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Polyzou, Agoritsa; Karypis, George – IEEE Transactions on Learning Technologies, 2019
Developing tools to support students and learning in a traditional or online setting is a significant task in today's educational environment. The initial steps toward enabling such technologies using machine learning techniques focused on predicting the student's performance in terms of the achieved grades. However, these approaches do not…
Descriptors: Prediction, Academic Achievement, Low Achievement, Classification
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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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Jimenez, Fernando; Paoletti, Alessia; Sanchez, Gracia; Sciavicco, Guido – IEEE Transactions on Learning Technologies, 2019
In the European academic systems, the public funding to single universities depends on many factors, which are periodically evaluated. One of such factors is the rate of success, that is, the rate of students that do complete their course of study. At many levels, therefore, there is an increasing interest in being able to predict the risk that a…
Descriptors: Prediction, Risk, Dropouts, College Students
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Ruiperez-Valiente, Jose A.; Munoz-Merino, Pedro J.; Alexandron, Giora; Pritchard, David E. – IEEE Transactions on Learning Technologies, 2019
One of the reported methods of cheating in online environments in the literature is CAMEO (Copying Answers using Multiple Existences Online), where harvesting accounts are used to obtain correct answers that are later submitted in the master account which gives the student credit to obtain a certificate. In previous research, we developed an…
Descriptors: Computer Assisted Testing, Tests, Online Courses, Identification
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Cano, Alberto; Leonard, John D. – IEEE Transactions on Learning Technologies, 2019
Early warning systems have been progressively implemented in higher education institutions to predict student performance. However, they usually fail at effectively integrating the many information sources available at universities to make more accurate and timely predictions, they often lack decision-making reasoning to motivate the reasons…
Descriptors: Progress Monitoring, At Risk Students, Disproportionate Representation, Underachievement
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Srba, Ivan; Bielikova, Maria – IEEE Transactions on Learning Technologies, 2015
In the current time of globalization, collaboration among people in virtual environments is becoming an important precondition of success. This trend is reflected also in the educational domain where students collaborate in various short-term groups created repetitively but changing in each round (e.g. in MOOCs). Students in these kind of dynamic…
Descriptors: Cooperative Learning, Online Courses, Group Dynamics, Feedback (Response)
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Cetintas, Suleyman; Si, Luo; Aagard, Hans Peter; Bowen, Kyle; Cordova-Sanchez, Mariheida – IEEE Transactions on Learning Technologies, 2011
Microblogging is a popular technology in social networking applications that lets users publish online short text messages (e.g., less than 200 characters) in real time via the web, SMS, instant messaging clients, etc. Microblogging can be an effective tool in the classroom and has lately gained notable interest from the education community. This…
Descriptors: Information Retrieval, Accuracy, Synchronous Communication, Classification
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Magnisalis, I.; Demetriadis, S.; Karakostas, A. – IEEE Transactions on Learning Technologies, 2011
This study critically reviews the recently published scientific literature on the design and impact of adaptive and intelligent systems for collaborative learning support (AICLS) systems. The focus is threefold: 1) analyze critical design issues of AICLS systems and organize them under a unifying classification scheme, 2) present research evidence…
Descriptors: Evidence, Instructional Design, Bibliographic Databases, Classification