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A Multi-Outcome Hybrid Model for Predicting Student Success in Engineering

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Collection

2011 ASEE Annual Conference & Exposition

Location

Vancouver, BC

Publication Date

June 26, 2011

Start Date

June 26, 2011

End Date

June 29, 2011

ISSN

2153-5965

Conference Session

Persistence and Retention I

Tagged Division

Educational Research and Methods

Page Count

12

Page Numbers

22.70.1 - 22.70.12

Permanent URL

https://peer.asee.org/17352

Download Count

88

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Paper Authors

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Qu Jin Purdue University, West Lafayette

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Qu Jin is a graduate student in the School of Engineering Education at Purdue University. She received a M.S. degree in Biomedical Engineering from Purdue University and a B.S. degree in Material Science and Engineering from Tsinghua University in China. Her research focuses on modeling students’ outcomes, which include placement, retention, and graduation.

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P.K. Imbrie Texas A&M University

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Joe J.J. Lin Purdue University, West Lafayette

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Joe J.J. Lin is a Ph.D. student in the School of Engineering Education at Purdue University. His research interest includes: student success models in engineering, global engineering education, teamwork and team effectiveness, and production systems control and optimization. He worked as a production control engineer in Taiwan, and has taught laboratory classes in manufacturing engineering and freshmen engineering in the U.S. He earned his Bachelor and Master degrees in Industrial Engineering from National Tsing Hua University (Taiwan) and Purdue University (USA). His ultimate career goal is to help cultivate world-class engineering graduates that can compete globally, as well as collaborate with the best engineers across different cultures.

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Xingyu Chen Purdue University

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Abstract

A Mutli-Outcome Hybrid Model for Predicting Student Success in EngineeringMany statistical modeling methods have been studied in predicting success outcomes of collegestudents, such as linear regression, logistic regression, and discriminate analysis. These methodsare principally used to predict single outcomes (e.g. retention in engineering). Recently, neuralnetwork models have been developed to predict student’s outcomes and they are shown to havebetter prediction performances over more traditional methods (e.g., SEM, Logistic Regression).One unique feature of the neural networks is they can predict multiple outcomes in the samemodel.First year retention and GPA are two of the most widely used success outcomes of engineeringstudents. The important factors identified using a model with these two outcomes may betterexplain what success means to engineering students. The purpose of the study is to design neuralnetwork models predicting first year retention and GPA of engineering students in the samemodels, to evaluate the predicting performances of the multi-outcome models, and to comparethe important factors identified by the models with those identified by single-outcome models.Participants in the study included 1500 first year engineering students enrolled in a largeMidwestern university. Cognitive input variables included high school GPAs, standardized testscores, and the grades and number of semesters in math, science and English courses in highschool. Affective input variables were measured using the Student Attitudinal SuccessInstrument (SASI), which include: nine major factors: leadership, deep learning, surface learning,teamwork, academic self-efficacy, motivation, metacognition, expectancy-value, and majordecision.The research questions to be answered are: 1) To what extent do the multi-outcome models improve student success predictions? 2) How are the factors identified by the multi-outcome models for success compared with the factors identified by single-outcome models?To be presented are: 1) The prediction performances of the multi-outcome neural network models. 2) Comparison of the factors identified by the multi-outcome and single-outcome models.

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