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Autor/inn/enAguiar, Everaldo; Ambrose, G. Alex; Chawla, Nitesh V.; Goodrich, Victoria; Brockman, Jay
TitelEngagement vs Performance: Using Electronic Portfolios to Predict First Semester Engineering Student Persistence
QuelleIn: Journal of Learning Analytics, 1 (2014) 3, S.7-33 (27 Seiten)Infoseite zur Zeitschrift
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Spracheenglisch
Dokumenttypgedruckt; online; Zeitschriftenaufsatz
ISSN1929-7750
SchlagwörterAcademic Persistence; Engineering Education; Portfolios (Background Materials); Dropouts; Prediction; Data Analysis; Early Intervention; School Holding Power; Inferences; Correlation; Withdrawal (Education); Academic Achievement; College Freshmen; Educational Technology; Regression (Statistics); Indiana
AbstractAs 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 not only to understand the causes for this undesired outcome, but 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/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 used effectively to augment the quality of predictions. (As Provided).
AnmerkungenSociety for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: http://learning-analytics.info/journals/index.php/JLA/
Erfasst vonERIC (Education Resources Information Center), Washington, DC
Update2020/1/01
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