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Demonstrating Use of Natural Language Processing to Compare College of Engineering Mission Statements

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Curricular Issues in Computing

Tagged Division

Computing & Information Technology

Page Count

14

DOI

10.18260/1-2--28102

Permanent URL

https://peer.asee.org/28102

Download Count

231

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Paper Authors

biography

Sreyoshi Bhaduri Virginia Tech

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Sreyoshi Bhaduri is a PhD candidate at Virginia Tech Department of Engineering Education. She is a proponent for use of technology in the classroom as well as education research. Sreyoshi is a Mechanical Engineer by training, who likes programming to “make life easier and efficient”. For her doctoral dissertation, she is exploring ways in which machine learning algorithms can be used by instructors in engineering classrooms.

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biography

Tamoghna Roy Virginia Tech

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Tamoghna Roy is a PhD candidate in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research interests include statistical signal processing and applied machine learning. Tamoghna is currently working with the Hume Center for National Security and Technology on application of Deep Learning to Wireless Communication.

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Abstract

Most higher education institutions have a mission and/or vision statement that is designed to communicate with a variety of audiences. These statements are developed strategically by organizations and often reflect the college’s unique vision which sets it apart from peer institutions. Analytical techniques which rely on word usage, semantic information, and metadata information can be used to generate powerful descriptive models with allow us to obtain relevant information from text-based data. This work-in-progress study presents a Natural Language Processing based textual data analytical approach to study the mission and vision statements with the purpose of understanding the key similarities and differences between the choice of words used in them. We analyzed a total of 59 engineering colleges: 29 public, and 30 private, across the United States. Results of this study indicate that there is indeed a difference in word frequencies for public versus private engineering colleges. The contribution of this research is in the form of charts quantitatively summarizing the comparative word usage and a descriptive overview of the complete vocabulary of pertinent words from the statements analyzed. Topical clustering based on words seen in prior literature was also conducted to analyze comparative categories across the institutions. This study can help inform strategies on the formation of mission and vision statements for universities by allowing administrators insight into vocabulary used across colleges.

Bhaduri, S., & Roy, T. (2017, June), Demonstrating Use of Natural Language Processing to Compare College of Engineering Mission Statements Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28102

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