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Showing 1 to 15 of 26 results Save | Export
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Marc Burchart; Joerg M. Haake – IEEE Transactions on Learning Technologies, 2024
In distance education courses with a large number of students and groups, the organization and facilitation of collaborative writing tasks are challenging. Teachers need support for planning, specification, execution, monitoring, and evaluation of collaborative writing tasks in their course. This requires a collaborative learning platform for…
Descriptors: Writing Instruction, Distance Education, Large Group Instruction, Learning Management Systems
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Deho, Oscar Blessed; Joksimovic, Srecko; Li, Jiuyong; Zhan, Chen; Liu, Jixue; Liu, Lin – IEEE Transactions on Learning Technologies, 2023
Many educational institutions are using predictive models to leverage actionable insights using student data and drive student success. A common task has been predicting students at risk of dropping out for the necessary interventions to be made. However, issues of discrimination by these predictive models based on protected attributes of students…
Descriptors: Learning Analytics, Models, Student Records, Prediction
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Fincham, Ed; Rozemberczki, Benedek; Kovanovic, Vitomir; Joksimovic, Srecko; Jovanovic, Jelena; Gasevic, Dragan – IEEE Transactions on Learning Technologies, 2021
In this article, we empirically validate Tinto's Student Integration model, in particular, the predictions the model makes regarding both students' academic outcomes and their dropout decisions. In doing so, we analyze three decades' worth of student enrollments at an Australian university and present a novel methodological approach using graph…
Descriptors: Models, Prediction, Outcomes of Education, Dropouts
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Sanchez-Ferreres, Josep; Delicado, Luis; Andaloussi, Amine Abbab; Burattin, Andrea; Calderon-Ruiz, Guillermo; Weber, Barbara; Carmona, Josep; Padro, Lluis – IEEE Transactions on Learning Technologies, 2020
The creation of a process model is primarily a formalization task that faces the challenge of constructing a syntactically correct entity, which accurately reflects the semantics of reality, and is understandable to the model reader. This article proposes a framework called "Model Judge," focused toward the two main actors in the process…
Descriptors: Models, Automation, Validity, Natural Language Processing
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Peralta, Montserrat; Alarcon, Rosa; Pichara, Karim E.; Mery, Tomas; Cano, Felipe; Bozo, Jorge – IEEE Transactions on Learning Technologies, 2018
Educational resources can be easily found on the Web. Most search engines base their algorithms on a resource's text or popularity, requiring teachers to navigate the results until they find an appropriate resource. This makes searching for resources a tedious and cumbersome task. Specialized repositories contain resources that are annotated with…
Descriptors: Educational Resources, Metadata, Foreign Countries, Bayesian Statistics
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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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Ortigosa, Alvaro; Carro, Rosa M.; Bravo-Agapito, Javier; Lizcano, David; Alcolea, Juan Jesus; Blanco, Oscar – IEEE Transactions on Learning Technologies, 2019
This paper presents the work done to support student dropout risk prevention in a real online e-learning environment: A Spanish distance university with thousands of undergraduate students. The main goal is to prevent students from abandoning the university by means of retention actions focused on the most at-risk students, trying to maximize the…
Descriptors: At Risk Students, Dropout Prevention, Undergraduate Students, Distance Education
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Vrablecová, Petra; Šimko, Marián – IEEE Transactions on Learning Technologies, 2016
The domain model is an essential part of an adaptive learning system. For each educational course, it involves educational content and semantics, which is also viewed as a form of conceptual metadata about educational content. Due to the size of a domain model, manual domain model creation is a challenging and demanding task for teachers or…
Descriptors: Semantics, Models, Metadata, Programming
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Cocea, Mihaela; Magoulas, George D. – IEEE Transactions on Learning Technologies, 2017
Exploratory learning environments (ELEs) promote a view of learning that encourages students to construct and/or explore models and observe the effects of modifying their parameters. The freedom given to learners in this exploration context leads to a variety of learner approaches for constructing models and makes modelling of learner behavior a…
Descriptors: Generalization, Mathematics Instruction, Computer Simulation, Discovery Learning
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Scheffel, Maren; Drachsler, Hendrik; de Kraker, Joop – IEEE Transactions on Learning Technologies, 2017
In collaborative learning environments, students work together on assignments in virtual teams and depend on each other's contribution to achieve their learning objectives. The online learning environment, however, may not only facilitate but also hamper group communication, coordination, and collaboration. Group awareness widgets that visualize…
Descriptors: Cooperative Learning, Electronic Learning, Computer Oriented Programs, Group Dynamics
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Thalmann, Stefan; Maier, Ronald – IEEE Transactions on Learning Technologies, 2017
Knowledge transfer between employees is a primary concern in organizations. Employees create or acquire content that partially represents knowledge. These knowledge elements are specific to the context in and for which they are created and rarely address the learning needs of other employees in other work situations. Organizations therefore need…
Descriptors: Workplace Learning, Models, Technology Transfer, Knowledge Management
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Wan, Han; Liu, Kangxu; Yu, Qiaoye; Gao, Xiaopeng – IEEE Transactions on Learning Technologies, 2019
Most educational institutions adopted the hybrid teaching mode through learning management systems. The logging data/clickstream could describe learners' online behavior. Many researchers have used them to predict students' performance, which has led to a diverse set of findings, but how to use insights from captured data to enhance learning…
Descriptors: Educational Practices, Learner Engagement, Identification, Study Habits
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Papanikolaou, Kyparisia A. – IEEE Transactions on Learning Technologies, 2015
In this paper, we discuss how externalizing learners' interaction behavior may support learners' explorations in an adaptive educational hypermedia environment that provides activity-oriented content. In particular, we propose a model for producing interpretative views of learners' interaction behavior and we further apply this model to…
Descriptors: Student Behavior, Interaction, Hypermedia, Educational Technology
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Yang, Juan; Huang, Zhi Xing; Gao, Yue Xiang; Liu, Hong Tao – IEEE Transactions on Learning Technologies, 2014
During the past decade, personalized e-learning systems and adaptive educational hypermedia systems have attracted much attention from researchers in the fields of computer science Aand education. The integration of learning styles into an intelligent system is a possible solution to the problems of "learning deviation" and…
Descriptors: Cognitive Style, Pattern Recognition, Intelligent Tutoring Systems, Prediction
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Conijn, Rianne; Snijders, Chris; Kleingeld, Ad; Matzat, Uwe – IEEE Transactions on Learning Technologies, 2017
With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students' online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables…
Descriptors: Blended Learning, Predictor Variables, Predictive Validity, Predictive Measurement
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