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Zheng, Lanqin; Niu, Jiayu; Zhong, Lu; Gyasi, Juliana Fosua – Interactive Learning Environments, 2023
Recently, artificial intelligence (AI) technologies have been widely used in the field of education, and artificial intelligence in education (AIEd) has gained increasing attention. However, no quantitative meta-analysis has been conducted on the overall effectiveness of AI on learning achievement and learning perception. To close this research…
Descriptors: Instructional Effectiveness, Artificial Intelligence, Academic Achievement, Student Attitudes
Guiqin Liang; Chunsong Jiang; Qiuzhe Ping; Xinyi Jiang – Interactive Learning Environments, 2024
With long-term impact of COVID-19 on education, online interactive live courses have been an effective method to keep learning and teaching from being interrupted, attracting more and more attention due to their synchronous and real-time interaction. However, there is no suitable method for predicting academic performance for students…
Descriptors: Academic Achievement, Prediction, Engineering Education, Online Courses
Zhibin Xu; Qiang Xu – Interactive Learning Environments, 2024
The purpose of this study is to compare academic results and psychological factors of influence in the context of the use of deep learning technologies. The experiment involved 238 respondents who were divided into two groups -- control and experimental. Students were tested for academic self-efficacy and well-being after taking the exam…
Descriptors: Foreign Countries, College Students, Music Education, Psychological Characteristics
Umer, Rahila; Susnjak, Teo; Mathrani, Anuradha; Suriadi, Lim – Interactive Learning Environments, 2023
Predictive models on students' academic performance can be built by using historical data for modelling students' learning behaviour. Such models can be employed in educational settings to determine how new students will perform and in predicting whether these students should be classed as at-risk of failing a course. Stakeholders can use…
Descriptors: Prediction, Student Behavior, Models, Academic Achievement
Tayebeh Sargazi Moghadam; Ali Darejeh; Mansoureh Delaramifar; Sara Mashayekh – Interactive Learning Environments, 2024
Learners' emotional states might change during the learning process, and unpredictable variations of a person's emotions raise the demand for regular assessment of feelings during learning. In this paper, an AI-based decision framework is proposed and implemented for e-learning systems that identify suitable micro-brake activities based on the…
Descriptors: Artificial Intelligence, Decision Making, Electronic Learning, Psychological Patterns
Çelikbilek, Yakup; Adigüzel Tüylü, Ayse Nur – Interactive Learning Environments, 2022
Institutions and universities have started using e-learning systems to reach the potential students from all over the world by decreasing costs of investments. The speed of technological developments increases the importance of e-learning systems and their technology-based components. E-learning systems also decrease the costs of both institutions…
Descriptors: Electronic Learning, Technology Uses in Education, Distance Education, Artificial Intelligence
Passig, David; Zoref, Lior – Interactive Learning Environments, 2022
Background: Proceeding with life-long learning decisions is a complex task for most adults pursuing professional development. Objective: This study investigated the use of collective intelligence as a new approach to examine one's professional development options. Methods: A systematic deliberation procedure within Facebook where people ask their…
Descriptors: Social Networks, Cooperative Learning, Intelligence, Decision Making
Merino-Armero, José Miguel; González-Calero, José Antonio; Cózar-Gutiérrez, Ramón – Interactive Learning Environments, 2023
In modern society technology is widely used and, with the digitization of many services, this is an upward trend. Therefore, computational thinking (CT) is an increasingly important concept; an aspect that is being reflected on educational policies and the extracurricular offer of different countries. This study aims to look at the efficacy of…
Descriptors: Foreign Countries, Extracurricular Activities, Robotics, Elementary School Students
Yu-Min Wang; Chung-Lun Wei; Hsin-Hui Lin; Sheng-Ching Wang; Yi-Shun Wang – Interactive Learning Environments, 2024
As artificial intelligence (AI) technology rapidly develops and is deployed, students increasingly need to understand and learn AI-related skills for future employment. This study investigates how students' AI learning anxiety and AI job replacement anxiety affect intrinsic/extrinsic learning motivations and subsequent AI learning intention. The…
Descriptors: Learning Processes, Artificial Intelligence, Anxiety, Employment Opportunities
Jiahong Su; Kai Guo; Xinyu Chen; Samuel Kai Wah Chu – Interactive Learning Environments, 2024
The teaching of artificial intelligence (AI) has increasingly become a topic of investigation among educational researchers. Studies of AI education have predominantly focused on the university level; less attention has been paid to teaching AI in K-12 classrooms. This study synthesised empirical studies on K-12 AI education, with the aims of…
Descriptors: Artificial Intelligence, Computer Science Education, Elementary Secondary Education, Teaching Methods
Hwang, Gwo-Jen; Chang, Ching-Yi – Interactive Learning Environments, 2023
This study explores the trends of chatbots in education studies by conducting a literature review to analyze relevant papers published in the Social Science Citation Index (SSCI) journals by searching the Web of Science (WoS) database. From the analysis results, it was found that the United States, Taiwan and Hong Kong are the top three…
Descriptors: Artificial Intelligence, Teaching Methods, Technology Uses in Education, Databases
Saman Ebadi; Asieh Amini – Interactive Learning Environments, 2024
Artificial Intelligence (AI) technology in the educational context, particularly chatbotics, has made significant changes in learning English. This mixed-methods study is intended to explore university students' attitudes toward the potential role of artificial intelligence (AI)-assisted mobile applications. Meanwhile, the role of social presence…
Descriptors: Artificial Intelligence, Educational Technology, English (Second Language), Second Language Learning
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
Chen-Chen Liu; Hai-Jie Wang; Dan Wang; Yun-Fang Tu; Gwo-Jen Hwang; Youmei Wang – Interactive Learning Environments, 2024
Teachers' instructional design skills influence their teaching practices and student learning performances. However, researchers have found that the traditional one-to-many model of preservice teacher education prevents preservice teachers from receiving timely and individualized feedback, making it difficult to fill in theoretical knowledge gaps…
Descriptors: Preservice Teachers, Instructional Design, Teaching Skills, Knowledge Level
Asselman, Amal; Khaldi, Mohamed; Aammou, Souhaib – Interactive Learning Environments, 2023
Performance Factors Analysis (PFA) is considered one of the most important Knowledge Tracing (KT) approaches used for constructing adaptive educational hypermedia systems. It has shown a high prediction accuracy against many other KT approaches. While, the desire to estimate more accurately the student level leads researchers to enhance PFA by…
Descriptors: Algorithms, Artificial Intelligence, Factor Analysis, Student Behavior

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