Journal of Research in Science, Mathematics and Technology Education

An Empirical Study of Career Talk Incorporation in Science, Technology, Engineering, and Mathematics Education

Journal of Research in Science, Mathematics and Technology Education, Volume 9, Issue 2, May 2026, pp. 15-37
OPEN ACCESS VIEWS: 29 DOWNLOADS: 19 Publication date: 15 May 2026
ABSTRACT
Guest speaker presentation is an important strategy for expanding career pathways in Science, Technology, Engineering, and Mathematics (STEM) education, particularly for underrepresented students at Hispanic-Serving Institutions (HSIs).  With support from a U.S. Department of Education grant, this study was designed to examine the merits of career talks by STEM professionals at California State University, Bakersfield.   Text analytics were conducted to extract the key features of guest speaker presentations, and survey data were gathered to assess the clarity, informativeness, and relevancy of career talks for student engagement.  The incorporation of qualitative and quantitative approaches is guided by Social Cognitive Career Theory (SCCT) to reconfirm the guest speaker's impact on student learning for career pathway expansion.  The results demonstrate efforts to strengthen STEM career preparation at this Hispanic-Serving Institution.
KEYWORDS
Career Talk, STEM Education, Mixed Methods.
CITATION (APA)
Wang, J., & Lam, C. (2026). An Empirical Study of Career Talk Incorporation in Science, Technology, Engineering, and Mathematics Education. Journal of Research in Science, Mathematics and Technology Education, 9(2), 15-37. https://doi.org/10.31756/jrsmte.922
REFERENCES
  1. References
  2. Al-Qudah, S., Davishahl, J., Davishahl, E., & Greiner, M. A. (2018, June). Investigation of sense of belonging to engineering in undergraduate introductory classes. In 2018 ASEE Annual Conference & Exposition Proceedings, Salt Lake City, Utah, doi: 10.18360/1-2—20730.
  3. Amin, M., Shah, B., Sharif, A., Ali, T., Kim, K. I., & Anwar, S. (2022). Android malware detection through generative adversarial networks. Transactions on Emerging Telecommunications Technologies, 33(2), e3675. https://doi.org/10.18360/1-2---20730
  4. Baker, R. S. J. D., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. Journal of Educational Data Mining, 1(1), 3-17.
  5. Barba, P. (2020). Machine Learning (ML) for Natural Language Processing (NLP). Amherst, MA: Lexalytics.
  6. Benoit, K., Watanabe, K., Wang, H., Nulty, P., Obeng, A., Müller, S., & Matsuo, A. (2018). quanteda: An R package for the quantitative analysis of textual data. Journal of Open Source Software, 3, 774.
  7. Brief, T. P. (2023). Career talks with guest speakers: A guide to delivering an effective career development activity. OECD Publishing. https://www.oecd-ilibrary.org/education/career-talks-with-guest-speakers_93594cb3-en
  8. Byars-Winston, A., Leverett, P., & Benbow, R. J. (2015). Career counseling for women of color. In W. Patton & M. McMahon (Eds.), Career development and systems theory: Connecting theory and practice (3rd ed., pp. 271-303). Sense Publishers.
  9. Chandrasekar, A., Clark, S. E., Martin, S., Vanderslott, S., Flores, E. C., Aceituno, D., Barnett, P., Vindrola-Padros, C., & Vera San Juan, N. (2024). Making the most of big qualitative datasets: A living systematic review of analysis methods. Frontiers in Big Data, 7. https://doi.org/10.3389/fdata.2024.1455399
  10. Crisp, G., & Cruz, I. (2009). Mentoring college students: A critical review of the literature between 1990 and 2007. Research in Higher Education, 50(6), 525–545.
  11. CSUB Auxiliary (2021). An equitable pathway to in-demand STEM careers (Funding No. P031C210093). U.S. Department of Education. https://www.ed.gov/sites/ed/files/programs/hsistem/2021hsistemfundedabstracts508compliant.pdf
  12. Culpeper, J. (2009). Keyness: Words, parts-of-speech and semantic categories in the character-talk of Shakespeare’s Romeo and Juliet. International Journal of Corpus Linguistics, 14(1), 29-59.
  13. Dake, D. K., & Gyimah, E. (2023). Using sentiment analysis to evaluate qualitative students’ responses. Education and Information Technologies, 28(4), 4629-4647.
  14. Dost, G. (2024). ‘STEM belonging’: the association between stereotype vulnerability, COVID-19 stress, general self-efficacy, multidimensional perceived social support, and STEM interest among Physics, Chemistry, and Mathematical Science students. International Journal of Adolescence and Youth, 29(1). Article 2394209.
  15. Đurović, Z. (2023). Frequency or Keyness?. Lexikos, 33(1), 184-206.
  16. Elmore, B. (2015). Tell me, show me & involve me. Baylor Business Review, 33(2), 46-49.
  17. Eugene, R., & Potter, K. (2023). Developing and assessing metrics to evaluate the quality of generated questions from data. https://www.researchgate.net/publication/377271595_Title_Developing_and_Assessing_Metrics_to_Evaluate_the_Quality_of_Generated_Questions_from_Data
  18. Excelencia in Education (2024). All HSIs resources. https://www.edexcelencia.org/research-policy/hispanic-serving-institutions-hsis
  19. Falk, J. H., & Dierking, L.D. (2016). The museum experience revisited. Routledge.
  20. Ferreira‐Mello, R., André, M., Pinheiro, A., Costa, E., & Romero, C. (2019). Text mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(6). Article e1332.
  21. Garcia, G. A., Núñez, A. M., & Sansone, V. A. (2019). Toward a multidimensional conceptual framework for understanding “servingness” in Hispanic-Serving Institutions. Review of Higher Education, 42(3), 117–148.
  22. Huang, C., Yang, C., Wang, S., Wu, W., Su, J., & Liang, C. (2020). Evolution of topics in education research: A systematic review using bibliometric analysis. Educational Review, 72(3), 281-297.
  23. Hunter, A. B. (2019). Why undergraduates leave SMT majors. https://web.archive.org/web/20220422212134id\_/https://casa.colorado.edu/\~dduncan/wp-content/uploads/2019_Book_TalkingAboutLeavingRevisited.pdf#page=106
  24. Hutchinson, J. (2019). Australian and Canadian far-right extremism: A cross-national comparative analysis of social media mobilisation on Facebook. [Doctoral dissertation, Macquarie University]. Macquarie University Research Repository. https://figshare.mq.edu.au/articles/thesis/Australian_and_Canadian_far-right_extremism_a_cross-national_comparative_analysis_of_social_media_mobilisation_on_Facebook/19428185/1/files/34520042.pdf
  25. Katre, P. D. (2019). NLP based text analytics and visualization of political speeches. International Journal of Recent Technology and Engineering, 8(3), 8574-8579.
  26. Koch, S., Wessels, M., Altpeter, B., Olvermann, M., & Johns, M. (2022). Keeping privacy labels honest. Proceedings on Privacy Enhancing Technologies, 4(2), 486-506.
  27. Kostelej, M., & Bagić Babac, M. (2022). Text analysis of the Harry Potter book series. South Eastern European Journal of Communication, 4(1), 17-30.
  28. Lent, R., Brown, S., & Hackett, G. (1994). Toward a unifying social cognitive theory of career and academic interest, choice, and performance. Journal of Vocational Behavior, 45, 79-122.
  29. Lent, R. W., Ezeofor, I., Morrison, M. A., Penn, L. T., & Ireland, G. W. (2016). Applying the social cognitive model of career self-management to career exploration and decision-making. Journal of Vocational Behavior, 93, 47-57.
  30. Mostafa, M. M., Feizollah, A., & Anuar, N. B. (2023). Fifteen years of YouTube scholarly research: Knowledge structure, collaborative networks, and trending topics. Multimedia Tools and Applications, 82(8), 12423-12443.
  31. National Science Foundation. (2017). Women, minorities, and persons with disabilities in science and engineering. https://sites.nationalacademies.org/cs/groups/pgasite/documents/webpage/pga_178246.pdf
  32. Packard, B. W. L. (2016). Successful STEM mentoring initiatives for underrepresented students: A research-based guide for faculty and administrators. Stylus Publishing.
  33. Periathiruvadi, S. (2013). Investigating the relationship between internet attitudes of college students and their SMT career perceptions. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=fc18f60410b7c5827c63d4ab51720d3c529b9920
  34. Ranjan, B., & Mishra, B. K. (2022). Overview of big data and natural language processing: A powerful combination for research. In Handbook of research for big data (pp. 113-135). Apple Academic Press.
  35. Rao, P., & Taboada, M. (2021). Gender bias in the news: A scalable topic modelling and visualization framework. Frontiers in Artificial Intelligence – Language and Computation, 4(82). https://doi.org/10.3389/frai.2021.664737
  36. Robinson, C. (2018). Guest speakers and mentors for career exploration in the science classroom. Science Scope, 41, 18-21.
  37. Sarkar, D. (2019). Text analytics with Python: A practitioner's guide to natural language processing. Springer.
  38. Shulman, J. (2022). Math identity experience of college students in developmental courses. [Doctoral dissertation, University of Nevada, Las Vegas]. UNLV Theses, Dissertations, Professional Papers, and Capstones. https://digitalscholarship.unlv.edu/cgi/viewcontent.cgi?article=5477&context=thesesdissertations
  39. Stryker, C., & Holdsworth, J. (2024). What is NLP (natural language processing). IBM. https://www.ibm.com/think/topics/natural-language-processing
  40. Svartzman, G. G., Ramirez-Marquez, J. E., & Barker, K. (2020). Social media analytics to connect system performability and quality of experience, with an application to Citibike. Computers & Industrial Engineering, 139. Article 106146.
  41. Syed, M., Azmitia, M., & Cooper, C. R. (2011). Identity and academic success among underrepresented college students: An interdisciplinary review and integration. Journal of Social Issues, 67(3), 442–468.
  42. Tufte, E. R., & Tufte, E. R. (2006). Beautiful evidence (Vol. 1). Graphics Press.
  43. Upadhye, A. (2020). A comprehensive survey of text data cleaning techniques: Challenges, methods, and best practices. Journal of Scientific and Engineering Research, 7(8), 205-210. https://jsaer.com/download/vol-7-iss-8-2020/JSAER2020-7-8-205-210.pdf
  44. Wang, Y., Wang, J. T., Raymond, F. T., & Wang, J. T. (2023). Elementary pre-service teachers’ horizon knowledge for teaching addition and subtraction: An analysis of video presentations. EURASIA Journal of Mathematics, Science and Technology Education, 19(6). Article em2276. https://www.ejmste.com/download/elementary-pre-service-teachers-horizon-knowledge-for-teaching-addition-and-subtraction-an-analysis-13202.pdf
  45. Weinberg, J. (2021). Who’s listening to whom? The UK house of lords and evidence-based policy-making on citizenship education. Journal of Education Policy, 36(4), 576-599.
  46. Yang, J., Kinshuk, & An, Y. (2023). A survey of the literature: How scholars use text mining in Educational Studies?. Education and Information Technologies, 28(2), 2071-2090.
  47. Zainudin, Z. N., Rong, L. W., Nor, A. M., Yusop, Y. M., & Othman, W. N. W. (2020). The relationship of holland theory in career decision making: A systematic review of literature. Journal of Critical Reviews, 7(9), 884-892.
  48. Zirkel, S. (2002). Is there a place for me? Role models and academic identity among White students and students of color. Teachers College Record, 104(2), 357–376.
LICENSE
Creative Commons License