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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
Wang, Yue; Eysink, Tessa H. S.; Qu, Zhili; Yang, Zhijiao; Shan, Huaming; Zhang, Nan; Zhang, Hai; Wang, Yining – Journal of Educational Computing Research, 2022
This research used a comparative quasi-experimental design to investigate the impacts of an IRS in the ILE on students' academic performance, cognitive load, and satisfaction with the lesson. A total of 31 middle school students were divided into the experimental group and the control group. Mann-Whitney U tests yielded three major results. (1)…
Descriptors: Intelligent Tutoring Systems, Active Learning, Academic Achievement, Cognitive Processes
Xuanyan Zhong; Zehui Zhan – Interactive Technology and Smart Education, 2025
Purpose: The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners' computational thinking. Design/methodology/approach: By…
Descriptors: Intelligent Tutoring Systems, Computer Science Education, Programming, Independent Study
Claudia De Barros Camargo; Antonio Hernández Fernández – Educational Process: International Journal, 2024
Background/Purpose: This study investigates the integration of neuropedagogy, neuroimaging, artificial intelligence (AI), and deep learning in educational systems. The research aims to elucidate how these technologies can be synergistically applied to optimize learning processes based on individual neurocognitive profiles, thereby enhancing…
Descriptors: Artificial Intelligence, Educational Practices, Intelligent Tutoring Systems, Neurosciences
Di Zhang; Gwo-Jen Hwang; Shih-Ting Chu – Interactive Learning Environments, 2024
When encountering difficulties in conventional educational games, learners seldom self-regulate to discover and organize the learning content in the game environment. With the development of the human-computer interaction technology, computer agents are gradually being applied to educational games to provide personalized guidance or support to…
Descriptors: Intelligent Tutoring Systems, Educational Games, Technology Uses in Education, Academic Achievement
Lamia, Mahnane; Mohamed, Hafidi – International Journal of Web-Based Learning and Teaching Technologies, 2019
Nowadays, students are becoming familiar with the computer technology at a very early age. Moreover, the wide availability of the internet gives a new perspective to distance education making e-learning environments crucial to the future of education. Intelligent tutoring systems (ITSs) provide sophisticated tutoring systems using artificial…
Descriptors: Problem Solving, Educational Technology, Technology Uses in Education, Intelligent Tutoring Systems
de Morais, Felipe; Jaques, Patricia A. – Informatics in Education, 2022
Intelligent Tutoring Systems (ITSs) for Math still use traditional data input methods: computers' keyboard and mouse. However, students usually solve math tasks using paper and pen. Therefore, the gap between the manner the students work and the requirements imposed by these typing-based systems expose students to an extraneous cognitive load,…
Descriptors: Intelligent Tutoring Systems, Mathematics Instruction, Educational Technology, Technology Uses in Education
Sun, Jerry Chih-Yuan; Yu, Shih-Jou; Chao, Chih-Hsuan – Educational Psychology, 2019
The current study developed an intelligent learning environment for online education of research ethics and investigated how encouragement and warning intelligent feedback influenced learners' engagement (behavioural, emotional, and cognitive) and cognitive load (mental load and mental effort). Participants included 191 graduate students in Taiwan…
Descriptors: Feedback (Response), Learner Engagement, Cognitive Processes, Difficulty Level
Turkmen, Gamze; Caner, Sonay – Turkish Online Journal of Distance Education, 2020
This study aims to provide a comprehensive and in-depth investigation of the debugging process in programming teaching in terms of cognitive and metacognitive aspects, based on programming students who demonstrate low, medium, and high programming performance and to propose instructional strategies for scaffolding novice learners in an effective…
Descriptors: Programming, Novices, Electronic Learning, Troubleshooting
Ibili, Emin; Billinghurst, Mark – International Journal of Assessment Tools in Education, 2019
In this study, the relationship between the usability of a mobile Augmented Reality (AR) tutorial system and cognitive load was examined. In this context, the relationship between perceived usefulness, the perceived ease of use, and the perceived natural interaction factors and intrinsic, extraneous, germane cognitive load were investigated. In…
Descriptors: Cognitive Processes, Difficulty Level, Correlation, Usability
Hayashi, Yugo; Takeuchi, Yugo – International Educational Data Mining Society, 2018
This study investigated the factors underlying the estimation of learner self-confidence during explanations with a conversational agent in an online explanation task. Based on reviews of previous studies, we focused on how factors such as the learner's task activities and personal characteristics can be predictors. To examine these points, we…
Descriptors: Self Efficacy, Task Analysis, Cognitive Processes, Individual Characteristics
Kamsa, Imane; Elouahbi, Rachid; El Khoukhi, Fatima – Journal of Information Technology Education: Research, 2017
Aim/Purpose: To identify and rectify the learning difficulties of online learners. Background: The major cause of learners' failure and non-acquisition of knowledge relates to their weaknesses in certain areas necessary for optimal learning. We focus on e-learning because, within this environment, the learner is mostly affected by these…
Descriptors: Foreign Countries, Graduate Students, Masters Programs, Learning Disabilities
Hafidi, Mohamed; Bensebaa, Tahar – International Journal of Information and Communication Technology Education, 2014
Several adaptive and intelligent tutoring systems (AITS) have been developed with different variables. These variables were the cognitive traits, cognitive styles, and learning behavior. However, these systems neglect the importance of the learner's multiple intelligences, the learner's skill level and the learner's feedback when implementing…
Descriptors: Intelligent Tutoring Systems, Models, Foreign Countries, Pretests Posttests
Stott, Angela; Hattingh, Annemarie – Educational Technology & Society, 2015
The paper presents a case study of the use of conceptual tutoring software to promote deep learning of the scientific concept of density among 50 final year pre-service student teachers in a natural sciences course in a South African university. Individually-paced electronic tutoring is potentially an effective way of meeting the students' varied…
Descriptors: Intelligent Tutoring Systems, Computer Software, Case Studies, Scientific Concepts
Chieu, Vu Minh; Luengo, Vanda; Vadcard, Lucile; Tonetti, Jerome – International Journal of Artificial Intelligence in Education, 2010
Cognitive approaches have been used for student modeling in intelligent tutoring systems (ITSs). Many of those systems have tackled fundamental subjects such as mathematics, physics, and computer programming. The change of the student's cognitive behavior over time, however, has not been considered and modeled systematically. Furthermore, the…
Descriptors: Foreign Countries, Medical Students, Surgery, Human Body
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