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Neil Dixon; Rob Howe; Uwe Matthias Richter – Research in Learning Technology, 2025
Learning analytics (LA) provides insight into student performance and progress, allowing for targeted interventions and support to improve the student learning experience. Uses of LA are diverse, including measuring student engagement, retention, progression, student well-being and curriculum development. This article provides perspectives on the…
Descriptors: Learning Analytics, Educational Benefits, Case Studies, Higher Education
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Punyapa Boontam; Supakorn Phoocharoensil – PASAA: Journal of Language Teaching and Learning in Thailand, 2024
In recent years, there has been growing interest in the use of data-driven learning (DDL) in L2 writing instruction. This paper examined whether and to what extent DDL activities could enhance the writing complexity, accuracy, and fluency (CAF) of 30 Thai EFL learners. The presentation of DDL in this study was hands-on concordancing with the…
Descriptors: Foreign Countries, English (Second Language), Data Use, Difficulty Level
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Clark, Jo-Anne; Liu, Yulin; Isaias, Pedro – Australasian Journal of Educational Technology, 2020
Critical success factors (CSFs) have been around since the late 1970s and have been used extensively in information systems implementations. CSFs provide a comprehensive understanding of the multiple layers and dimensions of implementation success. In the specific context of learning analytics (LA), identifying CSFs can maximise the possibilities…
Descriptors: Learning Analytics, Program Implementation, Data Use, Accuracy
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Gyamfi, George; Hanna, Barbara; Khosravi, Hassan – Assessment & Evaluation in Higher Education, 2022
Engaging students in the creation of learning resources is an effective way of developing a repository of revision items. However, a selection process is needed to separate high- from low-quality resources as some of the materials created by students can be ineffective, inappropriate or incorrect. In this study, we share our experiences and…
Descriptors: Peer Evaluation, Student Developed Materials, Educational Technology, Scoring
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Baneres, David; Rodriguez-Gonzalez, M. Elena; Serra, Montse – IEEE Transactions on Learning Technologies, 2019
Identifying at-risk students as soon as possible is a challenge in educational institutions. Decreasing the time lag between identification and real at-risk state may significantly reduce the risk of failure or disengage. In small courses, their identification is relatively easy, but it is impractical on larger ones. Current Learning Management…
Descriptors: Prediction, Feedback (Response), At Risk Students, College Freshmen