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Sarah Blanchard Kyte; Celeste Atkins; Elizabeth Collins; Regina Deil-Amen – Journal of Postsecondary Student Success, 2023
Universities are increasingly turning toward data-driven technologies like data dashboards to support advisors' work in student success, yet little empirical work has explored whether these tools help or hinder best practices in advising, which is in many ways a relationship-based enterprise. This mixed-methods study analyzed whether and why the…
Descriptors: Learning Analytics, Computer Software, School Holding Power, Academic Persistence
Kay, Ellie; Bostock, Paul – Student Success, 2023
Providing timely nudges to students has been shown to improve engagement and persistence in tertiary education. However, many studies focus on small-scale pilots rather than institution-wide initiatives. This article assesses the impact of a pan-institution Early Alert System at the University of Canterbury that utilises nudging when students are…
Descriptors: At Risk Students, Learner Engagement, Undergraduate Students, Handheld Devices
Morenike Adebodun – ProQuest LLC, 2020
The purpose of this study was to examine the predictive power of Academic and Learning Analytics models on the persistence, retention, and graduation rates for students enrolled in higher education institutions in the United States. Specifically, this study is concerned with the relationships between the present usage of Academic and Learning…
Descriptors: Predictor Variables, Learning Analytics, Academic Achievement, Higher Education
Sanguino, Juan; Manrique, Rubén; Mariño, Olga; Linares-Vásquez, Mario; Cardozo, Nicolas – International Educational Data Mining Society, 2022
Recommender systems in educational contexts have proven effective to identify learning resources that fit the interests and needs of learners. Their usage has been of special interest in online self-learning scenarios to increase student retention and improve the learning experience. In current recommendation techniques, and in particular, in…
Descriptors: Data Analysis, Learning Analytics, Student Interests, Student Needs