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Hao-Chiang Koong Lin; Chun-Hsiung Tseng; Nian-Shing Chen – Educational Technology & Society, 2025
In recent years, learning programming has been a challenge for both learners and educators. How to enhance student engagement and learning outcomes has been a significant concern for researchers. This study examines the effects of AI-based pedagogical agents on students' learning experiences in programming courses, focusing on web game development…
Descriptors: Programming, Learner Engagement, Self Efficacy, Artificial Intelligence
Chun-Hsiung Tseng; Hao-Chiang Koong Lin; Andrew Chih-Wei Huang; Jia-Rou Lin – Cogent Education, 2023
This study explores the use of machine learning and physiological signals to enhance learning performance based on students' personality traits. Traditional personality assessment methods often yield unreliable responses, prompting the need for a novel approach utilizing objective data collection through physiological signals. Participants from a…
Descriptors: Artificial Intelligence, Personality Traits, Foreign Countries, Engineering Education
Jui-Hung Chang; Chi-Jane Wang; Hua-Xu Zhong; Hsiu-Chen Weng; Yu-Kai Zhou; Hoe-Yuan Ong; Chin-Feng Lai – Educational Technology Research and Development, 2024
Amidst the rapid advancement in the application of artificial intelligence learning, questions regarding the evaluation of students' learning status and how students without relevant learning foundation on this subject can be trained to familiarize themselves in the field of artificial intelligence are important research topics. This study…
Descriptors: Artificial Intelligence, Technological Advancement, Student Evaluation, Models
Anna Y. Q. Huang; Jei Wei Chang; Albert C. M. Yang; Hiroaki Ogata; Shun Ting Li; Ruo Xuan Yen; Stephen J. H. Yang – Educational Technology & Society, 2023
To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from…
Descriptors: Academic Achievement, Tutoring, Artificial Intelligence, Individualized Instruction
Tsai, Chih-Cheng; Cheng, Yuh-Min; Tsai, Yu-Shan; Lou, Shi-Jer – Education Sciences, 2021
In this study, experimental teaching was conducted through the artificial intelligence of things (AIOT) practical course, and the 4D (discover, define, develop, deliver) double diamond shape was used to design the course and plan the teaching content to observe the students' self-efficacy and learning anxiety. The technology acceptance model (TAM)…
Descriptors: High School Students, Student Satisfaction, Value Judgment, Usability
Lin, Hao-Chiang Koong; Wang, Cheng-Hung; Chao, Ching-Ju; Chien, Ming-Kuan – Turkish Online Journal of Educational Technology - TOJET, 2012
Emotional expression in Artificial Intelligence has gained lots of attention in recent years, people applied its affective computing not only in enhancing and realizing the interaction between computers and human, it also makes computer more humane. In this study, emotional expressions were applied into intelligent tutoring system, where learners'…
Descriptors: Foreign Countries, Intelligent Tutoring Systems, Artificial Intelligence, Focus Groups
Chang, Wen-Chih; Yang, Hsuan-Che; Shih, Timothy K.; Chao, Louis R. – International Journal of Distance Education Technologies, 2009
E-learning provides a convenient and efficient way for learning. Formative assessment not only guides student in instruction and learning, diagnose skill or knowledge gaps, but also measures progress and evaluation. An efficient and convenient e-learning formative assessment system is the key character for e-learning. However, most e-learning…
Descriptors: Electronic Learning, Student Evaluation, Formative Evaluation, Educational Objectives