Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

12-2021

Abstract

Analysing student resilience is important as research has shown that resilience is related to students’ academic performance and their persistence through academic setbacks. While questionnaires can be conducted to assess student resilience directly, they suffer from human recall errors and deliberate suppression of true responses. In this paper, we propose ACREA, ACademic REsilience Analytics framework which adopts a datadriven approach to analyse student resilient behavior with the use of student-course data. ACREA defines academic setbacks experienced by students and measures how well students overcome such setbacks using a quasi-experimental design. By applying ACREA on a real world student-course dataset, we analyse different types of effects on future term and course performance due to earlier setbacks. We found that setbacks in early academic term significantly affect more subsequent academic results. We also analyse the multiplier and redemption effects due to the resilience-driven behavior. The insights from the analysis contribute to a better understanding of student resilience using their performance after some academic setbacks. When the recovery of post-setback academic performance is not satisfactory, one can consider introducing new measures to strengthen student resilience. Students may also benefit from the findings when they can be better guided to recover from academic setbacks.

Keywords

data-driven framework, academic resilience, quasiexperimental design, pairwise analysis

Discipline

Databases and Information Systems

Publication

Proceedings of the IEEE International Conference on Engineering, Technology & Education (TALE) 2021

Embargo Period

1-6-2022

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