Forecasting Career Choice for College Students Based on Campus Big Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9931)


Career indecision is a difficult obstacle in front of adolescents. Traditional vocational assessment research measure it by means of questionnaires and diagnose the potential sources of career indecision. Based on the diagnostic outcomes, career consolers develop the treatment plans tailor to students. However, because of personal motives and the architecture of the mind, it may be difficult for students to know themselves, so that the outcome of questionnaires can not fully reflect their inner states and statuses. Self-perception theory suggest students’ behavior could be used as clue for inference. Thus, we proposed a data-driven framework for forecast student career choice of graduation based on their behavior in and around the campus, playing an important role in supporting career counseling and career guiding. By evaluating on 10M behavior data of over four thousand students, we show the potential of this framework in these functionality.


Campus big data Career identity Career prediction Self-knowledge 


  1. 1.
    Albion, M.J., Fogarty, G.J.: Factors influencing career decision making in adolescents and adults. J. Career Assess. 10(1), 91–126 (2002)CrossRefGoogle Scholar
  2. 2.
    Balog, K., de Rijke, M.: Finding experts and their details in e-mail corpora. In: Proceedings of WWW 2006, pp. 1035–1036. ACM (2006)Google Scholar
  3. 3.
    Baruch, Y.: Transforming careers: from linear to multidirectional career paths: organizational and individual perspectives. Career Dev. Int. 9(1), 58–73 (2004)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bem, D.J.: Self-perception theory (1973)Google Scholar
  5. 5.
    Chen, Y.-P., Yang, J.-Y., Liou, S.-N., Lee, G.-Y., Wang, J.-S.: Online classifier construction algorithm for human activity detection using a tri-axial accelerometer. Appl. Math. Comput. 205(2), 849–860 (2008)MathSciNetGoogle Scholar
  6. 6.
    Desmarais, M.C.: Mapping question items to skills with non-negative matrix factorization. ACM SIGKDD Explor. Newslett. 13(2), 30–36 (2012)CrossRefGoogle Scholar
  7. 7.
    Deville, P., Wang, D., Sinatra, R., Song, C., Blondel, V.D., Barabási, A.-L.: Career on the move: geography, stratification, and scientific impact. Sci. Rep. 4, 1–7 (2014)CrossRefGoogle Scholar
  8. 8.
    Dudley, N.M., Orvis, K.A., Lebiecki, J.E., Cortina, J.M.: A meta-analytic investigation of conscientiousness in the prediction of job performance: examining the intercorrelations and the incremental validity of narrow traits. J. Appl. Psychol. 91(1), 40 (2006)CrossRefGoogle Scholar
  9. 9.
    Erikson, E.H.: Identity: Youth and Crisis, vol. 7. WW Norton & Company, New York (1994)Google Scholar
  10. 10.
    Festinger, L.: A theory of social comparison processes. Hum. Relat. 7(2), 117–140 (1954)CrossRefGoogle Scholar
  11. 11.
    Gati, I., Krausz, M., Osipow, S.H.: A taxonomy of difficulties in career decision making. J. Couns. Psychol. 43(4), 510 (1996)CrossRefGoogle Scholar
  12. 12.
    Guan, C., Lu, X., Li, X., Chen, E., Zhou, W., Xiong, H.: Discovery of college students in financial hardship. In: Proceedings of ICDM 2015, pp. 141–150. IEEE (2015)Google Scholar
  13. 13.
    Guy, I., Avraham, U., Carmel, D., Ur, S., Jacovi, M., Ronen, I.: Mining expertise and interests from social media. In: Proceedings of WWW 2013, pp. 515–526. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  14. 14.
    Hadiji, F., Mladenov, M., Bauckhage, C., Kersting, K.: Computer science on the move: inferring migration regularities from the web via compressed label propagation. In: Proceedings of IJCAI 2015, pp. 171–177. AAAI Press (2015)Google Scholar
  15. 15.
    Holland, J.L.: Making Vocational Choices: A Theory of Vocational Personalities and Work Environments. Psychological Assessment Resources, Odessa (1997)Google Scholar
  16. 16.
    Hong, W., Li, L., Li, T., Pan, W.: iHR: an online recruiting system for Xiamen talent service center. In: Proceedings of KDD 2013, pp. 1177–1185. ACM (2013)Google Scholar
  17. 17.
    Krumboltz, J.D., Mitchell, A.M., Jones, G.B.: A social learning theory of career selection. Couns. Psychol. 6(1), 71–81 (1976)CrossRefGoogle Scholar
  18. 18.
    Lopez, F.G.: A paradoxical approach to vocational indecision. Pers. Guidance J. 61(7), 410–412 (1983)CrossRefGoogle Scholar
  19. 19.
    Marcia, J.E., Waterman, A.S., Matteson, D.R., Archer, S.L., Orlofsky, J.L.: Ego Identity: A Handbook for Psychosocial Research. Springer Science & Business Media, New York (2012)Google Scholar
  20. 20.
    Paparrizos, I., Cambazoglu, B.B., Gionis, A.: Machine learned job recommendation. In: Proceedings of RecSys 2011, pp. 325–328. ACM (2011)Google Scholar
  21. 21.
    Parsons, F.: Choosing a Vocation. Houghton Mifflin, Boston (1909)Google Scholar
  22. 22.
    Reichling, T., Wulf, V.: Expert recommender systems in practice: evaluating semi-automatic profile generation. In: Proceedings of CHI 2009, pp. 59–68. ACM (2009)Google Scholar
  23. 23.
    Savickas, M.L.: Identity in vocational development. J. Vocat. Behav. 27(3), 329–337 (1985)CrossRefGoogle Scholar
  24. 24.
    Savickas, M.L.: The transition from school to work: a developmental perspective. Career Dev. Q. 47(4), 326–336 (1999)CrossRefGoogle Scholar
  25. 25.
    Schein, E.H.: Career Anchors: Discovering Your Real Values. University Associates San Diego, San Diego (1990)Google Scholar
  26. 26.
    Super, D.E.: A life-span, life-space approach to career development (1990)Google Scholar
  27. 27.
    Thompson, M.N., Subich, L.M.: The relation of social status to the career decision-making process. J. Vocat. Behav. 69(2), 289–301 (2006)CrossRefGoogle Scholar
  28. 28.
    Varshney, K.R., Chenthamarakshan, V., Fancher, S.W., Wang, J., Fang, D., Mojsilović, A.: Predicting employee expertise for talent management in the enterprise. In: Proceedings of KDD 2014, pp. 1729–1738. ACM (2014)Google Scholar
  29. 29.
    Varshney, K.R., Wang, J., Mojsilovic, A., Fang, D., Bauer, J.H.: Predicting and recommending skills in the social enterprise. In: Proceedings of AAAI ICWSM Workshop Social Computing for Workforce, vol. 2, pp. 20–23 (2013)Google Scholar
  30. 30.
    Wang, J., Zhang, Y., Posse, C., Bhasin, A.: Is it time for a career switch? In: Proceedings of WWW 2013, pp. 1377–1388. International World Wide Web Conferences Steering Committee (2013)Google Scholar
  31. 31.
    Wilson, T.D., Dunn, E.W.: Self-knowledge: its limits, value, and potential for improvement. Annu. Rev. Psychol. 55, 493–518 (2004)CrossRefGoogle Scholar
  32. 32.
    Xu, H., Yu, Z., Xiong, H., Guo, B., Zhu, H.: Learning career mobility and human activity patterns for job change analysis. In: Proceedings of ICDM 2015, pp. 1057–1062. IEEE (2015)Google Scholar
  33. 33.
    Xu, Y., Li, Z., Gupta, A., Bugdayci, A., Bhasin, A.: Modeling professional similarity by mining professional career trajectories. In: Proceedings of KDD 2014, pp. 1945–1954. ACM (2014)Google Scholar
  34. 34.
    Yang, Y.: An evaluation of statistical approaches to text categorization. Inf. Retrieval 1(1–2), 69–90 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina
  2. 2.College of Teacher Education and PsychologySichuan Normal UniversityChengduChina

Personalised recommendations