Skip to main content
SpringerLink
Log in

Using Institutional data and messages on Social Media to Predict the Career decisions of University Students - A Data-Driven Approach

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Enabling college graduates to achieve career success is increasingly considered a major responsibility of universities. Many studies have developed models of predicting students’ career decisions and have sought to provide appropriate treatments or early support for students to achieve this goal. Most studies, however, have focused on using institutional data, which might not be entirely sufficient for the prediction because students’ career decisions might also be affected by the social context. This study proposes a data-driven approach that considers both institutional data and social media news for predicting students’ career decisions. The results of this study suggest that such an approach achieved a higher performance in the prediction task. This study also discusses the data-driven approach as a means of supporting students’ career development in the university setting, how the approach can be used to inform educators on how to use the data that are both internal and external to the university, and what the impact of this approach is on educational support decisions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  • Akosah-Twumasi, P., Emeto, T. I., Lindsay, D., Tsey, K., & Malau-Aduli, B. S. (2018). A systematic review of factors that influence youths career choices—the role of culture. Frontiers, 3(58), https://doi.org/10.3389​/feduc.2018.00058

  • Alvarez, N. L., Callejas, Z., & Griol, D. (2020). Predicting Computer Engineering students’ dropout in Cuban Higher Education with pre-enrollment and early performance data. JOTSE, 10(2), 241–258

    Article  Google Scholar 

  • Alyahyan, E., & Düştegör, D. (2020). Predicting academic success in higher education: literature review and best practices. International Journal of Educational Technology in Higher Education, 17(1), 3. https://doi.org/10.1186/s41239-020-0177-7

    Article  Google Scholar 

  • Arnold, W. W. (2018). Strengthening college support services to improve student transitioning to careers. Journal of College Teaching & Learning (TLC), 15(1), 5–26

    Article  Google Scholar 

  • Atitsogbe, K. A., Mama, N. P., Sovet, L., Pari, P., & Rossier, J. (2019). Perceived employability and entrepreneurial intentions across university students and job seekers in Togo: The effect of career adaptability and self-efficacy. Frontiers in psychology, 10, 180. https://doi.org/10.3389/fpsyg.2019.00180

    Article  Google Scholar 

  • Avargil, S., Kohen, Z., & Dori, Y. J. (2020). Trends and perceptions of choosing chemistry as a major and a career. Chemistry Education Research and Practice, 21(2), 668–684

    Article  Google Scholar 

  • Basit, A., Rehman, A., & Gulshan, M. T. (2020). The Impacts of Media: A case study of Television news on the Youths of Mardan Area. Global Media and Social Sciences Research Journal (GMSSRJ), 1(2), 17–24

    Google Scholar 

  • Bathmaker, A. M. (2021). Constructing a graduate career future: Working with Bourdieu to understand transitions from university to employment for students from working-class backgrounds in England. European Journal of Education, 56(1), 78–92

    Article  Google Scholar 

  • Batistic, S., & Tymon, A. (2017). Networking behaviour, graduate employability: A social capital perspective. Education + Training, 59(4), 374–388

    Article  Google Scholar 

  • Bawack, R. E., & Kamdjoug, J. R. K. (2020). The role of digital information use on student performance and collaboration in marginal universities. International Journal of Information Management, 54, 102179

    Article  Google Scholar 

  • Benton, P. (2015). Career development learning in higher education: how authentic work experiences and opportunities for career exploration can increase self-efficacy and inform career identity. Journal of the National Institute for Career Education and Counselling, 34(1), 40–47

    Article  Google Scholar 

  • Bertoch, S. C., Lenz, J. G., Reardon, R. C., & Peterson, G. W. (2014). Goal instability in relation to career thoughts, decision state, and performance in a career course. Journal of Career Development, 41(2), 104–121

    Article  Google Scholar 

  • Bullock-Yowell, E., McConnell, A. E., & Schedin, E. A. (2014). Decided and undecided students: Career self-efficacy, negative thinking, and decision-making difficulties. Nacada journal, 34(1), 22–34

    Article  Google Scholar 

  • Carducci, B. J., & Nave, C. S. (2020). The Wiley Encyclopedia of Personality and Individual Differences, Personality Processes and Individuals Differences (3 vol.). John Wiley & Sons

  • Chin, M. Y., Cohen, C. A. B., & Hora, M. T. (2020). Examining US business undergraduates’ use of career information sources during career exploration. Education + Training, 62(1), 15–30

    Article  Google Scholar 

  • Chock, T. M., Wolf, J. M., Chen, G. M., Schweisberger, V. N., & Wang, Y. (2013). Social media features attract college students to news websites. Newspaper Research Journal, 34(4), 96–108

    Article  Google Scholar 

  • Chuang, N. K., Lee, P. C., & Kwok, L. (2020). Assisting students with career decision-making difficulties: Can career decision-making self-efficacy and career decision-making profile help? Journal of Hospitality Leisure Sport & Tourism Education, 26, 100235

    Article  Google Scholar 

  • Chugh, R., Grose, R., & Macht, S. A. (2021). Social media usage by higher education academics: A scoping review of the literature. Education and Information Technologies, 26(1), 983–999

    Article  Google Scholar 

  • Clark, L. E., Kara-Soteriou, J., & Alfano, M. P. (2018). Using Institutional Data to Predict Teacher Candidate Performance on a Certification Exam. Journal of Assessment and Institutional Effectiveness, 7(1–2), 1–19

    Google Scholar 

  • Clayton, K., Wessel, R. D., McAtee, J., & Knight, W. E. (2019). KEY careers: Increasing retention and graduation rates with career interventions. Journal of Career Development, 46(4), 425–439

    Article  Google Scholar 

  • Cogan, L. S., Schmidt, W. H., & Guo, S. (2019). The Role that mathematics plays in college-and career-readiness: Evidence from PISA. Journal of Curriculum Studies, 51(4), 530–553

    Article  Google Scholar 

  • Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and psychological measurement, 20(1), 37–46

    Article  Google Scholar 

  • Confalonieri, R., Coba, L., Wagner, B., & Besold, T. R. (2021). A historical perspective of explainable Artificial Intelligence. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 11(1), https://doi.org/10.1002/widm.1391

  • Corwin, L. A., Runyon, C. R., Ghanem, E., Sandy, M., Clark, G., Palmer, G. C. … Dolan, E. L. (2018). Effects of discovery, iteration, and collaboration in laboratory courses on undergraduates’ research career intentions fully mediated by student ownership. CBE—Life Sciences Education, 17(2), https://doi.org/10.1187/cbe.17-07-0141

  • Denson, N., & Bowman, N. (2015). The development and validation of an instrument assessing student-institution fit. Assessment & Evaluation in Higher Education, 40(8), 1104–1122

    Article  Google Scholar 

  • Dewsbury, B. M., Taylor, C., Reid, A., & Viamonte, C. (2019). Career choice among first-generation, minority STEM college students. Journal of microbiology & biology education, 20(3), https://doi.org/10.1128/jmbe.v20i3.1775

  • Dey, F., & Cruzvergara, C. Y. (2014). Evolution of career services in higher education. New directions for student services, 148, 5–18

    Article  Google Scholar 

  • Donelan, H. (2016). Social media for professional development and networking opportunities in academia. Journal of further and higher education, 40(5), 706–729

    Article  Google Scholar 

  • Drake, J. K., Jordan, P., & Miller, M. A. (Eds.). (2013). Academic advising approaches: Strategies that teach students to make the most of college. Hoboken, NJ: John Wiley & Sons

    Google Scholar 

  • Ellis, L. (2019). Artificial intelligence for precision education in radiology–Experiences in radiology teaching from a UK foundation doctor. The British Journal of Radiology, 92(1104), https://doi.org/10.1259/bjr.20190779

  • Ellison, A. (2021). A More Mindful Approach to College and Career Counseling. Leading Schools With Social, Emotional, and Academic Development (SEAD) (pp. 231–246). IGI Global

  • Estrada, M., Eroy-Reveles, A., & Matsui, J. (2018). The influence of affirming kindness and community on broadening participation in STEM career pathways. Social issues and policy review, 12(1), 258–297

    Article  Google Scholar 

  • Farnia, F., Nafukho, F. M., & Petrides, K. V. (2018). Predicting career decision-making difficulties: The role of trait emotional intelligence, positive and negative emotions. Frontiers in psychology, 9, 1107. https://doi.org/10.3389/fpsyg.2018.01107

    Article  Google Scholar 

  • Ferrare, J. J., & Miller, J. M. (2020). Making sense of persistence in scientific purgatory: A Multi-institutional analysis of instructors in introductory science, technology, engineering, and mathematics (STEM) courses. The Journal of Higher Education, 91(1), 113–138

    Article  Google Scholar 

  • Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R. … Warschauer, M. (2020). Mining big data in education: Affordances and challenges. Review of Research in Education, 44(1), 130–160

    Article  Google Scholar 

  • Hao, C. (2021). An Exploratory Research on Constructing a Model of Innovation and Entrepreneurship Education for College Students Based on Fuzzy Neural Network Algorithm. Security and Communication Networks, 2021. https://doi.org/10.1155/2021/5533376

  • Healy, M., Hammer, S., & McIlveen, P. (2022). Mapping graduate employability and career development in higher education research: a citation network analysis. Studies in Higher Education, 47(4), 799–811

    Article  Google Scholar 

  • Horta, H. (2018). PhD students’ self-perception of skills and career plans while in doctoral programs: are they associated? Asia Pacific Education Review, 19(2), 211–228

    Article  Google Scholar 

  • Hughes, A. N., Gibbons, M. M., & Mynatt, B. (2013). Using narrative career counseling with the underprepared college student. The Career Development Quarterly, 61(1), 40–49

    Article  Google Scholar 

  • Jimenez, L. (2020). Preparing American Students for the Workforce of the Future: Ensuring Every Student’s Readiness for College, Career, and Civic Life. Center for American progress

  • Johnson, S. T., & Ewbank, A. D. (2018). Heuristics: An Approach to Evaluating News Obtained through Social Media. Knowledge Quest, 47(1), 8–14

    Google Scholar 

  • Karadag, E. (2021). Academic (dis) qualifications of Turkish rectors: their career paths, H-index, and the number of articles and citations. Higher Education, 81(2), 301–323

    Article  Google Scholar 

  • Kuijpers, M., & Meijers, F. (2012). Learning for now or later? Career competencies among students in higher vocational education in the Netherlands. Studies in Higher Education, 37(4), 449–467

    Article  Google Scholar 

  • Lazarides, R., Dicke, A. L., Rubach, C., & Eccles, J. S. (2020). Profiles of motivational beliefs in math: Exploring their development, relations to student-perceived classroom characteristics, and impact on future career aspirations and choices. Journal of educational psychology, 112(1), 70

    Article  Google Scholar 

  • Lejealle, C., King, B., & Chapuis, J. M. (2021). Decoding the educational travel decision: destinations, institutions and social influence.Current Issues in Tourism,1–14

  • Lent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. Career choice and development, 4, 255–311

    Google Scholar 

  • Lynch, J., & Lungrin, T. (2018). Integrating academic and career advising toward student success. New Directions for Higher Education, 2018(184), 69–79

    Article  Google Scholar 

  • Mahadeo, J., Hazari, Z., & Potvin, G. (2020). Developing a computing identity framework: Understanding computer science and information technology career choice. ACM Transactions on Computing Education (TOCE), 20(1), 1–14

    Article  Google Scholar 

  • Makhluf, H. A. (2020). Precision education: Engineering learning, relevancy, mindset, and motivation in online environments. Exploring Online Learning Through Synchronous and Asynchronous Instructional Methods (pp. 202–224). Hershey, PA: IGI Global

    Google Scholar 

  • Martinez, R. R., Baker, S. B., & Young, T. (2017). Promoting career and college readiness, aspirations, and self-efficacy: Curriculum field test. The Career Development Quarterly, 65(2), 173–188

    Article  Google Scholar 

  • Moye, J. N. (2019). Institutional Data Modeling: Looking Beyond the Data. A Machine Learning, Artificial Intelligence Approach to Institutional Effectiveness in Higher Education. Emerald Publishing Limited

  • Navarro, K. M. (2015). An examination of the alignment of student-athletes’ undergraduate major choices and career field aspirations in life after sports. Journal of College Student Development, 56(4), 364–379

    Article  Google Scholar 

  • Nghia, T. L. H., Giang, H. T., & Quyen, V. P. (2019). At-home international education in Vietnamese universities: Impact on graduates’ employability and career prospects. Higher Education, 78(5), 817–834

    Article  Google Scholar 

  • Nie, M., Yang, L., Sun, J., Su, H., Xia, H., Lian, D., & Yan, K. (2018). Advanced forecasting of career choices for college students based on campus big data. Frontiers of Computer Science, 12(3), 494–503

    Article  Google Scholar 

  • Ognjanovic, I., Gasevic, D., & Dawson, S. (2016). Using institutional data to predict student course selections in higher education. The Internet and Higher Education, 29, 49–62

    Article  Google Scholar 

  • Owens, A., Brien, D. L., Ellison, L., & Batty, C. (2020). Student reflections on doctoral learning: challenges and breakthroughs. Studies in Graduate and Postdoctoral Education, 2014, 5–18

  • Park, S. Y., Cha, S. B., Joo, M. H., & Na, H. (2021). A multivariate discriminant analysis of university students’ career decisions based on career adaptability, social support, academic major relevance, and university life satisfaction. International Journal for Educational and Vocational Guidance, 22, 191–206

    Article  Google Scholar 

  • Park, S., & Gil-Garcia, J. R. (2022). Open data innovation: Visualizations and process redesign as a way to bridge the transparency-accountability gap. Government Information Quarterly, 39, 101456. https://doi.org/10.1016/j.giq.2020.101456

    Article  Google Scholar 

  • Peña-Calvo, J. V., Inda‐Caro, M., Rodríguez‐Menéndez, C., & Fernández‐García, C. M. (2016). Perceived supports and barriers for career development for second‐year STEM students. Journal of Engineering Education, 105(2), 341–365

    Article  Google Scholar 

  • Peng, Y., & Wang, Q. (2020). The Impact of mindful agency coaching and motivational interviewing on the development of positive learning dispositions in undergraduate students: A Quasi-experimental intervention study. Journal of Educational and Psychological Consultation, 30(1), 63–89

    Article  Google Scholar 

  • Pordelan, N., & Hosseinian, S. (2021). Design and development of the online career counselling: a tool for better career decision-making. Behaviour & Information Technology, 41(1), 118–138

    Article  Google Scholar 

  • Pordelan, N., Sadeghi, A., Abedi, M. R., & Kaedi, M. (2020). Promoting student career decision-making self-efficacy: An online intervention. Education and Information Technologies, 25(2), 985–996

    Article  Google Scholar 

  • Pugh, K. J., Paek, S. H., Phillips, M. M., Sexton, J. M., Bergstrom, C. M., Flores, S. D., & Riggs, E. M. (2021). Predicting academic and career choice: The role of transformative experience, connection to instructor, and gender accounting for interest/identity and contextual factors.Journal of Research in Science Teaching,1–30

  • Rosengard, D., Tucker-McLaughlin, M., & Brown, T. (2014). Students and social news: How college students share news through social media. Electronic news, 8(2), 120–137

    Article  Google Scholar 

  • Ross, S. M., & Morrison, G. R. (2012). Constructing a deconstructed campus: Instructional design as vital bricks and mortar. Journal of computing in higher education, 24(2), 119–131

    Article  Google Scholar 

  • Sampson, J. P., Osborn, D. S., Kettunen, J., Hou, P. C., Miller, A. K., & Makela, J. P. (2018). The validity of social media–based career information. The Career Development Quarterly, 66(2), 121–134

    Article  Google Scholar 

  • Schaub, M. (2012). The profession of college career services delivery: What college counselors should know about career centers. Journal of college student psychotherapy, 26(3), 201–215

    Article  Google Scholar 

  • Sharma, K., Papamitsiou, Z., & Giannakos, M. N. (2019, September). Modelling Learners’ Behaviour: A Novel Approach Using GARCH with Multimodal Data. In European Conference on Technology Enhanced Learning (pp. 450–465). Springer, Cham

  • Sobnath, D., Kaduk, T., Rehman, I. U., & Isiaq, O. (2020). Feature selection for UK disabled students’ engagement post higher education: a machine learning approach for a predictive employment model. Ieee Access : Practical Innovations, Open Solutions, 8, 159530–159541

    Article  Google Scholar 

  • Stebleton, M. J., Kaler, L. S., Diamond, K. K., & Lee, C. (2020). Examining Career Readiness in a Liberal Arts Undergraduate Career Planning Course. Journal of employment counseling, 57(1), 14–26

    Article  Google Scholar 

  • Steele, K. J., VanRyn, V. S., Stanescu, C. I., Rogers, J., & Wehrwein, E. A. (2020). Start with the end in mind: using student career aspirations and employment data to inform curriculum design for physiology undergraduate degree programs. Advances in Physiology Education, 44, 697–701

    Article  Google Scholar 

  • Sudhana, P., Ameen, A., & Isaac, O. (2020). A multi-theoretical framework to better understand the college major choice in arts and design. Journal of Applied Research in Higher Education, 12(5), 1009–1023

    Article  Google Scholar 

  • Suryani, A., & George, S. (2021). “Teacher education is a good choice, but I don’t want to teach in schools.” An analysis of university students’ career decision making.Journal of Education for Teaching,1–15

  • Swing, R. L., Jones, D., & Ross, L. E. (2016). The AIR national survey of institutional research offices. Retrieved from Association for Institutional Research website. http://www.airweb.org/nationalsurvey

  • Thompson, M. N., Her, P., Fetter, A. K., & Perez-Chavez, J. (2019). College Student Psychological Distress: Relationship to Self‐Esteem and Career Decision Self‐Efficacy Beliefs. The Career Development Quarterly, 67(4), 282–297

    Article  Google Scholar 

  • Tinto, V. (2004). Student retention and graduation: Facing the truth, living with the consequences. Retrieved from https://files.eric.ed.gov/fulltext/ED519709.pdf

  • Villi, M., Matikainen, J., & Khaldarova, I. (2016). Recommend, tweet, share: User-distributed content (UDC) and the convergence of news media and social networks. Media Convergence Handbook-Vol. 1 (pp. 289–306). Berlin, Heidelberg: Springer

    Chapter  Google Scholar 

  • Voss, N. M., Lake, C. J., & Chlevin-Thiele, C. (2019). Construction and initial validation of the career maximizing scale. The Career Development Quarterly, 67(2), 156–170

    Article  Google Scholar 

  • Wang, J., & Li, L. (2016). Research on the College Graduate Employment Education Based on Data Mining Technology. The Anthropologist, 23(1–2), 231–235

    Article  Google Scholar 

  • Wang, R., & Orr, J. E. Jr. (2022). Use of data analytics in supporting the advising of undecided students. Journal of College Student Retention: Research Theory & Practice, 23(4), 834–849. https://doi.org/10.1177/1521025119880819

    Article  Google Scholar 

  • Wendling, E., & Sagas, M. (2020). An application of the social cognitive career theory model of career self-management to college athletes’ career planning for life after sport. Frontiers in psychology, 11, 9. https://doi.org/10.3389/fpsyg.2020.00009

    Article  Google Scholar 

  • Whistance, D., & Campbell, S. (2019). Investigating the impact of a careers development model: Validation of the Employability Self Evaluation measure and proposal of a readiness/competence career development model.Higher Education Careers Service Unit. https://luminate.prospects.ac.uk/investigating-the-impact-of-a-careers-development-model

  • Wright, S. L., Jenkins-Guarnieri, M. A., & Murdock, J. L. (2013). Career development among first-year college students: College self-efficacy, student persistence, and academic success. Journal of Career Development, 40(4), 292–310

    Article  Google Scholar 

  • Xiao, M., & Yi, H. (2021). Building an efficient artificial intelligence model for personalized training in colleges and universities. Computer Applications in Engineering Education, 29(2), 350–358

    Article  Google Scholar 

  • Xueyan, L. (2017). An Empirical Study on the Improvement of College Students’ Career Planning and Psychological Quality in the Modern Media Environment. Agro Food Industry Hi Tech, 28(1), 295–299

    Google Scholar 

Download references

Acknowledgements

This research was supported by the Higher Education Sprout Project of National Yang Ming Chiao Tung University (NYCU) and the Ministry of Education (MOE), Taiwan, as well as the Ministry of Science and Technology in Taiwan through Grant numbers MOST 108-2511-H-009 -019-MY2.

Author information

Authors and Affiliations

  1. Institute of Education, National Yang Ming Chiao Tung University (NYCU), 1001 University Road, 300, Hsinchu, Taiwan

    Tzu-Chi Yang & Chung-Yuan Chang

Authors
  1. Tzu-Chi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chung-Yuan Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tzu-Chi Yang.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, TC., Chang, CY. Using Institutional data and messages on Social Media to Predict the Career decisions of University Students - A Data-Driven Approach. Educ Inf Technol 28, 1117–1139 (2023). https://doi.org/10.1007/s10639-022-11185-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-022-11185-3

Keywords

Search

Navigation