Data Analytics and STEM Student Success: The Impact of Predictive Analytics-Informed Academic Advising Among Undeclared First-Year Engineering Students

Abstract

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.

Keywords

data analytics choice of major STEM student success propensity score matching

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References

Allen

Robbins

S. B.

(2008). Prediction of college major persistence based on vocational interests, academic preparation, and first-year academic performance. Research in Higher Education, 49(1), 62–79.

Arcidiacono

(2004). Ability sorting and the returns to college major. Journal of Econometrics, 121(1), 343–375.

Arcidiacono

Aucejo

E. M.

Spenner

(2012). What happens after enrollment? An analysis of the time path of racial differences in GPA and major choice. IZA Journal of Labor Economics, 1(1), 1–24.

Astin

A. W.

(1993). What matters in college: Four critical years revisited (Vol. 1). San Francisco, CA: Jossey-Bass.

Bandura

(1993). Perceived self-efficacy in cognitive development and functioning. Educational Psychologist, 28(2), 117–148.

Barber

Sharkey

(2012). Course correction: Using analytics to predict course success. In Proceedings from LAK’12: The Second International Conference on Learning Analytics and Knowledge, Vancouver, Canada (pp. 259–262). New York, NY: ACM.

Barneveld

A. V.

Arnold

K. E.

Campbell

J. P.

(2012). Analytics in higher education: Establishing a common language. Retrieved from EDUCAUSE Learning Initiative: https://library.educause.edu/resources/2012/1/analytics-in-higher-education-establishing-a-common-language

Berger

M. C.

(1988). Predicted future earnings and choice of college major. Industrial & Labor Relations Review, 41(3), 418–429.

Bichsel

(2012). Analytics in higher education: Benefits, barriers, progress, and recommendations. Retrieved from EDUCAUSE Center for Applied Research: https://sites.ewu.edu/instructionaltech/2012/09/27/analytics-in-higher-education-benefits-barriers-progress-and-recommendations/

10.

Boudarbat

(2008). Field of study choice by community college students in Canada. Economics of Education Review, 27(1), 79–93.

11.

Brown

S. D.

Rector

C. C.

(2008). Conceptualizing and diagnosing problems in vocational decision making. In Brown

S. D.

Lent

R. W.

(Eds.), Handbook of counseling psychology (pp. 392–407). Hoboken, NJ: John Wiley.

12.

Cabrera

A. F.

Castaneda

M. B.

Nora

Hengstler

(1992). The convergence between two theories of college persistence. Journal of Higher Education, 63(2), 143–164.

13.

Cabrera

A. F.

Nora

Castaneda

M. B.

(1993). College persistence: Structural equations modeling test of an integrated model of student retention. Journal of Higher Education, 64(2), 123–139.

14.

Caliendo

Kopeinig

(2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31–72.

15.

Campbell

DeBlois

Oblinger , (2007). Academic analytics: A new tool for a new era. EDCUASE Review, 42(4), 40–57.

16.

Chen

(2013). STEM attrition: College students’ paths into and out of STEM fields (Statistical Analysis Report NCES 2014-001). Retrieved from National Center for Education Statistics: https://eric.ed.gov/?id=ED544470

17.

Cohen

Hanno

D. M.

(1993). An analysis of underlying constructs affecting the choice of accounting as a major. Issues in Accounting Education, 8(2), 219.

18.

Cueso

(2005). Decided,” “undecided,” and “in transition:” Implications for academic advisement, career counseling, and student retention. In Feldman

R. S.

(Ed.), Improving the first year of college: Research and practice. (pp. 27–48). Mahwah, NJ: Erlbaum.

19.

Dahlstrom

(2016). Moving the Red Queen forward: Maturing analytics capabilities in higher education. EDUCAUSE Review, 51(5), 36–54.

20.

Denley

(2014). How predictive analytics and choice architecture can improve student success. Research and Practice in Assessment, 9, 61–69.

21.

Eccles

J. S.

(1987). Gender roles and women's achievement-related decisions. Psychology of Women Quarterly, 11(2), 135–172.

22.

Eliot

Turns

(2011). Constructing professional portfolios: Sense-making and professional identity development for engineering undergraduates. Journal of Engineering Education, 100(4), 630.

23.

Ferrare

J. J.

Lee

Y. G.

(2014). Should we still be talking about leaving? A comparative examination of social inequality in undergraduate patterns of switching majors (WCER Working Paper No. 2014-5). Retrieved from http://files.eric.ed.gov/fulltext/ED556490.pdf

24.

Foraker

M. J.

(2012). Does changing majors really affect the time to graduate? The impact of changing majors on student retention, graduation, and time to graduate. Retrieved from Office of Institutional Research, Western Kentucky University: https://www.wku.edu/instres/documents/air_major_change.pdf

25.

Gordon

V. N.

Steele

G. E.

(2015). The undecided college student: An academic and career advising challenge (4th ed.). Springfield, IL: Charles C Thomas.

26.

Hersh

W. R.

(2002). Medical informatics: Improving health care through information. JAMA, 288(16), 1955–1958.

27.

Jenkins

Cho

(2012). Get with the program: Accelerating community college students’ entry into and completion of programs of study (Community College Research Center Working Paper No. 32). Retrieved from https://academiccommons.columbia.edu/catalog/ac:144895

28.

Kreysa

P. G.

(2006). The impact of remediation on persistence of under-prepared college students. Journal of College Student Retention: Research, Theory & Practice, 8(2), 251–270.

29.

Lechner

(2002). Some practical issues in the evaluation of heterogeneous labor market programs by matching methods. Journal of the Royal Statistical Society: Series A (Statistics in Society), 165(1), 59–82.

30.

Lent

R. W.

Brown

S. D.

Larkin

K. C.

(1984). Relation of self-efficacy expectations to academic achievement and persistence. Journal of Counseling Psychology, 31(3), 356.

31.

Leppel

(2001). The impact of major on college persistence among freshmen. Higher Education, 41(3), 327–342.

32.

Maple

S. A.

Stage

F. K.

(1991). Influences on the choice of math/science major by gender and ethnicity. American Educational Research Journal, 28(1), 37–60.

33.

Mauldin

Crain

J. L.

Mounce

P. H.

(2000). The accounting principles instructor's influence on students’ decision to major in accounting. Journal of Education for Business, 75(3), 142–148.

34.

Micceri

(2001, June 3–6). Change your major and double your graduation chances. Paper presented at the Association for Institutional Research Annual Forum, Long Beach, CA.

35.

Miller

A. D.

Rottinghaus

P. J.

(2014). Career indecision, meaning in life, and anxiety: An existential framework. Journal of Career Assessment, 22(2), 233–247.

36.

Murphy

(2000). Predicting graduation: Are admissions test scores and high school performance measures adequate (Doctoral dissertation). University of Illinois at Urbana-Champaign, Champaign, IL. Retrieved from IDEALS: http://hdl.handle.net/2142/80338

37.

National Science and Technology Council. (2013). Federal science, technology, engineering, and mathematics (STEM) education 5 year strategic plan. Retrieved from https://www.whitehouse.gov/sites/whitehouse.gov/files/ostp/Federal_STEM_Strategic_Plan.pdf

38.

National Science Foundation, National Center for Science and Engineering Statistics (2017). Women, minorities, and persons with disabilities in science and engineering: 2017 (Special Report NSF 17-310). Retrieved from www.nsf.gov/statistics/wmpd/

39.

Ngai

E. W. T.

Xiu

Chau

D. C. K.

(2009). Application of data mining techniques in customer relationship management: A literature relationship and classification. Expert Systems with Applications, 36(2), 2,529–2,602.

40.

Pajares

(1996). Self-efficacy beliefs in academic settings. Review of Educational Research, 66(4), 543–578.

41.

Pascarella

E. T.

Terenzini

P. T.

(1980). Predicting freshman persistence and voluntary dropout decisions from a theoretical model. The Journal of Higher Education, 51(1), 60–75.

42.

Porter

S. R.

Umbach

P. D.

(2006). College major choice: An analysis of person–environment fit. Research in Higher Education, 47(4), 429–449.

43.

Rosenbaum

P. R.

Rubin

D. B.

(1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

44.

Rubin

D. B.

(1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127(8), 757–763.

45.

Sandler

M. E.

(2000). Career decision-making self-efficacy, perceived stress, and an integrated model of student persistence: A structural model of finances, attitudes, behavior, and career development. Research in Higher Education, 41(5), 537–580.

46.

Sklar

(2014). The impact of change of major on time to Bachelor’s degree completion with special emphasis on STEM disciplines: A multilevel discrete-time hazard modeling approach (final report). Retrieved from https://www.airweb.org/GrantsAndScholarships/Documents/Grants2013/SklarFinalReport.pdf

47.

Skyfactor. (2014). The foundation of MAP-works: Research and theoretical underpinnings of MAP-works. Retrieved from https://www2.indstate.edu/studentsuccess/pdf/MAP-Works Foundation Oct 2012 .pdf

48.

Starobin

Laanan

F. S.

(2008). Broadening female participation in science, technology, engineering, and mathematics: Experiences at community colleges. New Directions for Community College, 142, 37–46.

49.

Stinebrickner

T. R.

Stinebrickner

(2011). Math or science? Using longitudinal expectations data to examine the process of choosing a college major (NBER Working Paper No. 16869). Washington, DC: National Bureau of Economic Research. Retrieved from http://www.nber.org/papers/w16869

50.

Tinto

(1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45(1), 89–125.

51.

Tinto

(1987). Leaving college: Rethinking the causes and cures of student attrition. Chicago, IL: University of Chicago Press.

52.

Tinto

(2005). Taking student success seriously: Rethinking the first year of college. Paper presented at the Ninth Annual Intersession Academic Affairs Forum, Fullerton, CA.

53.

Tinto

(2014). Tinto’s South Africa lectures. Journal of Student Affairs in Africa, 2(2), 5–28.

54.

Tinto

Pusser

(2006, June). Moving from theory to action: Building a model of institutional action for student success. Paper presented at the National Symposium on Postsecondary Student Success, Washington, DC. Retrieved from http://web.ewu.edu/groups/academicaffairs/IR/NPEC_5_Tinto_Pusser_Report.pdf

55.

Turner

S. E.

Bowen

W. G.

(1999). Choice of major: The changing (unchanging) gender gap. Industrial & Labor Relations Review, 52(2), 289–313.

56.

Watkins

Mazur

(2013). Retaining students in science, technology, engineering, and mathematics (STEM) majors. Journal of College Science Teaching, 42(5), 36–41.

57.

Y. J.

(2018). The experience and persistence of college students in STEM majors. Journal of College Student Retention: Research, Theory & Practice, 19(4), 413–432.

58.

Zafar

(2013). College major choice and the gender gap. Journal of Human Resources, 48(3), 545–595.

59.

Zimmerman

B. J.

Bandura

Martinez-Pons

(1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American Educational Research Journal, 29(3), 663–676.