Advanced forecasting of career choices for college students based on campus big data

Abstract

Career indecision is a difficult obstacle confronting adolescents. Traditional vocational assessment research measures it by means of questionnaires and diagnoses the potential sources of career indecision. Based on the diagnostic outcomes, career counselors develop treatment plans tailored to students. However, because of personal motives and the architecture of the mind, it may be difficult for students to know themselves, and the outcome of questionnaires may not fully reflect their inner states and statuses. Selfperception theory suggests that students’ behavior could be used as a clue for inference. Thus, we proposed a data-driven framework for forecasting student career choice upon graduation based on their behavior in and around the campus, thereby playing an important role in supporting career counseling and career guidance. By evaluating on 10M behavior data of over four thousand students, we show the potential of this framework for this functionality.

Keywords

campus big data / career identity / career choice prediction / self-knowledge

References

[1] Erikson E H. Identity: Youth and Crisis. No. 7. New York: W. W. Norton & Company, 1994

[2] Marcia J E, Waterman A S, Matteson D R, Archer S L, Orlofsky J L. Ego Identity: A Handbook for Psychosocial Research. New York: Springer Science & Business Media, 2012

[3] Lopez F G. A paradoxical approach to vocational indecision. The Personnel and Guidance Journal, 1983, 61(7): 410–412

[4] Savickas Mark L. Identity in vocational development. Journal of Vocational Behavior, 1985, 27(3): 329–337

[5] Gati I, Krausz M, Osipow S H. A taxonomy of difficulties in career decision making. Journal of Counseling Psychology, 1996, 43(4): 510

[6] Savickas M L. The transition from school to work: a developmental perspective. The Career Development Quarterly, 1999, 47(4): 326–336

[7] Wilson T D, Dunn E W. Self-knowledge: its limits, value, and potential for improvement. Psychology, 2004, 55

[8] Bem D J. Self-perception theory. Advances in Experimental Social Psychology, 1972, 6: 1–62

[9] Albion M J, Fogarty G J. Factors influencing career decision making in adolescents and adults. Journal of Career Assessment, 2002, 10(1): 91–126

[10] 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. Journal of Applied Psychology, 2006, 91(1): 40

[11] Thompson M N, Subich L M. The relation of social status to the career decision-making process. Journal of Vocational Behavior, 2006, 69(2): 289–301

[12] Festinger L. A theory of social comparison processes. Human Relations, 1954, 7(2): 117–140

[13] Reichling T, Wulf V. Expert recommender systems in practice: evaluating semi-automatic profile generation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. 2009, 59–68

[14] Balog K, de Rijke M. Finding experts and their eetails in e-mail corpora. In: Proceedings of the 15th ACM International Conference on World Wide Web. 2006, 1035–1036

[15] Guy I, Avraham U, Carmel D, Ur S, Jacovi M, Ronen I. Mining expertise and interests from social media. In: Proceedings of the 22nd ACM International Conference on World Wide Web. 2013, 515–526

[16] 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 the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014, 1729–1738

[17] Varshney K R, Wang J, Mojsilovic A, Fang D P, Bauer J H. Predicting and recommending skills in the social enterprise. In: Proceedings of AAAI ICWSM Workshop on Social Computing for Workforce. 2013, 20–23

[18] Baruch Y. Transforming careers: from linear to multidirectional career paths: organizational and individual perspectives. Career Development International, 2004, 9(1): 58–73

[19] Wang J, Zhang Y, Posse C, Bhasin A. Is it time for a career switch? In: Proceedings of the 22nd ACM International Conference on World Wide Web. 2013, 1377–1388

[20] Deville P, Wang D S, Sinatra R, Song C M, Blondel V D, Barabási A L. Career on the move: geography, stratification, and scientific impact. Scientific Reports. 2014

[21] Xu H, Yu Z W, Xiong H, Guo B, Zhu H S. Learning career mobility and human activity patterns for job change analysis. In: Proceedings of IEEE International Conference on Data Mining. 2015, 1057–1062

[22] 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, 171–177

[23] Wang J G, Huang J Z, Guo J F, Lan Y Y. Recommending high-utility search engine queries via a query recommending model. Neurocomputing, 2015, 167: 195–208

[24] Paparrizos I, Cambazoglu B B, Gionis A. Machine learned job recommendation. In: Proceedings of the 5th ACM Conference on Recommender Systems. 2011, 325–328

[25] Li J Q, Liu C C, Liu B, Mao R, Wang Y C, Chen S, Yang J J, Pan H, Wang Q. Diversity-aware retrieval of medical records. Computers in Industry, 2015, 69: 81–91

[26] Hong W X, Li L, Li T, Pan W F. iHR: an online recruiting system for Xiamen talent service center. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2013, 1177–1185

[27] Mao R, Xu H L, Wu W B, Li J Q, Li Y, Lu M H. Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems. IEEE Communications Magazine, 2015, 53(1): 42–47

[28] Xu Y, Li Z, Gupta A, Bugdayci A, Bhasin A. Modeling professional similarity by mining professional career trajectories. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2014, 1945–1954

[29] Mao R, Zhang P H, Li X L, Liu X, Lu M H. Pivot selection for metricspace indexing. International Journal of Machine Learning and Cybernetics, 2016, 7(2): 311–323

[30] Parsons F. Choosing a Vocation. Boston, MA: Houghton Mifflin, 1909

[31] Holland J L. Making vocational choices: a theory of vocational personalities and work environments. Psychological Assessment Resources, 1997

[32] Super D E. A life-span, life-space approach to career development. Journal of Vocational Behavior, 1980, 16(3): 282–298

[33] Schein E H. Career Anchors: Discovering Your Real Values. San Francisco: Jossey Bass Pfeiffer, 1990

[34] Krumboltz J D, Mitchell A M, Jones G B. A social learning theory of career selection. The Counseling Psychologist, 1976, 6(1): 71–81

[35] Wu R Z, Liu Q, Liu Y P, Chen E H, Su Y, Chen Z G, Hu G P. Cognitive modelling for predicting examinee performance. In: Proceedings of International Conference on Artificial Intelligence. 2015: 1017–1024

[36] Guan C, Lu X J, Li X L, Chen E H, Zhou W J, Xiong H. Discovery of college students in financial hardship. In: Proceedings of IEEE International Conference on Data Mining. 2015, 141–150

[37] Desmarais M C. Mapping question items to skills with nonnegative matrix factorization. ACM SIGKDD Explorations Newsletter, 2012, 13(2): 30–36

[38] Nie M, Yang L, Ding B, Xia H, Xu H C, Lian D F. Forecasting career choice for college students based on campus big data. In: Proceeding of Asia-Pacific Web Conference. 2016, 359–370

[39] Lian D F, Liu Q, Zhu W Y, Xie X, Xiong H. Mutual reinforcement of academic performance prediction and library book recommendation. In Proceedings of the 16th IEEE International Conference on Data Mining. 2016, 1023–1028

[40] Lian D F, Ge Y, Zhang F Z, Yuan N J, Xie X, Zhou T, Rui Y. Contentaware collaborative filtering for location recommendation based on human mobility data. In: Proceedings of IEEE International Conference on Data Mining. 2015, 261–270

[41] 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. Applied Mathematics and Computation, 2008, 205(2): 849–860

[42] Yang Y M. An evaluation of statistical approaches to text categorization. Information Retrieval, 1999, 1(1–2): 69–90

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