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Showing 1 to 15 of 38 results Save | Export
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Senthil Kumaran, V.; Malar, B. – Interactive Learning Environments, 2023
Churn in e-learning refers to learners who gradually perform less and become lethargic and may potentially drop out from the course. Churn prediction is a highly sensitive and critical task in an e-learning system because inaccurate predictions might cause undesired consequences. A lot of approaches proposed in the literature analyzed and modeled…
Descriptors: Electronic Learning, Dropouts, Accuracy, Classification
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Zhou, Yizhuo; Zhao, Jin; Zhang, Jianjun – Interactive Learning Environments, 2023
On e-learning platforms, most e-learners didn't complete the course successfully. It means that reducing dropout is a critical problem for the sustainability of e-learning. This paper aims to establish a predictive model to describe e-learners' dropout behavior, which can help the commercial e-learning platforms to make appropriate interventions…
Descriptors: Electronic Learning, Prediction, Dropouts, Student Behavior
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Xia, Xiaona – Interactive Learning Environments, 2023
Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance…
Descriptors: Behavior, Learning Processes, Electronic Learning, Algorithms
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Bingxue Zhang; Yang Shi; Yuxing Li; Chengliang Chai; Longfeng Hou – Interactive Learning Environments, 2023
The adaptive learning environment provides learning support that suits individual characteristics of students, and the student model of the adaptive learning environment is the key element to promote individualized learning. This paper provides a systematic overview of the existing student models, consequently showing that the Elo rating system…
Descriptors: Electronic Learning, Models, Students, Individualized Instruction
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Rosmansyah, Yusep; Putro, Budi Laksono; Putri, Atina; Utomo, Nur Budi; Suhardi – Interactive Learning Environments, 2023
In this article, smart learning environment (SLE) is defined as a hybrid learning system that provides learners and other stakeholders with a joyful learning process while achieving learning outcomes as a result of the employed intelligent tools and techniques. From literature study, existing SLE models and frameworks are difficult to understand…
Descriptors: Electronic Learning, Artificial Intelligence, Educational Technology, Technology Uses in Education
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Mona Tabatabaee-Yazdi – Interactive Learning Environments, 2024
In the era of COVID-19 and right after the announcement of it as a pandemic and threat to humanity by the World Health Organization, most educational activities were globally forced to shut down their traditional teaching/learning activities. This is one of the biggest and most vital changes of educational settings which have led to migration to…
Descriptors: English (Second Language), Second Language Instruction, COVID-19, Pandemics
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Guomin Chen; Pengrun Chen; Ying Wang; Nan Zhu – Interactive Learning Environments, 2024
The paper describes the research of causal relationships between the factors of technological, organizational, environmental, and personal contexts and their influence on the development of learning intentions in potential students. Its purpose was to develop a mechanism for designing a public online educational resource platform based on the…
Descriptors: MOOCs, Electronic Learning, Design, Technology Uses in Education
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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
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Ma, Ning; Li, Ya-Meng; Guo, Jia-Hui; Laurillard, Diana; Yang, Min – Interactive Learning Environments, 2023
The use of massive open online courses (MOOCs) for teacher professional development (TDP) has increased in the past decades. This study explored the key factors that influenced teachers' online course completion as a significant indicator of their success in a TPD MOOC. Six key influencing factors (self-efficacy, interaction with curriculum…
Descriptors: Inservice Teacher Education, MOOCs, Faculty Development, Pedagogical Content Knowledge
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Baginda Anggun Nan Cenka; Harry B. Santoso; Kasiyah Junus – Interactive Learning Environments, 2023
Presently, learning is more flexible, personal and has richer learning resources. In the digital era, students use digital tools in almost all aspects of learning, such as seeking information, note-taking, discussion and communication, which is in line with personal learning environments. Therefore, this study proposes a conceptual model of the…
Descriptors: Educational Environment, Lifelong Learning, Educational Resources, Electronic Learning
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Tang, Tao; Abuhmaid, Atef M.; Olaimat, Melad; Oudat, Dana M.; Aldhaeebi, Maged; Bamanger, Ebrahim – Interactive Learning Environments, 2023
Due to the spread of COVID-19 worldwide, a large number of universities had to close their campuses. To maintain teaching and learning during this disruption to the traditional teaching, most universities have adopted online teaching model. The current study aimed at investigating the efficacy of various online teaching modes as well as comparing…
Descriptors: Flipped Classroom, Electronic Learning, COVID-19, Pandemics
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Qi Wang; Shengquan Yu – Interactive Learning Environments, 2024
Learning resources are quite important for online learning while resource provision based on algorithms could not address learners' ubiquitous needs well. Moreover, the structure and content of resources are pre-defined which makes the "Structure" and "Content" coupled closely and could not easily adjust when learners' needs…
Descriptors: Electronic Learning, Educational Resources, Automation, Models
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Ming Du; Sufen Wang; Zhijun Wang; Leizhi Wang; Rong Yu; Mingyou Yin – Interactive Learning Environments, 2024
This study aims to explore the difference between on-line and off-line teaching and comprehensively evaluate the of elearning system operation effect as both technology-mediated learning and an open information system. This study develops "process-situation-result" (PSR) network, a comprehensive learning system evaluation model. It…
Descriptors: Electronic Learning, Asynchronous Communication, Synchronous Communication, Technology Uses in Education
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Hwang, Wu-Yuin; Hariyanti, Uun; Chen, Nian-Shing; Purba, Siska Wati Dewi – Interactive Learning Environments, 2023
The purpose of this study is to develop and validate an authentic contextual learning framework with six constructs to model the relationships among essential factors of authentic contextual learning. In particular, this framework explores the relationships across six constructs, i.e. learning by applying, healthy learning, collaborative learning,…
Descriptors: Authentic Learning, Models, Experiential Learning, Cooperative Learning
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Poitras, Eric; Butcher, Kirsten R.; Orr, Matthew; Hudson, Michelle A.; Larson, Madlyn – Interactive Learning Environments, 2022
This study mined student interactions with visual representations as a means to automate assessment of learning in a complex, inquiry-based learning environment. Log trace data of 143 middle school students' interactions with an interactive map in Research Quest (an inquiry-based, online learning environment) were analyzed. Students used the…
Descriptors: Middle School Students, Electronic Learning, Maps, Science Instruction
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