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Rezaei, Mohammadsadegh; Bobarshad, Hossein; Badie, Kambiz – Interactive Learning Environments, 2021
The development of information technology and social networks has created new opportunities to access lifelong learning in the form of informal learning. In an informal learning environment, learning takes place via Communities of Practice (CoP). The learning success factors in online CoPs are learners' similarity in learning interests and…
Descriptors: Prediction, Electronic Learning, Communities of Practice, Information Technology
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Yildiz Durak, Hatice – Interactive Learning Environments, 2021
The present study aims to determine the relation between Technological Pedagogical Content Knowledge (TPACK) levels of teachers and their self-efficacy in integrating technology, their technology literacy and their usage objective of social networks. Structural equation modeling was utilized to create a model explaining and predicting the…
Descriptors: Correlation, Social Networks, Pedagogical Content Knowledge, Technological Literacy
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Lemay, David John; Doleck, Tenzin – Interactive Learning Environments, 2022
Predicting student performance in Massive Open Online Courses (MOOCs) is important to aid in retention efforts. Researchers have demonstrated that video watching features can be used to accurately predict student test performance on video quizzes employing neural networks to predict video test grades from viewing behavior including video searching…
Descriptors: MOOCs, Academic Achievement, Prediction, Student Behavior
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Garcia, Consuelo; Privado, Jesús – Interactive Learning Environments, 2023
This article reports the findings of a study on cooperative factors that predict higher education students' satisfaction with using collaborative online tools. Although there is evidence of relationships between certain factors of teamwork and satisfaction in traditional groups that are guided by their teacher, little is known about what happens…
Descriptors: Student Satisfaction, Prediction, Personal Autonomy, Web Sites
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Cahillane, Marie; MacLean, Piers; Smy, Victoria – Interactive Learning Environments, 2019
Periods of no practice in performing a technical procedure may impact on the retention of the procedural skills required to produce VLE content. This exploratory paper reports a case study into the application of a validated skills retention model, the User Decision Aid (UDA). Use of the UDA results in a series of indicative retention rates…
Descriptors: College Faculty, Educational Technology, Technology Uses in Education, Retention (Psychology)
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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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Chen, Jyun-Chen – Interactive Learning Environments, 2022
"Learning by doing" involves completing a practice activity through the method of inquiry. This study combined the predict-observe-explain (POE) inquiry method and hands-on doing processes to develop a cycle-mode POED (predict-observe-explain-do) model with an e-learning system to help students complete a practice activity using…
Descriptors: Inquiry, Prediction, Observation, Hands on Science
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Yang, Jinzhong; Wang, Qiyun; Wang, Jingying; Huang, Muxiong; Ma, Yongjun – Interactive Learning Environments, 2021
E-Schoolbag is an integrated platform presented on the portable digital devices for educational purposes. Successful and sustainable integration of e-Schoolbag into the classroom often demands teachers to be equipped with essential knowledge, skills, and willingness to employ it on a voluntary basis. The purpose of this study was to examine if…
Descriptors: Elementary School Teachers, Secondary School Teachers, Pedagogical Content Knowledge, Technological Literacy
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Ding, Xinyi; Larson, Eric C.; Doyle, Amanda; Donahoo, Kevin; Rajgopal, Radhika; Bing, Eric – Interactive Learning Environments, 2021
In this paper, we develop a context-aware, tablet-based learning module for adult education. Specifically, we focus on adult education in healthcare-teaching learners to perform a medical screening procedure. Based upon how learners navigate through the learning module (e.g. swipe-speed and click duration, among others), we use machine learning to…
Descriptors: Handheld Devices, Educational Technology, Navigation (Information Systems), Learning Modules
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Er, Erkan; Gómez-Sánchez, Eduardo; Dimitriadis, Yannis; Bote-Lorenzo, Miguel L.; Asensio-Pérez, Juan I.; Álvarez-Álvarez, Susana – Interactive Learning Environments, 2019
This paper presents the findings of a mixed-methods research that explored the potentials emerging from aligning learning design (LD) and learning analytics (LA) during the design of a predictive analytics solution and from involving the instructors in the design process. The context was a past massive open online course, where the learner data…
Descriptors: Alignment (Education), Learning Analytics, Instructional Design, Teacher Participation
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Wu, Pai-Hsing; Wu, Hsin-Kai; Kuo, Che-Yu; Hsu, Ying-Shao – Interactive Learning Environments, 2015
Computer-based learning tools include design features to enhance learning but learners may not always perceive the existence of these features and use them in desirable ways. There might be a gap between what the tool features are designed to offer (intended affordance) and what they are actually used (actual affordance). This study thus aims at…
Descriptors: Science Instruction, Computer Uses in Education, Educational Technology, High School Students