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Okubo, Fumiya; Shiino, Tetsuya; Minematsu, Tsubasa; Taniguchi, Yuta; Shimada, Atsushi – IEEE Transactions on Learning Technologies, 2023
In this study, we propose an integrated system to support learners' reviews. In the proposed system, the review dashboard is used to recommend review contents that are adaptive to the individual learner's level of understanding and to present other information that is useful for review. The pages of the digital learning materials that are…
Descriptors: Learning Management Systems, Student Evaluation, Automation, Artificial Intelligence
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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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Imhof, Christof; Comsa, Ioan-Sorin; Hlosta, Martin; Parsaeifard, Behnam; Moser, Ivan; Bergamin, Per – IEEE Transactions on Learning Technologies, 2023
Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include a higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems (LMS) and learning analytics (LA), indicators of such behavior can be detected, enabling predictions of…
Descriptors: Prediction, Time Management, Electronic Learning, Artificial Intelligence
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Shard; Kumar, Devesh; Koul, Sapna; Siringoringo, Hotniar – IEEE Transactions on Learning Technologies, 2023
Students' and instructors' adoption of "e-learning management systems (e-LMSs)" is critical to their success in a "virtual learning environment." Students can use "e-learning" to obtain instructional materials to supplement "traditional classroom" instruction. This study intends to highlight the important…
Descriptors: Foreign Countries, Students, Behavior, Intention
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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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Kaiiali, Mustafa; Ozkaya, Armagan; Altun, Halis; Haddad, Hatem; Alier, Marc – IEEE Transactions on Learning Technologies, 2016
M-learning has enhanced the e-learning by making the learning process learner-centered. However, enforcing exam security in open environments where each student has his/her own mobile/tablet device connected to a Wi-Fi network through which it is further connected to the Internet can be one of the most challenging tasks. In such environments,…
Descriptors: Electronic Learning, Computer System Design, Computer Security, Confidentiality
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García-González, Herminio; Gayo, José Emilio Labra; del Puerto Paule-Ruiz, María – IEEE Transactions on Learning Technologies, 2017
We describe a new educational tool that relies on Semantic Web technologies to enhance lessons content. We conducted an experiment with 32 students whose results demonstrate better performance when exposed to our tool in comparison with a plain native tool. Consequently, this prototype opens new possibilities in lessons content enhancement.
Descriptors: Semantics, Teaching Methods, Electronic Learning, Course Content
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Brut, Mihaela M.; Sedes, Florence; Dumitrescu, Stefan D. – IEEE Transactions on Learning Technologies, 2011
This paper presents a solution to extend the IEEE LOM standard with ontology-based semantic annotations for efficient use of learning objects outside Learning Management Systems. The data model corresponding to this approach is first presented. The proposed indexing technique for this model development in order to acquire a better annotation of…
Descriptors: Management Systems, Linguistics, Semantics, Electronic Learning