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Xiao Wen; Hu Juan – Interactive Learning Environments, 2024
To address three issues identified in previous research this study proposes a clustering-based MOOC dropout identification method and an early prediction model based on deep learning. The MOOC learning behavior of self-paced students was analyzed, and two well-known MOOC datasets were used for analysis and validation. The findings are as follows:…
Descriptors: MOOCs, Dropouts, Dropout Characteristics, Dropout Research
Eisuke Saito; Jennifer Mansfield; Richard O'Donovan – Interactive Learning Environments, 2024
By assessing student engagement with learning tasks along with students' understanding of subject matter before and during teaching, teachers are able to shift their teaching approaches through improvisational pedagogical reasoning in real time. However, if a teacher does not know how to respond to students' cues, their capacity to effectively…
Descriptors: Educational Practices, Teaching Methods, Reflective Teaching, Decision Making
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
Ahmad, Faizan; Zongwei, Luo; Ahmed, Zeeshan; Muneeb, Sara – Interactive Learning Environments, 2023
An insight regarding few of the experiences during video games playing activity is still fuzzy. This paper presents an extensive empirical study that analyzes the experiences of 100 participants (i.e. 25 children, younger adults, older adults, and elders each) during brain games play. This concludes a number of significant correlations among the…
Descriptors: Children, Young Adults, Older Adults, Experience
Xia, Xiaona – Interactive Learning Environments, 2023
The interactive learning is a continuous process, which is full of a large number of learning interaction activities. The data generated between learners and learning interaction activities can reflect the online learning behaviors. Through the correlation analysis among learning interaction activities, this paper discusses the potential…
Descriptors: Behavior Patterns, Learning Analytics, Decision Making, Correlation
Cai, Yiyu; Chiew, Ruby; Nay, Zin Tun; Indhumathi, Chandrasekaran; Huang, Lihui – Interactive Learning Environments, 2017
Basic social interaction and executing certain tasks can be difficult for children with autism spectrum disorder (ASD). The symptoms of such behaviour include inappropriate gestures, body language and facial expressions, lack of interest in certain tasks, cognitive disability in coordination of limbs, and a difficulty in comprehending tasks'…
Descriptors: Autism, Pervasive Developmental Disorders, Interaction, Interpersonal Relationship
Johnson, Mark William; Sherlock, David – Interactive Learning Environments, 2014
The Personal Learning Environment (PLE) has been presented in a number of guises over a period of 10 years as an intervention which seeks the reorganisation of educational technology through shifting the "locus of control" of technology towards the learner. In the intervening period to the present, a number of initiatives have attempted…
Descriptors: Educational Environment, Intervention, Educational Technology, Locus of Control
Wong, Lung-Hsiang; Looi, Chee-Kit – Interactive Learning Environments, 2012
The notion of a system adapting itself to provide support for learning has always been an important issue of research for technology-enabled learning. One approach to provide adaptivity is to use social navigation approaches and techniques which involve analysing data of what was previously selected by a cluster of users or what worked for…
Descriptors: Electronic Learning, Entomology, Educational Technology, Individualized Instruction
Manlove, Sarah; Lazonder, Ard W.; de Jong, Ton – Interactive Learning Environments, 2009
Scaffolds to plan, monitor, and evaluate learning within technology-enhanced inquiry and modeling environments are often little used by students. One reason may be that students frequently work collaboratively in these settings and their group work may interfere with the use of regulative supports. This research compared the use of regulative…
Descriptors: Learning Strategies, Educational Technology, Scaffolding (Teaching Technique), High School Students
Teo, Timothy – Interactive Learning Environments, 2012
This study examined pre-service teachers' self-reported intention to use technology. One hundred fifty-seven participants completed a survey questionnaire measuring their responses to six constructs from a research model that integrated the Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB). Structural equation modeling was…
Descriptors: Foreign Countries, Educational Technology, Structural Equation Models, Computer Uses in Education
Kenny, Claire; Pahl, Claus – Interactive Learning Environments, 2009
Active learning facilitated through interactive and adaptive learning environments differs substantially from traditional instructor-oriented, classroom-based teaching. We present a web-based e-learning environment that integrates knowledge learning and skills training. How these tools are used most effectively is still an open question. We…
Descriptors: Feedback (Response), Active Learning, Educational Technology, Evaluation Methods

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