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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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Delaval, Marine; Michinov, Nicolas; Le Bohec, Olivier; Le Hénaff, Benjamin – Interactive Learning Environments, 2017
The aim of this study was to examine how social or temporal-self comparison feedback, delivered in real-time in a web-based training environment, could influence the academic performance of students in a statistics examination. First-year psychology students were given the opportunity to train for a statistics examination during a semester by…
Descriptors: Mathematics Achievement, Statistics, Self Evaluation (Individuals), Feedback (Response)
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Wanxue Zhang; Lingling Meng; Bilan Liang – Interactive Learning Environments, 2023
With the continuous development of education, personalized learning has attracted great attention. How to evaluate students' learning effects has become increasingly important. In information technology courses, the traditional academic evaluation focuses on the student's learning outcomes, such as "scores" or "right/wrong,"…
Descriptors: Information Technology, Computer Science Education, High School Students, Scoring
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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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Huang, Xiaoxia – Interactive Learning Environments, 2017
Previous research has indicated the disconnect between example-based research focusing on worked examples (WEs) and that focusing on modeling examples. The purpose of this study was to examine and compare the effect of four different types of examples from the two separate lines of research, including standard WEs, erroneous WEs, expert (masterly)…
Descriptors: Teaching Methods, Problem Solving, Academic Achievement, Cognitive Processes
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Smith, Cary Stacy; Hung, Li-Ching – Interactive Learning Environments, 2017
In Taiwan, teaching focuses around lecturing, with students having little opportunity to interact with each other. Problem-based learning (PBL) is a means of instruction where students learn the subject by being active participants in the pedagogical process, with the emphasis on problem-solving. In this study, the authors investigated whether PBL…
Descriptors: Foreign Countries, Problem Based Learning, Computer Literacy, Self Efficacy
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Wu, Jiun Yu; Peng, Ya-Chun – Interactive Learning Environments, 2017
This study tested the effects of the modality of reading formats (electronic vs. print), online reading habits (engagement in different online reading activities), use of cognitive strategies, metacognitive knowledge, and navigation skills on printed and electronic reading literacy across regions. Participants were 31,784 fifteen-year-old students…
Descriptors: Reading Habits, Literacy, Printed Materials, Information Seeking
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Shadiev, Rustam; Wu, Ting-Ting; Huang, Yueh-Min – Interactive Learning Environments, 2017
In this study, we provide STR-texts to non-native English speaking students during English lectures to facilitate learning, attention, and meditation. We carry out an experiment to test the feasibility of our approach. Our results show that the participants in the experimental group both outperform those in the control group on the post-tests and…
Descriptors: College Students, Second Language Learning, English (Second Language), Assistive Technology
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Chen, Chih-Ming; Wang, Jung-Ying; Chen, Yong-Ting; Wu, Jhih-Hao – Interactive Learning Environments, 2016
To reduce effectively the reading anxiety of learners while reading English articles, a C4.5 decision tree, a widely used data mining technique, was used to develop a personalized reading anxiety prediction model (PRAPM) based on individual learners' reading annotation behavior in a collaborative digital reading annotation system (CDRAS). In…
Descriptors: Reading Strategies, Prediction, Models, Quasiexperimental Design
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Chang, Rong-Chi; Chung, Liang-Yi; Huang, Yong-Ming – Interactive Learning Environments, 2016
The learning of plants has garnered considerable attention in recent years, but students often lack the motivation to learn about the process of plant growth. Also, students are not able to apply what they have learned in class in the form of observation, since plant growth takes a long time. In this study, we use augmented reality (AR) technology…
Descriptors: Plants (Botany), Teaching Methods, Student Motivation, Simulated Environment
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Chang, Hsin-Yi; Hsu, Ying-Shao; Wu, Hsin-Kai – Interactive Learning Environments, 2016
We investigated the impact of an augmented reality (AR) versus interactive simulation (IS) activity incorporated in a computer learning environment to facilitate students' learning of a socio-scientific issue (SSI) on nuclear power plants and radiation pollution. We employed a quasi-experimental research design. Two classes (a total of 45…
Descriptors: Comparative Analysis, Computer Simulation, Computer Uses in Education, Science and Society