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Patterson, Chris R.; York, Emily; Maxham, Danielle; Molina, Rudy; Mabrey, Paul, III – Journal of Learning Analytics, 2023
The anticipation, inclusion, responsiveness, and reflexivity (AIRR) framework (Stilgoe et al., 2013) is a novel framework that has helped those in science and technology fields shift their focus from products to the processes used to create those products. However, the framework has not been known to be applied to the development and…
Descriptors: Learning Analytics, Innovation, School Holding Power, At Risk Students
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
Trezise, Kelly; Ryan, Tracii; de Barba, Paula; Kennedy, Gregor – Journal of Learning Analytics, 2019
Rural teachers and educators are increasingly called upon to build partnerships with families who use languages other than English in the home (US DOE, 2016). This is equally true for rural schools, where the number of multilingual families is small, and the language and cultural backgrounds of students differs from those of school. This article…
Descriptors: College Students, Cheating, Identification, Learning Analytics
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