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Noah L. Schroeder; Robert O. Davis; Eunbyul Yang – Journal of Educational Computing Research, 2025
Pedagogical agents are virtual characters that instructional designers include in learning environments to help students learn. Research in the area has flourished for thirty years, yet there are still critical questions about the efficacy of pedagogical agents for influencing learning and affect. As such, we conducted an umbrella review to…
Descriptors: Educational Technology, Technology Uses in Education, Artificial Intelligence, Intelligent Tutoring Systems
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Paul Kim; Wilson Wang; Curtis J. Bonk – Journal of Educational Computing Research, 2025
Following the launch of the generative AI Web application, Ask.SMILE, designed to evaluate the cognitive levels of questions asked, 2559 educators generated 25,973 question-feedback sets over a three-month period, with an average of over 10 questions per participant. Analyses revealed a significant improvement in question quality from initial…
Descriptors: Artificial Intelligence, Technology Uses in Education, Test Wiseness, Test Items
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Xiaodong Wei; Lei Wang; Lap-Kei Lee; Ruixue Liu – Journal of Educational Computing Research, 2025
Notwithstanding the growing advantages of incorporating Augmented Reality (AR) in science education, the pedagogical use of AR combined with Pedagogical Agents (PAs) remains underexplored. Additionally, few studies have examined the integration of Generative Artificial Intelligence (GAI) into science education to create GAI-enhanced PAs (GPAs)…
Descriptors: Artificial Intelligence, Technology Uses in Education, Models, Science Education
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Zhai, Xuesong; Xu, Jiaqi; Chen, Nian-Shing; Shen, Jun; Li, Yan; Wang, Yonggu; Chu, Xiaoyan; Zhu, Yumeng – Journal of Educational Computing Research, 2023
Affective computing (AC) has been regarded as a relevant approach to identifying online learners' mental states and predicting their learning performance. Previous research mainly used one single-source data set, typically learners' facial expression, to compute learners' affection. However, a single facial expression may represent different…
Descriptors: Affective Behavior, Nonverbal Communication, Video Technology, Online Courses
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Shan Li; Zuer Liu; Mengling Qiu; Jiaxin Huang; Juan Zheng; Guozhu Ding – Journal of Educational Computing Research, 2024
Educational robots represent a unique form of teacher presence. Exploring how the communication features of robot instructors affect student learning experience could contribute to the advancement of educational robots. This study examined the impact of speech rate, voice type, and emotional tone of robots on students' cognitive load, attitudes…
Descriptors: Educational Technology, Technology Uses in Education, Cognitive Processes, Difficulty Level
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Semerci, Yusuf Can; Goularas, Dionysis – Journal of Educational Computing Research, 2021
Estimating the flow state of students in a course allows evaluating their sentimental state and the challenges they are facing. In e-learning platforms, the evaluation of flow state is a complex task because it depends on the ability to extract the parameters that better reflect the activity and effort of students. In this scope, the current study…
Descriptors: Educational Technology, Electronic Learning, Interaction, Online Courses
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Yin, Jiaqi; Goh, Tiong-Thye; Yang, Bing; Xiaobin, Yang – Journal of Educational Computing Research, 2021
This study investigated the impact of a chatbot-based micro-learning system on students' learning motivation and performance. A quasi-experiment was conducted with 99 first-year students taking part in a basic computer course on number system conversion. The students were assigned to a traditional learning group or a chatbot-based micro-learning…
Descriptors: Educational Technology, Technology Uses in Education, Student Motivation, Academic Achievement
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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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Guo, Yan Ru; Goh, Dion Hoe-Lian – Journal of Educational Computing Research, 2015
Over the past decade, computer games and other interactive technologies have shown great potential when used in innovative ways to enhance learning. It is now known that learning is associated not only with cognitive ability but also with affect. The incorporation of affective embodied pedagogical agents (EPAs) in computer programs for learning…
Descriptors: Meta Analysis, Affective Behavior, Educational Technology, Instructional Effectiveness
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Schroeder, Noah L.; Adesope, Olusola O.; Gilbert, Rachel Barouch – Journal of Educational Computing Research, 2013
Research on the use of software programs and tools such as pedagogical agents has peaked over the last decade. Pedagogical agents are on-screen characters that facilitate instruction. This meta-analysis examined the effect of using pedagogical agents on learning by reviewing 43 studies involving 3,088 participants. Analysis of the results…
Descriptors: Meta Analysis, Cybernetics, Artificial Intelligence, Technology Uses in Education
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Knauf, Rainer; Sakurai, Yoshitaka; Tsuruta, Setsuo; Jantke, Klaus P. – Journal of Educational Computing Research, 2010
University education often suffers from a lack of an explicit and adaptable didactic design. Students complain about the insufficient adaptability to the learners' needs. Learning content and services need to reach their audience according to their different prerequisites, needs, and different learning styles and conditions. A way to overcome such…
Descriptors: Prerequisites, College Instruction, Educational Experiments, Cognitive Style