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Radical Solutions and Learning AnalyticsPreventing Student and Faculty Attrition in Times of Change

Radical Solutions and Learning Analytics: Preventing Student and Faculty Attrition in Times of... [A good university experience can help motivate and retain students and faculty. Universities place a strong focus on the student population to reduce attrition levels. Models of attrition and their applications are becoming important and useful given the greater amounts of data available to institutions. Yet, these data focus on transactional events rather than the learning journey. In contrast, there seems to be less emphasis on faculty attrition, and data surrounding this phenomenon is lacking. Here, I argue that teaching and data associated with teaching can help promote retention of both students and academics. To capture the effects of teaching (positive or negative) on faculty, we need better data to understand the dynamics behind teaching load and delivery. I provide a model that can help initiate hypothesis testing and data gathering. This model focuses on minimal amounts of data to understand cohorts (student and academic) at different stages of their journey. I provide a framework to produce positive feedback loops that would benefit learners and scholars alike.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Radical Solutions and Learning AnalyticsPreventing Student and Faculty Attrition in Times of Change

Part of the Lecture Notes in Educational Technology Book Series
Editors: Burgos, Daniel
Munguia, Pablo
Radical Solutions and Learning Analytics — May 9, 2020
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References (23)

Publisher
Springer Singapore
Copyright
© Springer Nature Singapore Pte Ltd. 2020
ISBN
978-981-15-4525-2
Pages
115 –129
DOI
10.1007/978-981-15-4526-9_8
Publisher site
See Chapter on Publisher Site

Abstract

[A good university experience can help motivate and retain students and faculty. Universities place a strong focus on the student population to reduce attrition levels. Models of attrition and their applications are becoming important and useful given the greater amounts of data available to institutions. Yet, these data focus on transactional events rather than the learning journey. In contrast, there seems to be less emphasis on faculty attrition, and data surrounding this phenomenon is lacking. Here, I argue that teaching and data associated with teaching can help promote retention of both students and academics. To capture the effects of teaching (positive or negative) on faculty, we need better data to understand the dynamics behind teaching load and delivery. I provide a model that can help initiate hypothesis testing and data gathering. This model focuses on minimal amounts of data to understand cohorts (student and academic) at different stages of their journey. I provide a framework to produce positive feedback loops that would benefit learners and scholars alike.]

Published: May 9, 2020

Keywords: Learning analytics; Teacher data; Student data; Attrition; University experience

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