Modeling Engineering Student Success Needs

  • Tracee Walker Gilbert
  • Janis TerpennyEmail author
  • Tonya Smith-Jackson
Part of the Women in Engineering and Science book series (WES)


Much research has sought to understand student success and capture this knowledge in terms of underlying theory. Prior work has provided an accounting of the factors that explain why students decide to leave and, to some extent, why students persist on to graduation. In spite of research, there continues to be a gap between theory and practice. Many times, theoretical findings have not translated well into programs and actions that have significantly improved student success outcomes. This research shifts the focus from trying to understand why students leave or stay in college to understanding the needs of students as the basis for improving student success outcomes.

A statistically verified model of engineering student success needs is developed. Emphasis in this model is placed on post-entry variables that provide insight into those factors and educational processes that institutional leaders can directly impact. An eight step questionnaire development and validation process is presented for a new instrument—the Engineering Student Needs Questionnaire (ESNQ)—to measure the model variables. Since institutions vary considerably in their size, culture, and student demographics, the model provides insight into the dimensions that institutional decision-makers can target to meet the unique needs of their engineering students. A case example is presented to apply the ESNQ.


  1. Astin AW (1984) Student involvement: a developmental theory for higher education. J Coll Stud Pers 25:297–308Google Scholar
  2. Bartlett MS (1954) A note on the multiplying factors for various x2 approximation. J R Stat Soc 16:296–298zbMATHGoogle Scholar
  3. Bean JP (1980) Dropouts and turnover: The synthesis and test of a causal model of student attrition. Res High Educ 12(2):155–187CrossRefGoogle Scholar
  4. Bean JP (1983) The application of a model of turnover in work organizations to the student attrition process. Rev High Educ 2:129–148CrossRefGoogle Scholar
  5. Bean JP, Eaton SB (2000) A psychological model of college student retention. In: Braxton JM (ed) Reworking the student departure puzzle. Vanderbilt University Press, NashvilleGoogle Scholar
  6. Berger JB, Lyon SC (2005) Past and present: a historical view of retention. In: Seidman A (ed) College student retention: formula for student success. Praeger, WestportGoogle Scholar
  7. Besterfield-Sacre M, Moreno M, Shuman L, Atman CJ (2001) Gender and ethnicity differences in freshman engineering student attitudes: A cross-institutional study. J Eng Educ 90(4):477–490CrossRefGoogle Scholar
  8. Blanchard BS, Fabrycky WJ (2017) Systems engineering and analysis, 6th edn. Prentice Hall, Englewood Cliffs, NJGoogle Scholar
  9. Bowman NA (2010) The development of psychological well-being among first-year college students. J Coll Stud Dev 51(2):180–200CrossRefGoogle Scholar
  10. Braxton J, McKinney J, Reynolds P (2006) Cataloging institutional efforts to understand and reduce college student departure. In: John ES (ed) Improving academic success: using persistence research to address critical challenges, New directions for institutional research. Jossey-Bass, San FranciscoGoogle Scholar
  11. Chen X (2013) STEM attrition: college students’ paths into and out of STEM fields. Statistical analysis report. NCES 2014–001. National Center for Education Statistics, Washington, DCGoogle Scholar
  12. Churchill GA (1979) A paradigm for developing better measures of marketing constructs. J Mark Res 16:64–73CrossRefGoogle Scholar
  13. Clark LA, Watson D (1995) Constructing validity: basic issues in objective scale development. Psychol Assess 7(3):309–319CrossRefGoogle Scholar
  14. Cohen J, Cohen P (1983) Applied multiple regression/correlation analysis for the behavioral sciences, 3rd edn. Erlbaum, HillsdaleGoogle Scholar
  15. Cohen P, West SG, Aiken LS (2014) Applied multiple regression/correlation analysis for the behavioral sciences. Psychology Press, New YorkCrossRefGoogle Scholar
  16. DeVillis RF (1991) Scale development: theory and applications. Sage, Newbury ParkGoogle Scholar
  17. Eris O, Chen H, Bailey T, Engerman K, Loshbaugh HG, Griffin A, Lichtenstein G, Cole A (2005) Development of the Persistence in Engineering (PIE) survey instrument. In: Proc. Amer. Soc. Eng. Educ, p 1Google Scholar
  18. Hair JF, Anderson RE, Tatham RL, Black WC (1998) Multivariate data analysis. Prentice Hall, Upper Saddle RiverGoogle Scholar
  19. Kaiser HF (1970) A second generation little jiffy. Psychometrika 35:401–415CrossRefGoogle Scholar
  20. Kuh GD (2001) Assessing what really matters to student learning: inside the national survey of student engagement. Change 33(3):10–17CrossRefGoogle Scholar
  21. Kuh GD (2009) The national survey of student engagement: conceptual and empirical foundations. New Directions for Institutional Research 141:1–20Google Scholar
  22. Kuh GD, Kinzie J et al (2006) What matters to student success: a review of the literature. National Postsecondary Education Cooperative, BloomingtonGoogle Scholar
  23. Lent RW, Sheu H, Schmidt J, Brenner BR, Wilkins G, Brown SD, Gloster CS, Schmidt LC, Lyons H, Treisteman D (2005) Social cognitive predictors of academic interests and goals in engineering: utility for women and students at historically Black universities. J Couns Psychol 52(1):84–92CrossRefGoogle Scholar
  24. Moore GC, Benbasat I (1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Inf Syst Res 2(3):192–222CrossRefGoogle Scholar
  25. Netemeyer RG, Bearden WO, Sharma S (2003) Scaling procedures: issues and applications. Sage Publications, Thousand OaksCrossRefGoogle Scholar
  26. Nora A (2003) Access to higher education for Hispanic students: real or illusory? In: Castellanos J, Jones L (eds) The majority in the minority: expanding representation of Latino/a faculty, administration and students in higher education. Stylus Publishing, Sterling, pp 47–67Google Scholar
  27. Nunnally JC (1978) Psychometric theory. McGraw-Hill, New YorkGoogle Scholar
  28. Pahl G, Beitz W (2013) Engineering design: a systematic approach. Springer Science & Business Media, BerlinGoogle Scholar
  29. Pascarella ET, Terenzini PT (1980) Predicting freshman persistence and voluntary dropout decisions from a theoretical model. J High Educ 51:60–75CrossRefGoogle Scholar
  30. Pérez D II, Ashlee KC, Do VH, Karikari SN, Sim C (2017) Re-conceptualizing student success in higher education: reflections from graduate student affairs educators using anti-deficit achievement framework. J Excell Coll Teach 28(3):5–28Google Scholar
  31. Schreiner LA, Juillerat SL (2010) Sample student satisfaction inventory: 4-year college or university version. Noel Levitt, Iowa CityGoogle Scholar
  32. Solano-Flores G, Nelson-Barber S (2001) On the cultural validity of science assessments. J Res Sci Teach 38(5):553–573CrossRefGoogle Scholar
  33. St. John EP, Paulsen MB, Carter DF (2005) Diversity, college costs, and postsecondary opportunity: an examination of the financial nexus between college choice and persistence for African Americans and Whites. J High Educ 76(5):545–569CrossRefGoogle Scholar
  34. Tabachnick BG, Fidell LS (2007) Using multivariate statistics, 5th edn. Allyn and Bacon, New YorkGoogle Scholar
  35. Tinto V (1993) Leaving college: rethinking the causes and cures of student attrition. The University of Chicago Press, ChicagoGoogle Scholar
  36. Tinto V (2006–2007) Research and practice of student retention: what next? J Coll Stud Retent 8(1):1–19CrossRefGoogle Scholar
  37. Tinto V (2010) From theory to action: exploring the institutional conditions for student retention. In: Smart J (ed) Higher education: handbook of theory and research, vol 25. Springer, DordrechtGoogle Scholar
  38. Tinto V, Pusser B (2006) Moving from theory to action: building a model of institutional action for student success. In: Commissioned paper presented at the 2006 Symposium of the National Postsecondary Education Cooperative (NPEC)Google Scholar
  39. U.S. Department of Education, National Center for Education Statistics (2017) The condition of education 2017 (NCES 2017-144), Undergraduate retention and graduation ratesGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Tracee Walker Gilbert
    • 1
  • Janis Terpenny
    • 2
    Email author
  • Tonya Smith-Jackson
    • 3
  1. 1.System Innovation LLCArlingtonUSA
  2. 2.The Pennsylvania State UniversityState CollegeUSA
  3. 3.North Carolina A&T State UniversityGreensboroUSA

Personalised recommendations