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Showing 1 to 15 of 39 results Save | Export
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
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Aaron Haim; Eamon Worden; Neil T. Heffernan – Grantee Submission, 2024
Since GPT-4's release it has shown novel abilities in a variety of domains. This paper explores the use of LLM-generated explanations as on-demand assistance for problems within the ASSISTments platform. In particular, we are studying whether GPT-generated explanations are better than nothing on problems that have no supports and whether…
Descriptors: Artificial Intelligence, Learning Management Systems, Computer Software, Intelligent Tutoring Systems
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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
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Jiyou Jia; Tianrui Wang; Yuyue Zhang; Guangdi Wang – Asia Pacific Journal of Education, 2024
In designing an intelligent tutoring system, a core area of the application of AI in education, tips from the system or virtual tutors are crucial in helping students solve difficult questions in disciplines like mathematics. Traditionally, the manual design of general tips by teachers is time-consuming and error-prone. Generative AI, like…
Descriptors: Problem Solving, Artificial Intelligence, Learning Processes, Prompting
Mao, Ye – ProQuest LLC, 2021
Intelligent Tutoring Systems (ITSs) have emerged as valuable systems to promote active learning. It is critical to build accurate student models to support the learning process. In order to provide efficient and effective personalized instructions for students, tracking a student's time-varying knowledge state is essential to an ITS. Prior…
Descriptors: Time Perspective, STEM Education, Intelligent Tutoring Systems, Learning Processes
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Taub, Michelle; Azevedo, Roger – International Journal of Artificial Intelligence in Education, 2019
The goal of this study was to use eye-tracking and log-file data to investigate the impact of prior knowledge on college students' (N = 194, with a subset of n = 30 for eye tracking and sequence mining analyses) fixations on (i.e., looking at) self-regulated learning-related areas of interest (i.e., specific locations on the interface) and on the…
Descriptors: Prior Learning, Eye Movements, Metacognition, Learning Processes
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Trifa, Amal; Hedhili, Aroua; Chaari, Wided Lejouad – Education and Information Technologies, 2019
E-learning systems have gained nowadays a large student community due to the facility of use and the integration of one-to-one service. Indeed, the personalization of the learning process for every user is needed to increase the student satisfaction and learning efficiency. Nevertheless, the number of students who give up their learning process…
Descriptors: Educational Technology, Technology Uses in Education, Learning Processes, Student Needs
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Borracci, Giuliana; Gauthier, Erica; Jennings, Jay; Sale, Kyle; Muldner, Kasia – Journal of Educational Computing Research, 2020
We investigated the impact of assistance on learning and affect during problem-solving activities with a computer tutor we built using the Cognitive Tutor Authoring Tools framework. The tutor delivered its primary form of assistance in the form of worked-out examples. We manipulated the level of assistance the examples in the tutor provided, by…
Descriptors: Intelligent Tutoring Systems, Mathematics Instruction, Mathematics Education, Algebra
Suijing Yang; Daniel Taylor-Griffiths; Fabienne van der Kleij; Pauline Taylor-Guy; Ralph Saubern – Australian Council for Educational Research, 2025
Many existing reviews of educational technologies focus on the affordances of specific types of technology rather than how different technologies can be designed and used to achieve specific teaching and learning objectives. Furthermore, there appears to be a widely held assumption that the use of educational technology will result in improved…
Descriptors: Teacher Empowerment, Educational Technology, Technology Uses in Education, Technology Integration
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Chen-Chung Liu; Wan-Jun Chen; Fang-ying Lo; Chia-Hui Chang; Hung-Ming Lin – Journal of Educational Computing Research, 2024
Reading requires appropriate strategies to spark initial interest and sustain engagement. One promising strategy is the pedagogical approach of learning-by-teaching, transforming learners into active participants. Integrating this approach into digitalized and individualized reading contexts has the potential to foster the development of young…
Descriptors: Reading Interests, Active Learning, Intelligent Tutoring Systems, Artificial Intelligence
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Yuan, Chia-Ching; Li, Cheng-Hsuan; Peng, Chin-Cheng – Interactive Learning Environments, 2023
Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization…
Descriptors: Artificial Intelligence, Man Machine Systems, Technology Uses in Education, Educational Technology
Steven Moore; John Stamper; Norman Bier; Mary Jean Blink – Grantee Submission, 2020
In this paper we show how we can utilize human-guided machine learning techniques coupled with a learning science practitioner interface (DataShop) to identify potential improvements to existing educational technology. Specifically, we provide an interface for the classification of underlying Knowledge Components (KCs) to better model student…
Descriptors: Learning Analytics, Educational Improvement, Classification, Learning Processes
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Kim, Yanghee; Baylor, Amy L. – International Journal of Artificial Intelligence in Education, 2016
In this paper we review the contribution of our original work titled "Simulating Instructional Roles Through Pedagogical Agents" published in the "International Journal of Artificial Intelligence and Education" (Baylor and Kim in "Computers and Human Behavior," 25(2), 450-457, 2005). Our original work operationalized…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Computer Interfaces, Instructional Design
Bull, Susan – Research and Practice in Technology Enhanced Learning, 2016
Today's technology-enabled learning environments are becoming quite different from those of a few years ago, with the increased processing power as well as a wider range of educational tools. This situation produces more data, which can be fed back into the learning process. Open learner models have already been investigated as tools to promote…
Descriptors: Educational Technology, Electronic Learning, Models, Computer Assisted Instruction
Kenneth Holstein; Bruce M. McLaren; Vincent Aleven – Grantee Submission, 2017
Classroom experiments that evaluate the effectiveness of educational technologies do not typically examine the effects of classroom contextual variables (e.g., out-of-software help-giving and external distractions). Yet these variables may influence students' instructional outcomes. In this paper, we introduce the Spatial Classroom Log Explorer…
Descriptors: Learning Processes, Visual Learning, Visualization, Computer Software
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