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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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Tan, Hongye; Wang, Chong; Duan, Qinglong; Lu, Yu; Zhang, Hu; Li, Ru – Interactive Learning Environments, 2023
Automatic short answer grading (ASAG) is a challenging task that aims to predict a score for a given student response. Previous works on ASAG mainly use nonneural or neural methods. However, the former depends on handcrafted features and is limited by its inflexibility and high cost, and the latter ignores global word cooccurrence in a corpus and…
Descriptors: Automation, Grading, Computer Assisted Testing, Graphs
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Troussas, Christos; Giannakas, Filippos; Sgouropoulou, Cleo; Voyiatzis, Ioannis – Interactive Learning Environments, 2023
Computer-Supported Collaborative Learning is a promising innovation that ameliorates tutoring through modern technologies. However, the way of recommending collaborative activities to learners, by taking into account their learning needs and preferences, is an important issue of increasing interest. In this context, this paper presents a framework…
Descriptors: Computer Assisted Instruction, Cognitive Style, Cooperative Learning, Models
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Aguilar, J.; Buendia, O.; Pinto, A.; Gutiérrez, J. – Interactive Learning Environments, 2022
Social Learning Analytics (SLA) seeks to obtain hidden information in large amounts of data, usually of an educational nature. SLA focuses mainly on the analysis of social networks (Social Network Analysis, SNA) and the Web, to discover patterns of interaction and behavior of educational social actors. This paper incorporates the SLA in a smart…
Descriptors: Learning Analytics, Cognitive Style, Socialization, Social Networks
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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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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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Lau, Simon Boung-Yew; Lee, Chien-Sing; Singh, Yashwant Prasad – Interactive Learning Environments, 2015
With the proliferation of social Web applications, users can now collaboratively author, share and access hypermedia learning resources, contributing to richer learning experiences outside formal education. These resources may or may not be educational. However, they can be harnessed for educational purposes by adapting and personalizing them to…
Descriptors: Hypermedia, Metadata, Web 2.0 Technologies, Educational Resources
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Rodriguez, Daniel; Sicilia, Miguel Angel; Sanchez-Alonso, Salvador; Lezcano, Leonardo; Garcia-Barriocanal, Elena – Interactive Learning Environments, 2011
The online interaction of learners and tutors in activities with concrete objectives provides a valuable source of data that can be analyzed for different purposes. One of these purposes is the use of the information extracted from that interaction to aid tutors and learners in decision making about either the configuration of further learning…
Descriptors: Electronic Learning, Interaction, Tutors, Social Networks
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Huang, Yueh-Min; Huang, Yong-Ming; Liu, Chien-Hung; Tsai, Chin-Chung – Interactive Learning Environments, 2013
Web-based self-learning (WBSL) has received a lot of attention in recent years due to the vast amount of varied materials available in the Web 2.0 environment. However, this large amount of material also has resulted in a serious problem of cognitive overload that degrades the efficacy of learning. In this study, an information graphics method is…
Descriptors: Web 2.0 Technologies, Cognitive Processes, Difficulty Level, College Students
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Hwang, Wu-Yuin; Huang, Yueh-Min; Wu, Sheng-Yi – Interactive Learning Environments, 2011
The use of instant messaging to support e-learning will continue to gain importance because of its speed, effectiveness, and low cost. This study developed an MSN agent to mediate and facilitate students' learning in a Web-based course. The students' acceptance of the MSN agent and its effect on learning community identification and learning…
Descriptors: Electronic Learning, Feedback (Response), Web Based Instruction, Identification
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Lim, Wei-Ying; So, Hyo-Jeong; Tan, Seng-Chee – Interactive Learning Environments, 2010
While the growing prevalence of Web 2.0 in education opens up exciting opportunities for universities to explore expansive, new literacies practices, concomitantly, it presents unique challenges. Many universities are changing from a content delivery paradigm of eLearning 1.0 to a learner-focused paradigm of eLearning 2.0. In this article, we…
Descriptors: Models, Internet, Electronic Learning, Technology Uses in Education
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Capuruco, Renato A. C.; Capretz, Luiz F. – Interactive Learning Environments, 2009
There have been a number of frameworks and models developed to support different aspects of interactive learning. Some were developed to deal with course design through the application of authoring tools, whereas others such as conversational, advisory, and ontology-based systems were used in virtual classrooms to improve and support collaborative…
Descriptors: Learning Activities, Web Based Instruction, Online Courses, Computer Assisted Instruction
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Jovanovic, Jelena; Gasevic, Dragan; Torniai, Carlo; Bateman, Scott; Hatala, Marek – Interactive Learning Environments, 2009
Today's technology-enhanced learning practices cater to students and teachers who use many different learning tools and environments and are used to a paradigm of interaction derived from open, ubiquitous, and socially oriented services. In this context, a crucial issue for education systems in general, and for Intelligent Learning Environments…
Descriptors: Models, Interaction, Educational Technology, Design Requirements
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Scardamalia, M.; And Others – Interactive Learning Environments, 1992
Presents results from elementary school classroom uses of the Computer Supported Integrated Learning Environments (CSILE) software that stores student productions, including text and graphics, in one database to which all users have simultaneous access. Educational uses, effects, and outcomes of CSILE are described; and knowledge construction is…
Descriptors: Academic Achievement, Classroom Observation Techniques, Comparative Analysis, Computer Assisted Instruction