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Direct linkRobert J. Summers; Adrian P. Burgess; Helen E. Higson; Elisabeth Moores – Studies in Higher Education, 2024
To explore potential effects of disadvantage on engagement and attainment under different teaching and assessment regimes, the influence of pedagogic changes implemented during the COVID-19 pandemic on attainment and engagement of students from different backgrounds were compared using a cohort-study design. Learner analytics and attainment data…
Descriptors: Foreign Countries, Learning Analytics, Disadvantaged, College Freshmen
Download full textYu, Renzhe; Li, Qiujie; Fischer, Christian; Doroudi, Shayan; Xu, Di – International Educational Data Mining Society, 2020
In higher education, predictive analytics can provide actionable insights to diverse stakeholders such as administrators, instructors, and students. Separate feature sets are typically used for different prediction tasks, e.g., student activity logs for predicting in-course performance and registrar data for predicting long-term college success.…
Descriptors: Prediction, Accuracy, College Students, Success
Summers, Robert; Higson, Helen; Moores, Elisabeth – Assessment & Evaluation in Higher Education, 2023
The pandemic forced many education providers to pivot rapidly their models of education to increased online provision, raising concerns that this may accentuate effects of digital poverty on education. Digital footprints created by learning analytics systems contain a wealth of information about student engagement. Combining these data with…
Descriptors: Disadvantaged, Learner Engagement, COVID-19, Pandemics
Foster, Ed; Siddle, Rebecca – Assessment & Evaluation in Higher Education, 2020
In this article we investigate the effectiveness of learning analytics for identifying at-risk students in higher education institutions using data output from an in-situ learning analytics platform. Amongst other things, the platform generates 'no-engagement' alerts if students have not engaged with any of the data sources measured for 14…
Descriptors: Learning Analytics, At Risk Students, Identification, Higher Education
Pennacchia, Jodie; Bathmaker, Ann-Marie – British Educational Research Journal, 2021
The English further education (FE) sector caters for young learners who are regularly defined as at risk due to a range of economic and social challenges, as transitions from youth to adulthood become more protracted, and inequalities amongst young people and between generations persist and deepen. At a time when policy places increasing…
Descriptors: Governing Boards, Social Justice, Cost Effectiveness, Foreign Countries
Grimaldi, Phillip; Weatherholtz, Kodi; Hill, Kelli Millwood – International Educational Data Mining Society, 2022
As educational technology platforms become more and more commonplace in education, it is critical that these systems work well across a diverse range of student sub-groups. In this study, we estimated the effectiveness of MAP Accelerator; a large-scale, personalized, web-based, mathematics mastery learning platform. Our analysis placed a…
Descriptors: Educational Technology, Mastery Learning, Learning Management Systems, Middle School Students
