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Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students

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Conference

2017 Mid-Atlantic Section Fall Conference

Location

Penn State University - Berks Campus - Reading, Pennsylvania

Publication Date

October 6, 2017

Start Date

October 6, 2017

End Date

October 7, 2017

Conference Session

Mid Atlantic Papers

Tagged Topic

Mid-Atlantic Section Fall Conference

Page Count

7

Permanent URL

https://peer.asee.org/29384

Download Count

46

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Paper Authors

biography

Tak Cheung

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Tak Cheung, Ph.D., professor of physics, teaches in CUNY Queensborough Community College. He also conducts research and mentors student research projects.

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biography

sunil Dehipawala Queensborough Community College

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Sunil Dehipawala received his B.S. degree from University of Peradeniya in Sri Lanka and Ph.D from City University of New York. Currently, he is working as a faculty member at Queensborough Community College of CUNY.

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biography

Rex Taibu

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Dr. Rex Taibu has taught studio physics classes for several years. His teaching experience has shaped his research focus. Currently, Dr. Taibu is actively engaged in

1) promoting scientific inquiry attitudes in students through designing, implementing, and assessing innovative inquiry based physics labs.

2) conducting research regarding the role of language in conceptual understanding.

3) exploring cosmic rays (detection, data collection, and analysis).

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biography

Vazgen Shekoyan

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Dr. Vazgen Shekoyan is a professor of physics and his experiences include pedagogy, CubeSat, etc.

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Abstract

The latent variable modeling technique in education assessment is illustrated using problem solving examples in first semester physics for engineering and technology students. The regression modeling of data in the Newton second law laboratory class has equivalence to an analysis with the internal force serving as the latent variable. A path diagram would contain the internal force as a latent variable which manifest its influence on the observed datasets, namely, acceleration and force. Similarly, the pre-score and post-score data collected for education assessment can be studied with latent variable modeling to broaden the scope beyond regression which requires an independent variable. The numerical and flexibility advantages of latent variable modeling versus regression modeling in education assessment are presented. Free LISREL student version working details and free confirmatory factor analysis Microsoft Excel software from university faculty are presented. The adaptation of Queensborough Community College Physics faculty education research into Queensborough NSF-REU projects for engineering students is discussed.

Cheung, T., & Dehipawala, S., & Taibu, R., & Shekoyan, V. (2017, October), Latent variable modeling with applications to education assessment and NSF-REU projects for engineering students Paper presented at 2017 Mid-Atlantic Section Fall Conference, Penn State University - Berks Campus - Reading, Pennsylvania. https://peer.asee.org/29384

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