Engagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence

Everaldo Aguiar
G. Alex Ambrose Ambrose
Nitesh V. Chawla
Victoria Goodrich
Jay Brockman


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, or who may be following a suboptimal path to success, 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 especially 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.


Electronic Portfolios; Student Retention; Early Intervention; Data Fusion; Learning Analytics; Predictive Analytics

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DOI: http://dx.doi.org/10.18608/jla.2014.13.3


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