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Mark Johnson; Rafiq Saleh – Interactive Learning Environments, 2024
Educational assessment is inherently uncertain, where physiological, psychological and social factors play an important role in establishing judgements which are assumed to be "absolute". AI and other algorithmic approaches to grading of student work strip-out uncertainty, leading to a lack of inspectability in machine judgement and…
Descriptors: Artificial Intelligence, Evaluation Methods, Technology Uses in Education, Man Machine Systems
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Alshurideh, Muhammad; Al Kurdi, Barween; Salloum, Said A.; Arpaci, Ibrahim; Al-Emran, Mostafa – Interactive Learning Environments, 2023
Despite the plethora of m-learning acceptance studies, few have tackled the importance of examining the actual use of m-learning systems from the lenses of social influence, expectation-confirmation, and satisfaction. Additionally, most of the prior technology adoption literature tends to use the structural equation modeling (SEM) technique in…
Descriptors: Electronic Learning, Prediction, Least Squares Statistics, Structural Equation Models
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Yung-Hsiang Hu; Jo Shan Fu; Hui-Chin Yeh – Interactive Learning Environments, 2024
Artificial intelligence aims to restructure and process re-engineering education and teaching processes and accelerate the evolution of the whole education system from information to intelligence. Robotic Process Automation (RPA) robots learn by observing people at work, analyzing user processes repeatedly, and adjusting or correcting automated…
Descriptors: Intelligent Tutoring Systems, Robotics, Automation, Instructional Effectiveness
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Milkova, Eva; Pekarkova, Simona – Interactive Learning Environments, 2023
The presented study focuses on children aged from 5 to 6.5 who attend Czech kindergartens. Its purpose is to explore a potential positive impact of an educational game application on malleability of children's spatial skills through the application usage. The research was conducted as a pedagogical experiment in which the pre-test and post-test…
Descriptors: Spatial Ability, Kindergarten, Preschool Children, Educational Games
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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
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Wang, Shuai; Christensen, Claire; Cui, Wei; Tong, Richard; Yarnall, Louise; Shear, Linda; Feng, Mingyu – Interactive Learning Environments, 2023
Adaptive learning systems personalize instruction to students' individual learning needs and abilities. Such systems have shown positive impacts on learning. Many schools in the United States have adopted adaptive learning systems, and the rate of adoption in China is accelerating, reaching almost 2 million unique users for one product alone in…
Descriptors: Comparative Analysis, Teaching Methods, Intelligent Tutoring Systems, Foreign Countries
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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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Ting-Chia Hsu; Ching Chang; Tien-Hsiu Jen – Interactive Learning Environments, 2024
Young learners' vocabulary learning needs interaction with language input when they are engaged in an activity. Given that AI-supported image recognition technologies offer hands-on learning in authentic contexts, and that self-regulated learning (SRL) enables learners to monitor and evaluate their learning when interacting with multi-sensory…
Descriptors: Metacognition, Multisensory Learning, Vocabulary Development, Learning Strategies
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Paquette, Luc; Baker, Ryan S. – Interactive Learning Environments, 2019
Learning analytics research has used both knowledge engineering and machine learning methods to model student behaviors within the context of digital learning environments. In this paper, we compare these two approaches, as well as a hybrid approach combining the two types of methods. We illustrate the strengths of each approach in the context of…
Descriptors: Comparative Analysis, Student Behavior, Models, Case Studies
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Premlatha, K. R.; Dharani, B.; Geetha, T. V. – Interactive Learning Environments, 2016
E-learning allows learners individually to learn "anywhere, anytime" and offers immediate access to specific information. However, learners have different behaviors, learning styles, attitudes, and aptitudes, which affect their learning process, and therefore learning environments need to adapt according to these differences, so as to…
Descriptors: Electronic Learning, Profiles, Automation, Classification
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Ijaz, Kiran; Bogdanovych, Anton; Trescak, Tomas – Interactive Learning Environments, 2017
In this paper, we investigate an application of virtual reality and artificial intelligence (AI) as a technological combination that has a potential to improve the learning experience and engage with the modern generation of students. To address this need, we have created a virtual reality replica of one of humanity's first cities, the city of…
Descriptors: Educational Technology, Technology Uses in Education, History Instruction, Simulated Environment
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Hsiao, Hsien-Sheng; Chang, Cheng-Sian; Lin, Chien-Yu; Hsu, Hsiu-Ling – Interactive Learning Environments, 2015
This study focused on an intelligent robot which was viewed as a language teaching/learning tool to improve children's reading ability, reading interest, and learning behavior. The iRobiQ, with its multimedia contents, was employed to encourage children to read, speak, and answer questions. Fifty-seven pre-kindergarteners participated in this…
Descriptors: Robotics, Artificial Intelligence, Teaching Methods, Reading Ability
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Shute, Valerie J.; Glaser, Robert – Interactive Learning Environments, 1990
Presents an evaluation of "Smithtown," an intelligent tutoring system designed to teach inductive inquiry skills and principles of basic microeconomics. Two studies of individual differences in learning are described, including a comparison of knowledge acquisition with traditional instruction; hypotheses tested are discussed; and the…
Descriptors: Artificial Intelligence, Cluster Analysis, Comparative Analysis, Computer Assisted Instruction