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Zehui Zhan; Yao Tong; Xixin Lan; Baichang Zhong – Interactive Learning Environments, 2024
In recent years, Game-Based Learning (GBL) has been widely adopted in various educational settings. This paper aims to review empirical studies that adopt GBL in the field of AI education and explore its future research perspectives. After a systematic keyword search in the online database and a snowballing approach, a total of 125 empirical…
Descriptors: Game Based Learning, Robotics, Teaching Methods, Artificial Intelligence
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
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
Wan, Haipeng; Yu, Shengquan – Interactive Learning Environments, 2023
Most online learning researchers use resource recommendation and retrieve based on learning performance and learning style to provide accurate learning resources, but it is a closed and passive adaptive way. Learners always do not know the recommendation rationale and just receive the result-oriented recommended resources without having a chance…
Descriptors: Electronic Learning, Intelligent Tutoring Systems, Artificial Intelligence, Cognitive Mapping
Vladimir Beketov; Marina Lebedeva; Marina Taranova – Interactive Learning Environments, 2024
The use of the innovative technologies, in particular virtual and augmented reality technologies, can have a significant impact on the training of future doctors. The sample set of the experiment consisted of 211 students from I.M. Sechenov First Moscow State Medical University. The students were divided into 4 age groups: full-time students of…
Descriptors: Artificial Intelligence, Computer Simulation, Academic Achievement, Influence of Technology
Chi-Jen Lin; Husni Mubarok; Rakha Ramadhana A.B.; Samuel Gasperius; Chia-Ying Liu; Salisa Sawettanun; Kantapat Meesomyut; Ling-Rong Zheng – Interactive Learning Environments, 2024
This systematic review aimed to investigate the role of technology as a solution in Speech-Language Pathology (SLP). A total of 49 articles published between 2004 and 2023 were examined to gather information on general aspects, methodology, technology implementation, learning outcomes, and limitations and solutions related to technology-enhanced…
Descriptors: Educational Trends, Technology Uses in Education, Learning Strategies, Speech Language Pathology
Ai-Jou Pan; Yu-Che Huang; Chin-Feng Lai – Interactive Learning Environments, 2024
Engineering education emphasizes experiential learning and laboratory experience, an approach which has faced significant challenges during the COVID-19 pandemic. The inability to conduct hands-on laboratory experiments in engineering courses can significantly impede the student's learning experience, as well as their acquisition and retention of…
Descriptors: Learning Management Systems, Hands on Science, Distance Education, Laboratories
Ching-Yi Chang; Patcharin Panjaburee; Shao-Chen Chang – Interactive Learning Environments, 2024
Educators have recognized the importance of providing a realistic learning environment which helps learners to not only comprehend learning content, but also to link the content to practical problems. Such an environment can hence foster problem-solving skills in nursing training. However, when learners interact in a virtual environment with rich…
Descriptors: Artificial Intelligence, Context Effect, Nursing Education, Technology Integration
Min-Chi Chiu; Gwo-Jen Hwang; Lu-Ho Hsia; Fong-Ming Shyu – Interactive Learning Environments, 2024
In a conventional art course, it is important for a teacher to provide feedback and guidance to individual students based on their learning status. However, it is challenging for teachers to provide immediate feedback to students without any aid. The advancement of artificial intelligence (AI) has provided a possible solution to cope with this…
Descriptors: Art Education, Artificial Intelligence, Teaching Methods, Comparative Analysis

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