Engagement vs performance: using electronic portfolios to predict first semester engineering student retention

Authors:
Everaldo Aguiar;Nitesh V. Chawla;Jay Brockman;G. Alex Ambrose;Victoria Goodrich
Affiliations:
University of Notre Dame, Notre Dame, Indiana;University of Notre Dame, Notre Dame, Indiana;University of Notre Dame, Notre Dame, Indiana;University of Notre Dame, Notre Dame, Indiana;University of Notre Dame, Notre Dame, Indiana
Venue:
Proceedings of the Fourth International Conference on Learning Analytics And Knowledge
Year:
2014

Citing 12
Cited 0

C4.5: programs for machine learning

C4.5: programs for machine learning
Random Forests

Machine Learning
Induction of Decision Trees

Machine Learning
The relationship between Precision-Recall and ROC curves

ICML '06 Proceedings of the 23rd international conference on Machine learning
Investigating university students' adaptation to a digital learner course portfolio

Computers & Education
SMOTE: synthetic minority over-sampling technique

Journal of Artificial Intelligence Research
A comparative analysis of machine learning techniques for student retention management

Decision Support Systems
Hellinger distance decision trees are robust and skew-insensitive

Data Mining and Knowledge Discovery
Data mining for student retention management

Journal of Computing Sciences in Colleges
Learning patterns of university student retention

Expert Systems with Applications: An International Journal
Mining academic data to improve college student retention: an open source perspective

Proceedings of the 2nd International Conference on Learning Analytics and Knowledge
A critical assessment of imbalanced class distribution problem: The case of predicting freshmen student attrition

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

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.