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Showing 1 to 15 of 27 results Save | Export
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Jelle Wemmenhove; Dorina Bór; Rianne Conijn; Jim Portegies – Journal of Computer Assisted Learning, 2025
Background: Recently human-centred design plays an increasing role in learning analytics, however this approach is mostly lacking in the design and evaluation of Intelligent Tutoring Systems (ITSs). A potential way to incorporate human-centred design principles in ITS development is by adopting a service design approach. Objectives: This article…
Descriptors: Intelligent Tutoring Systems, Program Evaluation, Design, Stakeholders
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Markus Wolfgang Hermann Spitzer; Miguel Ruiz-Garcia; Korbinian Moeller – British Journal of Educational Technology, 2025
Research on fostering learning about percentages within intelligent tutoring systems (ITSs) is limited. Additionally, there is a lack of data-driven approaches for improving the design of ITS to facilitate learning about percentages. To address these gaps, we first investigated whether students' understanding of basic mathematical skills (eg,…
Descriptors: Mathematics Skills, Fractions, Prediction, Mathematical Concepts
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Yu Lu; Deliang Wang; Penghe Chen; Zhi Zhang – IEEE Transactions on Learning Technologies, 2024
Amid the rapid evolution of artificial intelligence (AI), the intricate model structures and opaque decision-making processes of AI-based systems have raised the trustworthy issues in education. We, therefore, first propose a novel three-layer knowledge tracing model designed to address trustworthiness for an intelligent tutoring system. Each…
Descriptors: Models, Intelligent Tutoring Systems, Artificial Intelligence, Technology Uses in Education
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Soonri Choi; Soomin Kang; Kyungmin Lee; Hongjoo Ju; Jihoon Song – Contemporary Educational Technology, 2024
This study proposes that the gestures of an agent tutor in a multimedia learning environment can generate positive and negative emotions in learners and influence their cognitive processes. To achieve this, we developed and integrated positive and negative agent tutor gestures in a multimedia learning environment directed by cognitive gestures.…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Cognitive Processes, Difficulty Level
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Yildirim-Erbasli, Seyma N.; Bulut, Okan; Demmans Epp, Carrie; Cui, Ying – Journal of Educational Technology Systems, 2023
Conversational agents have been widely used in education to support student learning. There have been recent attempts to design and use conversational agents to conduct assessments (i.e., conversation-based assessments: CBA). In this study, we developed CBA with constructed and selected-response tests using Rasa--an artificial intelligence-based…
Descriptors: Artificial Intelligence, Intelligent Tutoring Systems, Computer Mediated Communication, Formative Evaluation
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Pule, Sarah; Attard, Jean-Paul – Design and Technology Education, 2021
While there is much research concerning the interpretation of diagrams such as geographical maps and networks for information systems, there is very little on the diagrams involved in electrical and electronic engineering. Such research is important not only because it supports arguments made for other types of diagrams but also because it informs…
Descriptors: Spatial Ability, Cognitive Processes, Electronic Equipment, Engineering Education
Ethan Prihar; Manaal Syed; Korinn Ostrow; Stacy Shaw; Adam Sales; Neil Heffernan – Grantee Submission, 2022
As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user…
Descriptors: Educational Trends, Electronic Learning, Educational Experience, Educational Experiments
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Ethan Prihar; Manaal Syed; Korinn Ostrow; Stacy Shaw; Adam Sales; Neil Heffernan – International Educational Data Mining Society, 2022
As online learning platforms become more ubiquitous throughout various curricula, there is a growing need to evaluate the effectiveness of these platforms and the different methods used to structure online education and tutoring. Towards this endeavor, some platforms have performed randomized controlled experiments to compare different user…
Descriptors: Educational Trends, Electronic Learning, Educational Experience, Educational Experiments
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Nikola M. Luburic; Luka Z. Doric; Jelena J. Slivka; Dragan Lj. Vidakovic; Katarina-Glorija G. Grujic; Aleksandar D. Kovacevic; Simona B. Prokic – IEEE Transactions on Learning Technologies, 2025
Software engineers are tasked with writing functionally correct code of high quality. Maintainability is a crucial code quality attribute that determines the ease of analyzing, modifying, reusing, and testing a software component. This quality attribute significantly affects the software's lifetime cost, contributing to developer productivity and…
Descriptors: Intelligent Tutoring Systems, Coding, Computer Software, Technical Occupations
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Cindy Peng; Conrad Borchers; Vincent Aleven – Grantee Submission, 2024
Prior studies identified effective, but mainly non-digital, homework aids. This research involved 18 middle school students in a lo-fi prototyping study to integrate traditional homework support tools with intelligent tutoring systems (ITS), leveraging rich log data for personalized learning. Feature investigations in standardized diaries, goal…
Descriptors: Middle School Students, Intelligent Tutoring Systems, Homework, Design
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Tawfik, Andrew A.; Gatewood, Jessica; Gish-Lieberman, Jaclyn J.; Hampton, Andrew J. – Technology, Knowledge and Learning, 2022
Various theories and models have implicitly discussed the role of interaction when using learning technologies. Indeed, interaction is described as being important as it relates to technology adoption, cognitive load, and usability. While each of these perspectives describe elements of interaction, they fail to comprehensively detail how educators…
Descriptors: Definitions, Learning Experience, Interaction, Usability
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Maniktala, Mehak; Cody, Christa; Barnes, Tiffany; Chi, Min – International Journal of Artificial Intelligence in Education, 2020
Within intelligent tutoring systems, considerable research has investigated hints, including how to generate data-driven hints, what hint content to present, and when to provide hints for optimal learning outcomes. However, less attention has been paid to "how" hints are presented. In this paper, we propose a new hint delivery mechanism…
Descriptors: Intelligent Tutoring Systems, Cues, Computer Interfaces, Design
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Erümit, Ali Kürsat; Çetin, Ismail – Education and Information Technologies, 2020
The aim of this study is to examine adaptation elements and Intelligent Tutoring System (ITS) elements used in Adaptive Intelligent Tutoring Systems (AITSs), using meta-synthesis methods to analyze the results of previous research. Toward this end, articles appearing in the Web of Science, Google Scholar, Eric and Science Direct databases in 2000…
Descriptors: Intelligent Tutoring Systems, Educational Technology, Design, Information Technology
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Kuhail, Mohammad Amin; Alturki, Nazik; Alramlawi, Salwa; Alhejori, Kholood – Education and Information Technologies, 2023
Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners' behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents…
Descriptors: Educational Technology, Computer Mediated Communication, Artificial Intelligence, Technology Uses in Education
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Holmes, Mike; Latham, Annabel; Crockett, Keeley; O'Shea, James D. – IEEE Transactions on Learning Technologies, 2018
Comprehension is an important cognitive state for learning. Human tutors recognize comprehension and non-comprehension states by interpreting learner non-verbal behavior (NVB). Experienced tutors adapt pedagogy, materials, and instruction to provide additional learning scaffold in the context of perceived learner comprehension. Near real-time…
Descriptors: Comprehension, Classification, Artificial Intelligence, Networks
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