Student’s Interests and Career Understanding: A Topic Analysis of First-year Career Courses

Authors

  • Tatsuya Tsumagari Seigakuin University
  • Nakazato Nakazato Kagoshima University
  • Takashi Tsumagari Prefectural University of Kumamoto

DOI:

https://doi.org/10.52731/lir.v001.013

Keywords:

first year experience, career education, actual state of learning, topic model

Abstract

This study conducted a topic analysis of the free-text reports submitted by the students to ex-amine the outcomes of their first-year career courses. There were two types of reports: 1) asking students why they were interested in a lecture, and 2) how they understood careers to be important as an outcome of the course.In the analysis, both reports were used as the data set, and LDA (Latent Dirichlet Allocation) was used to estimate the topic model, and Gibbs sampling was used to estimate the parameters. Students were classified into five types according to the lecturesthey were interested in. The analysis confirmed that for the 14 topics extracted, each student type had a unique topic that emerged as the reason for their interest. It was also found that many of the student types tended to have a long-term understandingof career as “their life itself.” The result of the analysis also found that there may be a reciprocal phenomenon regarding key topics in the students’ interest and career understanding.

References

M. Kikuchi, T. Suda, Y. Tange, and K. Murakami, “Students’ learning and Thought-inducing Factors analyzed form their comments on a Career Course,” [in Japanese], Jpn. Assoc. for College and University Education, vol. 41, no. 1, 2019, pp.117–156.

B. Grün and K. Hornik, “Topicmodels: An R Package for fitting Topic Models,” Journal of Statistical Software, vol. 40, 2011, pp.1–30.

https://taku910.github.io/mecab/

T. Tsumagari, Y. Nakazato, T. Tsumagari, “Analysis of the Actual State of Learning through Career Education as First-Year Experience Using a Topic Model,” 2021 10th Int’l Congress on Advanced Applied Informatics(IIAI-AAI), 2020, pp.938–939.

T. L. Griffiths and M. Steyvers, “Finding Scientific Topics,” Proc. the National Academy of Sciences of the United States of America, vol. 101(Suppl 1), 2004, 5228–35.

C. Jacobi, W. Atteveldt and K. Welbers, “Quantitative analysis of large amounts of journalistic texts using topic modelling,” Digital Journalism, vol. 4, 2015, pp.1–18.

M. Jin, “Basics and Practice of Text Analytics,” [in Japanese], Iwanami Shoten, 2021.

L. Sun and Y. Yin, “Discovering themes and trends in transportation research using topic modeling,” Transportation Research Part C: Emerging Technologies, vol. 77, 2017, pp.49–66.

Downloads

Published

2022-08-25