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Board 34: Use of Big Data Analytics in a First-year Engineering Project

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

NSF Grantees Poster Session

Tagged Topics

Diversity and NSF Grantees Poster Session

Page Count

15

DOI

10.18260/1-2--32328

Permanent URL

https://peer.asee.org/32328

Download Count

181

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Paper Authors

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Kevin D. Dahm Rowan University

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Kevin Dahm is a Professor of Chemical Engineering at Rowan University. He earned his BS from Worcester Polytechnic Institute (92) and his PhD from Massachusetts Institute of Technology (98). He has published two books, "Fundamentals of Chemical Engineering Thermodynamics" and "Interpreting Diffuse Reflectance and Transmittance." He has also published papers on effective use of simulation in engineering, teaching design and engineering economics, and assessment of student learning.

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biography

Nidhal Carla Bouaynaya Rowan University

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Nidhal Bouaynaya received the B.S. degree in Electrical Engineering and Computer Science from
the Ecole Nationale Superieure de L’Electronique et de ses Applications (ENSEA), France, in 2002, the
MS degree in Electrical and Computer Engineering from the Illinois Institute of Technology, Chicago, in
2002, the Diplome d’Etudes Approfondies in Signal and Image processing from ENSEA, France, in
2003, the M.S. degree in Mathematics and the Ph.D. degree in Electrical and Computer Engineering from
the University of Illinois at Chicago, in 2007. From 2007-2013, she was an Assistant then Associate Professor with the Department of Systems Engineering at the University of Arkansas at Little Rock. Since 2013, she joined Rowan University, where she is currently an Associate Professor with the Department of Electrical and Computer Engineering. Dr. Bouaynaya won the Best Student Paper Award in Visual Communication
and Image Processing 2006, the Best Paper Award at the IEEE International Workshop on Genomic Signal Processing and Statistics 2013 and the runner-up Best Paper Award at the IEEE International Conference on Bioinformatics and Biomedicine 2015. She is also one of the winners of the Brain Tumor Image Segmentation (BRATS) Challenge 2016. Her current research interests are in medical imaging, machine learning, mathematical biology and dynamical systems.

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Ravi P. Ramachandran Rowan University

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Ravi P. Ramachandran received the B. Eng degree (with great distinction) from Concordia University in 1984, the M. Eng degree from McGill University in 1986 and the Ph.D. degree from McGill University in 1990. From October 1990 to December 1992, he worked at the Speech Research Department at AT&T Bell Laboratories. From January 1993 to August 1997, he was a Research Assistant Professor at Rutgers University. He was also a Senior Speech Scientist at T-Netix from July 1996 to August 1997. Since September 1997, he is with the Department of Electrical and Computer Engineering at Rowan University where he has been a Professor since September 2006. He has served as a consultant to T-Netix, Avenir Inc., Motorola and Focalcool. From September 2002 to September 2005, he was an Associate Editor for the IEEE Transactions on Speech and Audio Processing and was on the Speech Technical Committee for the IEEE Signal Processing society. Since September 2000, he has been on the Editorial Board of the IEEE Circuits and Systems Magazine. Since May 2002, he has been on the Digital Signal Processing Technical Committee for the IEEE Circuits and Systems society. His research interests are in digital signal processing, speech processing, biometrics, pattern recognition and filter design.

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Abstract

This paper will describe a project on big data analytics that was developed and introduced into Freshman Engineering Clinic, which is an introductory course for students in all engineering disciplines. Among the learning objectives for the Freshman Engineering Clinic are developing skills in data collection, analyzing data to draw sound conclusions, and writing, with visual/graphical representation of information being recognized as a critical component of effective technical writing. The NSF has awarded a grant to the authors’ University (name withheld for blind review) to support vertical integration of big data analytics throughout the engineering curriculum. This paper focuses on the Freshman Clinic project, the intent of which is to introduce students to big data analytics while also furthering the general instructional objectives of the freshman course.

The project was titled “Introduction to Big Data Analytics: Analyzing Tweets with Matlab”. The instructor provided the students with Matlab code that was designed to facilitate applying Sentiment Analysis to tweets. For example, the code can be used to (1) identify tweets that contain one or more specific keywords and (2) create a histogram of words used in these tweets, in order to identify recurring themes in tweets that mention the keyword(s). The final deliverable for the project was a report in which students detailed how they used the Matlab code to answer a number of open-ended questions, as well as an introductory section in which students discussed the importance and applications of big data analytics in general. The paper will provide a detailed description of the project and the deliverable. For students whose sections of Freshman Clinic used the big data project, a 10-question quiz was administered at both the beginning and the end of the semester in order to assess whether students were more knowledgeable about big data analytics at the end of the project than they were prior to the start of the project. For these students there was a statistically significant improvement in the post-test results compared to the pre-test. The same quiz was also administered at the end of the course to “control” sections of Freshman Engineering Clinic; students who completed a different project unrelated to big data. The control group’s average quiz scores were lower at the end of the semester than the target group’s average scores at the beginning of the semester. This result implies that the improvement seen throughout the semester is indeed attributable to the big data project itself rather than to the Freshman Clinic experience in general. The paper will also provide the full assessment results.

Dahm, K. D., & Bouaynaya, N. C., & Ramachandran, R. P. (2019, June), Board 34: Use of Big Data Analytics in a First-year Engineering Project Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32328

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