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Sonja Kleter; Uwe Matzat; Rianne Conijn – IEEE Transactions on Learning Technologies, 2024
Much of learning analytics research has focused on factors influencing model generalizability of predictive models for academic performance. The degree of model generalizability across courses may depend on aspects, such as the similarity of the course setup, course material, the student cohort, or the teacher. Which of these contextual factors…
Descriptors: Prediction, Models, Academic Achievement, Learning Analytics
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Roshan Lal Neupane; Prasad Calyam; Songjie Wang; Kiran Neupane; Ashish Pandey; Xiyao Cheng; Durbek Gafurov; Hemanth Sai Yeddulapalli; Noah Glaser; Kanu Priya Singh; Yuanyuan Gu; Shangman Li; Sharan Srinivas – IEEE Transactions on Learning Technologies, 2024
Cloud-hosted services are being increasingly used in hosting business and scientific applications due to cost-effectiveness, scalability, and ease of deployment. To facilitate rapid development, change and release process of cloud-hosted applications, the area of development and operations (DevOps) is fast evolving. It is necessary to train the…
Descriptors: Online Courses, Computer Assisted Instruction, Learning Management Systems, Computer Assisted Design
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Gupta, Anika; Garg, Deepak; Kumar, Parteek – IEEE Transactions on Learning Technologies, 2022
With the onset of online education via technology-enhanced learning platforms, large amount of educational data is being generated in the form of logs, clickstreams, performance, etc. These Virtual Learning Environments provide an opportunity to the researchers for the application of educational data mining and learning analytics, for mining the…
Descriptors: Markov Processes, Online Courses, Learning Management Systems, Learning Analytics
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Uto, Masaki; Nguyen, Duc-Thien; Ueno, Maomi – IEEE Transactions on Learning Technologies, 2020
With the wide spread large-scale e-learning environments such as MOOCs, peer assessment has been popularly used to measure the learner ability. When the number of learners increases, peer assessment is often conducted by dividing learners into multiple groups to reduce the learner's assessment workload. However, in such cases, the peer assessment…
Descriptors: Item Response Theory, Electronic Learning, Peer Evaluation, Accuracy
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Wan, Han; Zhong, Zihao; Tang, Lina; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2023
Small private online courses (SPOCs) have influenced teaching and learning in China's higher education. Learning management systems (LMSs) are important components in SPOCs. They can collect various data related to student behavior and support pedagogical interventions. This research used feature engineering and nearest neighbor smoothing models…
Descriptors: Online Courses, Learning Management Systems, Higher Education, Student Behavior
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Kostopoulos, Georgios; Karlos, Stamatis; Kotsiantis, Sotiris – IEEE Transactions on Learning Technologies, 2019
Educational data mining has gained a lot of attention among scientists in recent years and constitutes an efficient tool for unraveling the concealed knowledge in educational data. Recently, semisupervised learning methods have been gradually implemented in the educational process demonstrating their usability and effectiveness. Cotraining is a…
Descriptors: Academic Achievement, Case Studies, Usability, Data Analysis
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Schwendimann, Beat A.; Rodriguez-Triana, Maria Jesus; Vozniuk, Andrii; Prieto, Luis P.; Boroujeni, Mina Shirvani; Holzer, Adrian; Gillet, Denis; Dillenbourg, Pierre – IEEE Transactions on Learning Technologies, 2017
This paper presents a systematic literature review of the state-of-the-art of research on learning dashboards in the fields of Learning Analytics and Educational Data Mining. Research on learning dashboards aims to identify what data is meaningful to different stakeholders and how data can be presented to support sense-making processes. Learning…
Descriptors: Literature Reviews, Educational Research, Data Analysis, Data Processing
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Sun, Geng; Shen, Jun – IEEE Transactions on Learning Technologies, 2014
Mobile learning is an emerging trend that brings many advantages to distributed learners, enabling them to achieve collaborative learning, in which the virtual teams are usually built to engage multiple learners working together towards the same pedagogical goals in online courses. However, the socio-technical mechanisms to enhance teamwork…
Descriptors: Teamwork, Computer Software, Online Courses, Teaching Methods