Cited by 70Online Learning and AnalyticsEvasion and Academic Success FactorsHigher Education Research Studies

“Engagement vs. performance: using electronic portfolios to predict first semester engineering student retention,”

Aguiar, Everaldo, Chawla, Nitesh V., Brockman, Jay, Ambrose, G. Alex, Goodrich, Victoria·2014·Proceedings of the Fourth International Conference on Learning Analytics And Knowledge

Abstract

As providers of higher education begin to harness the power of big data analytics, one very fitting application for these new techniques is that of predicting student attrition. The ability to pinpoint students who might soon decide to drop out of a given academic program allows those in charge to not only understand the causes for this undesired outcome, but it also provides room for the development of early intervention systems. While making such inferences based on academic performance data alone is certainly possible, we claim that in many cases there is no substantial correlation between how well a student performs and his or her decision to withdraw. This is specially true when the overall set of students has a relatively similar academic performance. To address this issue, we derive measurements of engagement from students' electronic portfolios and show how these features can be effectively used to augment the quality of predictions.

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Crossref · 10.1145/2567574.2567583

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  • Researchers have explored features such as students' prior experiences, motivation and home environment, and student engagement with the course for attrition prediction.

    “To this end, researchers have explored features such as students' prior experiences, motivation and home environment (DeBoer et al., 2013) and student engagement with the course (Aguiar et al., 2014; Ramesh et al., 2014).”

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Authors
Aguiar, E., Chawla, N. V., Brockman, J., Ambrose, G. A., & Goodrich, V.
Affiliations
University of Notre Dame (US)
Venue
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Publisher
ACM
Published
2014-03-24
Pages
103-112
OpenAlex
W2046993599
Cited by
69
References
55
Citations by year
2026: 2 · 2025: 5 · 2024: 2 · 2023: 4 · 2022: 4 · 2021: 7 · 2020: 9 · 2019: 9 · 2018: 9 · 2017: 8 · 2016: 5 · 2015: 4 · 2014: 1
FWCI
3.81
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Social Sciences › Demography › Evasion and Academic Success Factors
  • Social Sciences › Education › Higher Education Research Studies
Keywords
student dropout prediction, student engagement, e-portfolio, academic performance, early intervention, higher education, big data analytics

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