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Cormack, Andrew; Reeve, David – Journal of Learning Analytics, 2022
With student and staff wellbeing a growing concern, several authors have asked whether existing data might help institutions provide better support. By analogy with the established field of Learning Analytics, this might involve identifying causes of stress, improving access to information for those who need it, suggesting options, providing rapid…
Descriptors: Foreign Countries, Well Being, Data Use, Ethics
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Tsai, Yi-Shan; Whitelock-Wainwright, Alexander; Gasevic, Dragan – Journal of Learning Analytics, 2021
The adoption of learning analytics (LA) in complex educational systems is woven into sociocultural and technical challenges that have induced distrust in data and difficulties in scaling LA. This paper presents a study that investigated areas of distrust and threats to trustworthy LA through a series of consultations with teaching staff and…
Descriptors: Learning Analytics, Program Implementation, Trust (Psychology), Higher Education
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Olney, Tom; Walker, Steve; Wood, Carlton; Clarke, Anactoria – Journal of Learning Analytics, 2021
Most higher education institutions view their increasing use of learning analytics as having significant potential to improve student academic achievement, retention outcomes, and learning and teaching practice but the realization of this potential remains stubbornly elusive. While there is an abundance of published research on the creation of…
Descriptors: Learning Analytics, Higher Education, STEM Education, College Faculty
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Nguyen, Quan; Rienties, Bart; Whitelock, Denise – Journal of Learning Analytics, 2020
The use of analytical methods from learning analytics (LA) research combined with visualizations of learning activities using learning design (LD) tools and frameworks has provided important insight into how instructors design for learning. Nonetheless, there are many subtle nuances in instructors' design decisions that might not easily be…
Descriptors: Instructional Design, Online Courses, Distance Education, Electronic Learning
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Herodotou, Christothea; Naydenova, Galina; Boroowa, Avi; Gilmour, Alison; Rienties, Bart – Journal of Learning Analytics, 2020
Despite the potential of Predictive Learning Analytics (PLAs) to identify students at risk of failing their studies, research demonstrating effective application of PLAs to higher education is relatively limited. The aims of this study are: (1) to identify whether and how PLAs can inform the design of motivational interventions; and (2) to capture…
Descriptors: Learning Analytics, Predictive Measurement, Student Motivation, Intervention
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Herodotou, Christothea; Rienties, Bart; Verdin, Barry; Boroowa, Avinash – Journal of Learning Analytics, 2019
Predictive Learning Analytics (PLA) aim to improve learning by identifying students at risk of failing their studies. Yet, little is known about how best to integrate and scaffold PLA initiatives into higher education institutions. Towards this end, it becomes essential to capture and analyze the perceptions of relevant educational stakeholders…
Descriptors: Prediction, Data Analysis, Higher Education, Distance Education