June 15, 2019
June 15, 2019
June 19, 2019
Computers in Education
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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