, but only limited work has investigated this hierarchy. Here, we provide a computational framework inspired by computational psycholinguistics to address hierarchy in visual narratives. The predictions generated by this framework were…
Descriptors: Visual Perception, Comprehension, Vertical Organization, Story Grammar
Julia Ericson; Torkel Klingberg – npj Science of Learning, 2023
A key goal in cognitive training research is understanding whether cognitive training enhances general cognitive capacity or provides only task-specific improvements. Here, we developed a quantitative model for describing the temporal dynamics of these two processes. We analyzed data from 1300 children enrolled in an 8 week working memory training…
Descriptors: Cognitive Processes, Training, Children, Short Term Memory
Hyeon-Ah Kang; Adam Sales; Tiffany A. Whittaker – Grantee Submission, 2023
Increasing use of intelligent tutoring systems in education calls for analytic methods that can unravel students' learning behaviors. In this study, we explore a latent variable modeling approach for tracking learning flow during computer-interactive artificial tutoring. The study considers three models that give discrete profiles of a latent…
Descriptors: Intelligent Tutoring Systems, Algebra, Educational Technology, Learning Processes
Jing Chen; Bei Fang; Hao Zhang; Xia Xue – Interactive Learning Environments, 2024
High dropout rate exists universally in massive open online courses (MOOCs) due to the separation of teachers and learners in space and time. Dropout prediction using the machine learning method is an extremely important prerequisite to identify potential at-risk learners to improve learning. It has attracted much attention and there have emerged…
Descriptors: MOOCs, Potential Dropouts, Prediction, Artificial Intelligence
Lee, Morgan P.; Croteau, Ethan; Gurung, Ashish; Botelho, Anthony F.; Heffernan, Neil T. – International Educational Data Mining Society, 2023
The use of Bayesian Knowledge Tracing (BKT) models in predicting student learning and mastery, especially in mathematics, is a well-established and proven approach in learning analytics. In this work, we report on our analysis examining the generalizability of BKT models across academic years attributed to "detector rot." We compare the…
Descriptors: Bayesian Statistics, Models, Generalizability Theory, Longitudinal Studies
Shu, Tian; Luo, Guanzhong; Luo, Zhaosheng; Yu, Xiaofeng; Guo, Xiaojun; Li, Yujun – Journal of Educational and Behavioral Statistics, 2023
Cognitive diagnosis models (CDMs) are the statistical framework for cognitive diagnostic assessment in education and psychology. They generally assume that subjects' latent attributes are dichotomous--mastery or nonmastery, which seems quite deterministic. As an alternative to dichotomous attribute mastery, attention is drawn to the use of a…
Descriptors: Cognitive Measurement, Models, Diagnostic Tests, Accuracy
Longwei Zheng; Tong Liu; Yuanyuan Feng; Xiaoqing Gu; Ming-Hua Yu – SAGE Open, 2024
Understanding the teacher's technology adoption process is essential to comprehend and narrow the digital divide in the post-epidemic age. During the pandemic, the stay-at-home orders not only intervened schooling and teaching but also increased digital accessibility to teachers. This research studies teacher heterogeneity and adoption controls in…
Descriptors: COVID-19, Pandemics, Technology Integration, Technology Uses in Education
Zheng, Longwei; Liu, Tong; Islam, A. Y. M. Atiquil; Gu, Xiaoqing – Educational Technology Research and Development, 2023
This study proposed a dynamic model of organizational technology adoption within a school institute culture. We described an implementation of a nonhomogeneous hidden Markov model based on a downscaling scheme that can project the cultural factors of the institute onto a teacher's implementation behavior. To reveal the dynamics of cultural…
Descriptors: Technology Integration, School Culture, Models, Cultural Influences
Christhilf, Katerina; Newton, Natalie; Butterfuss, Reese; McCarthy, Kathryn S.; Allen, Laura K.; Magliano, Joseph P.; McNamara, Danielle S. – International Educational Data Mining Society, 2022
Prompting students to generate constructed responses as they read provides a window into the processes and strategies that they use to make sense of complex text. In this study, Markov models examined the extent to which: (1) patterns of strategies; and (2) strategy combinations could be used to inform computational models of students' text…
Descriptors: Markov Processes, Reading Strategies, Reading Comprehension, Models
Janet H. Hsiao; Jeehye An; Veronica Kit Sum Hui; Yueyuan Zheng; Antoni B. Chan – npj Science of Learning, 2022
Greater eyes-focused eye movement pattern during face recognition is associated with better performance in adults but not in children. We test the hypothesis that higher eye movement consistency across trials, instead of a greater eyes-focused pattern, predicts better performance in children since it reflects capacity in developing visual…
Descriptors: Eye Movements, Recognition (Psychology), Human Body, Visual Perception
Jang, Yoonsun; Cohen, Allan S. – Educational and Psychological Measurement, 2020
A nonconverged Markov chain can potentially lead to invalid inferences about model parameters. The purpose of this study was to assess the effect of a nonconverged Markov chain on the estimation of parameters for mixture item response theory models using a Markov chain Monte Carlo algorithm. A simulation study was conducted to investigate the…
Descriptors: Markov Processes, Item Response Theory, Accuracy, Inferences
ALSaad, Fareedah; Reichel, Thomas; Zeng, Yuchen; Alawini, Abdussalam – International Educational Data Mining Society, 2021
With the emergence of MOOCs, it becomes crucial to automate the process of a course design to accommodate the diverse learning demands of students. Modeling the relationships among educational topics is a fundamental first step for automating curriculum planning and course design. In this paper, we introduce "Topic Transition Map" (TTM),…
Descriptors: Online Courses, Student Diversity, Student Needs, Course Content
Lozano, José H.; Revuelta, Javier – Educational and Psychological Measurement, 2023
The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and…
Descriptors: Bayesian Statistics, Learning Processes, Test Items, Item Analysis
Meng, Lingling; Zhang, Mingxin; Zhang, Wanxue; Chu, Yu – Interactive Learning Environments, 2021
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…
Descriptors: Bayesian Statistics, Intelligent Tutoring Systems, Student Evaluation, Knowledge Level
Tran, Tuan M.; Hasegawa, Shinobu – International Association for Development of the Information Society, 2022
A learner model reflects learning patterns and characteristics of a learner. A learner model with learning history and its effectiveness plays a significant role in supporting a learner's understanding of their strengths and weaknesses of their way of learning in order to make proper adjustments for improvement. Nowadays, learners have been…
Descriptors: Markov Processes, Learning Processes, Models, Scores