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Using Natural Language Processing Tools on Individual Stories from First-year Students to Summarize Emotions, Sentiments, and Concerns of Transition from High School to College

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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

Technical Session 4: Modulus Topics 1

Tagged Division

Computers in Education

Tagged Topic

Diversity

Page Count

14

Permanent URL

https://peer.asee.org/31917

Download Count

10

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Paper Authors

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Ashwin Satyanarayana New York City College of Technology

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Dr. Ashwin Satyanarayana is currently an Associate Professor with the Department of Computer Systems Technology, New York City College of Technology (CUNY). Prior to this, Dr. Satyanarayana was a Research Scientist at Microsoft in Seattle from 2006 to 2012, where he worked on several Big Data problems including Query Reformulation on Microsoft’s search engine Bing. He holds a PhD in Computer Science from SUNY, with particular emphasis on Data Mining and Big data analytics. He is an author or co-author of over 25 peer reviewed journal and conference publications and co-authored a textbook – “Essential Aspects of Physical Design and Implementation of Relational Databases.” He has four patents in the area of Search Engine research. He is also a recipient of the Math Olympiad Award, and is currently serving as Chair Elect of the ASEE (American Society of Engineering Education) Mid-Atlantic Conference. He also serves as an NSF (National Science Foundation) panelist.

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Karen Goodlad New York City College of Technology, CUNY

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Karen Goodlad is an Assistant Professor specializing in food and beverage management at New York City College of Technology, CUNY. In addition to her joy of teaching she is often found advising students.
Finding it important to continually increase her understanding of the teaching and learning practices that help students develop a joy for learning, she works as the faculty co-coordinator of the college's first-year learning communities. Here she facilitates seminars for full and part-time faculty focused on incorporating the high-impact educational practice of learning communicates into the course work.

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Jennifer Sears New York City College of Technology, CUNY

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Jennifer Sears is Assistant Professor in the English Department at the New York City College of Technology (CUNY) in Brooklyn, NY.

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Philip Kreniske Columbia University, The HIV Center

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Dr. Kreniske examines the promise and perils of digital technology with a focus on adolescence and young adulthood. In the U.S. Kreniske has used mixed methods to examine the role of innovative technologies in the transition to college. In Kreniske's early work in sub-Saharan Africa he showed how digital technology could be leveraged as a tool to defend human rights and create culturally relevant educational materials. Recently, Kreniske has examined how cell phone technology can be used to disseminate health information to hard to reach youth with implications for HIV in rural Uganda. Kreniske is currently a Postdoctoral Research Fellow at the HIV Center for Clinical and Behavioral Studies at the New York State Psychiatric Institute and Columbia University.

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Mery F. Diaz New York City College of Technology

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Mery Diaz is Assistant Professor in the Human Services Department at New York City College of Technology/CUNY (City Tech). She holds a doctorate in clinical social work from the School of Policy and Practice at the University of Pennsylvania. Dr. Diaz practiced as a licensed clinical social worker with children, adolescents, and families in a variety of mental health settings, non-profit organizations, and was a consultant in high-need high-poverty New York City public schools where she supported the development of integrated mental health systems. She is currently the faculty liaison for CREAR Futuros at City Tech, a peer-mentoring program supporting first-year undergraduate Latino students, and has been a long-time faculty participant in the First-Year Learning Communities program. Her research interests include mental health and social justice issues in education. She has published in several peer-reviewed research journals and is co-editor of the forthcoming Narrating Practice with Children and Adolescents with Columbia University Press (2019).

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Sandra Cheng New York City College of Technology

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Sandra Cheng is an Associate Professor of art history at New York City College of Technology, City University of New York. She has served as a Faculty Leader for City Tech’s First Year Learning Community (FYLC) program, and she continues to teach FYLC courses.

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Abstract

Research indicates striking disparities in college completion rates between students who are first-generation and come from low-income households (FLI) as compared to continuing generation students. At New York City College of Technology, CUNY (City Tech) the majority of the student body are FLI. In the last decade, educators have made great efforts to re-shape and improve students’ First-year college experience with a focus on FLI students. One of the ten high-impact educational practices recognized nationally to improve first year student persistence and retention is First-Year Learning Communities (LC). A LC is a group of students who enroll in two or more courses, generally in different disciplines that are linked together by a common theme, in an academic semester. LCs involve cooperative learning, alternative assessment in the classroom, cross-disciplinary writing assignments, and critical thinking activities. LCs first came to our institution, City Tech, through a Title V Grant in 2000 and were adopted by the college in 2005. The academic performance of students participating in LCs at City Tech reflects national trends. When compared to the general population at the College, students in LC earn higher GPAs, have higher retention rates, and demonstrate greater satisfaction.

In order to complement the community-building efforts within learning community classrooms, we, a cohort of faculty leaders and administrators of City Tech’s First Year Learning Communities, a program offered through the college’s Office of First Year Programs, developed “Our Stories” digital writing project which extends the student’s network beyond the physical and temporal limits of class meeting times. Students in our LC were given the opportunity to share their personal stories of the transition from high school to college on a digital platform called OpenLab, a campus-wide, open digital WordPress platform for teaching, learning, and sharing. Over the course of a semester, LC students were prompted with the same prompt three times, at the beginning of the semester, roughly in the middle of the term, and in the last weeks. Peer Mentors, upper level students who, among other responsibilities, were trained to respond to “Our Stories” posts actively engaged in the project.

We analyzed student stories, using text analytics tools such as Natural Language processing (NLP) and Tone Analyzer to better understand the transition experience. The NLP analyzer helped summarize emotions and concepts, and identified some common concerns of students by identifying common keywords. The Tone Analyzer tool uses linguistic analysis to detect joy, fear, sadness, anger, analytical, confident and tentative tones found in text. Such summarizations of student stories provide suggestions to the college on how we can better orient students and prepare them for their first year. In this paper, we present top concerns of students who are transitioning from high school to college. We will also investigate through the stories if the overall experience of students gets better or worse through their first year.

Satyanarayana, A., & Goodlad, K., & Sears, J., & Kreniske, P., & Diaz, M. F., & Cheng, S. (2019, June), Using Natural Language Processing Tools on Individual Stories from First-year Students to Summarize Emotions, Sentiments, and Concerns of Transition from High School to College Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. https://peer.asee.org/31917

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