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Showing 106 to 120 of 453 results Save | Export
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Hanrui Gao; Yi Zhang; Gwo-Jen Hwang; Sunan Zhao; Ying Wang; Kang Wang – Education and Information Technologies, 2024
Artificial Intelligence (AI) education in primary schools has received a great deal of attention globally, and it is thus important to investigate primary school students' perceptions and understanding of AI learning. Therefore, in this study, 673 drawings of conceptions of AI learning by third to sixth grade students were collected. Firstly, a…
Descriptors: Elementary School Students, Student Attitudes, Artificial Intelligence, Freehand Drawing
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Zhi Liu; Huimin Duan; Shiqi Liu; Rui Mu; Sannyuya Liu; Zongkai Yang – Educational Technology & Society, 2024
Conversational agents (CAs) primarily adopt knowledge scaffolding (KS) or emotional scaffolding (ES) to intervene in learners' knowledge gain and emotional experience in online learning. However, the ill-defined design for KS and ES, as well as insufficient understanding of their interactive effects on learning outcomes, have hindered the…
Descriptors: Electronic Learning, Achievement Gains, Knowledge Level, Emotional Experience
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Janine Arantes – Policy Futures in Education, 2024
There has been a policy push in K-12 educational settings towards personalized learning in the last decade. Commercial platforms and learning designers have responded, offering learning tools to support teaching and learning through data-driven insights and recommendations. Trending towards the augmentation or replacing human teachers with…
Descriptors: Educational Policy, Individualized Instruction, Elementary Secondary Education, Electronic Learning
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Hongyu Xie; He Xiao; Yu Hao – International Journal of Web-Based Learning and Teaching Technologies, 2024
Modern e-learning system is a representative service form in innovative service industry. This paper designs a personalized service domain system, optimizes various parameters and can be applied to different education quality evaluation, and proposes a decision tree recommendation algorithm. Information gain is carried out through many existing…
Descriptors: Artificial Intelligence, Electronic Learning, Individualized Instruction, Models
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Schmucker, Robin; Wang, Jingbo; Hu, Shijia; Mitchell, Tom M. – Journal of Educational Data Mining, 2022
We consider the problem of assessing the changing performance levels of individual students as they go through online courses. This student performance modeling problem is a critical step for building adaptive online teaching systems. Specifically, we conduct a study of how to utilize various types and large amounts of log data from earlier…
Descriptors: Academic Achievement, Electronic Learning, Artificial Intelligence, Predictor Variables
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Hur, Paul; Lee, HaeJin; Bhat, Suma; Bosch, Nigel – International Educational Data Mining Society, 2022
Machine learning is a powerful method for predicting the outcomes of interactions with educational software, such as the grade a student is likely to receive. However, a predicted outcome alone provides little insight regarding how a student's experience should be personalized based on that outcome. In this paper, we explore a generalizable…
Descriptors: Artificial Intelligence, Individualized Instruction, College Mathematics, Statistics
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Ng, Davy Tsz Kit; Leung, Jac Ka Lok; Su, Jiahong; Ng, Ross Chi Wui; Chu, Samuel Kai Wah – Educational Technology Research and Development, 2023
The pandemic has catalyzed a significant shift to online/blended teaching and learning where teachers apply emerging technologies to enhance their students' learning outcomes. Artificial intelligence (AI) technology has gained its popularity in online learning environments during the pandemic to assist students' learning. However, many of these AI…
Descriptors: Teacher Competencies, Digital Literacy, 21st Century Skills, Artificial Intelligence
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Xu, Xiaoqiu; Dugdale, Deborah M.; Wei, Xin; Mi, Wenjuan – American Journal of Distance Education, 2023
The recent surge of online language learning services in the past decade has benefitted second language learners. However, there is a lack of understanding of whether learners, especially young learners, are engaged in online learning, and how educators can enhance the engagement of the online learning experience. This study examines an artificial…
Descriptors: Artificial Intelligence, Prediction, Electronic Learning, Learner Engagement
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Jasin, Jamil; Ng, He Tong; Atmosukarto, Indriyati; Iyer, Prasad; Osman, Faiezin; Wong, Peng Yu Kelly; Pua, Ching Yee; Cheow, Wean Sin – Education and Information Technologies, 2023
Low student engagement and motivation in online classes are well-known issues many universities face, especially with distance education during the COVID-19 pandemic. The online environment makes it even harder for teachers to connect with their students through traditional verbal and nonverbal behaviours, further decreasing engagement. Yet,…
Descriptors: Artificial Intelligence, Synchronous Communication, Electronic Learning, Chemistry
Charles, Brian – ProQuest LLC, 2023
Online executive education courses are a specialized mode of learning used by organizational leaders to develop knowledge in discrete subjects. Universities that offer these courses may lack the technology skills to develop such courses with supportive artificial intelligence (AI) technologies. Instructors of such courses serve as adjuncts who are…
Descriptors: Artificial Intelligence, Online Courses, Business Administration Education, Adjunct Faculty
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Hui-Tzu Chang; Chia-Yu Lin – IEEE Transactions on Education, 2024
Contribution: This study incorporates competition-based learning (CBL) into machine learning courses. By engaging students in innovative problem-solving challenges within information competitions, revealing that students' participation in online problem-solving competitions can improve their information technology, and showcase competitions can…
Descriptors: Competition, Artificial Intelligence, Curriculum, Problem Solving
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Zixi Li; Chaoran Wang; Curtis J. Bonk – Online Learning, 2024
As generative AI tools are increasingly popular in today's teaching and learning process, challenges and opportunities occur at the same time. Self-directed learning has been regarded as a powerful learning ability that supports learners in informal learning contexts and its importance rises in salience when incorporating AI into learning. This…
Descriptors: Artificial Intelligence, Technology Uses in Education, Independent Study, Electronic Learning
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Olga Ovtšarenko – Discover Education, 2024
Machine learning (ML) methods are among the most promising technologies with wide-ranging research opportunities, particularly in the field of education, where they can be used to enhance student learning outcomes. This study explores the potential of machine learning algorithms to build and train models using log data from the "3D…
Descriptors: Artificial Intelligence, Algorithms, Technology Uses in Education, Opportunities
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Gerti Pishtari; María Jesús Rodríguez-Triana; Luis P. Prieto; Adolfo Ruiz-Calleja; Terje Väljataga – Journal of Computer Assisted Learning, 2024
Background: In the field of Learning Design, it is common that researchers analyse manually design artefacts created by practitioners, using pedagogically-grounded approaches (e.g., Bloom's Taxonomy), both to understand and later to support practitioners' design practices. Automatizing these high-level pedagogically-grounded analyses would enable…
Descriptors: Electronic Learning, Instructional Design, Active Learning, Inquiry
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S. Sageengrana; S. Selvakumar; S. Srinivasan – Interactive Learning Environments, 2024
Students are termed "multitaskers," and it is likely that they easily fall prey to other subjects or topics that most interest them. They occasionally took heed or gave close and thoughtful attention to the lectures they were on. In the current educational system, our young generations receive materials from their leftovers, and their…
Descriptors: Electronic Learning, Dropouts, Student Behavior, Student Interests
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