scholarly journals Big data-driven early alert systems as means of enhancing university student retention and success

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
N. Cele
Keyword(s):  
Big Data ◽  
Student Retention ◽  
University Student ◽  
Data Driven ◽  
Early Alert Systems ◽  
University Student Retention ◽  
Early Alert

Linking early alert systems and student retention: a survival analysis approach

2018 ◽  
Vol 76 (5) ◽  
pp. 903-920 ◽  
Author(s):  
Renato Villano ◽  
Scott Harrison ◽  
Grace Lynch ◽  
George Chen
Keyword(s):  
Survival Analysis ◽  
Student Retention ◽  
Analysis Approach ◽  
Early Alert Systems ◽  
Early Alert

Learning patterns of university student retention

2011 ◽  
Vol 38 (12) ◽  
pp. 14984-14996 ◽  
Author(s):  
Ashutosh Nandeshwar ◽  
Tim Menzies ◽  
Adam Nelson
Keyword(s):  
Student Retention ◽  
University Student ◽  
Learning Patterns ◽  
University Student Retention

scholarly journals Microeconometric Approaches in Exploring the Relationships Between Early Alert Systems and Student Retention: A Case Study of a Regionally Based University in Australia

2021 ◽  
pp. 1-17
Author(s):  
Scott Harrison ◽  
Renato Villano ◽  
Grace Lynch ◽  
George Chen
Keyword(s):  
Student Retention ◽  
Survival Models ◽  
Retention Data ◽  
Explanatory Variables ◽  
Retention Factors ◽  
Confounding Variables ◽  
Technological Tool ◽  
Early Alert Systems ◽  
Dynamic Relationships ◽  
Early Alert

Early alert systems (EAS) are an important technological tool to help manage and improve student retention. Data spanning 16,091 students over 156 weeks was collected from a regionally based university in Australia to explore various microeconometric approaches that establish links between EAS and student retention outcomes. Controlling for numerous confounding variables, significant relationships between the EAS and student retention were identified. Capturing dynamic relationships between the explanatory variables and the hazard of discontinuing provides new insight into understanding student retention factors. We concluded that survival models are the best methods of understanding student retention when temporal data is available.

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