Higher Education

, Volume 73, Issue 3, pp 479–497 | Cite as

Insights on supporting learning during computing science and engineering students’ transition to university: a design-oriented, mixed methods exploration of instructor and student perspectives

  • Sheryl Guloy
  • Farimah Salimi
  • Diana Cukierman
  • Donna McGee Thompson


Using a design-based orientation, this mixed-method study explored ways to support computing science and engineering students whose study strategies may be inadequate to meet coursework expectations. Learning support workshops, paired with university courses, have been found to assist students as they transition to university learning, thereby contributing to lower attrition rates. Unfortunately, at-risk students are less likely to attend paired learning support initiatives. To broaden participation, incentives can be provided to all students. However, doing so entails that learning support workshops provide students, in general, with relevant insights on learning. Our first research question involved determining the kind of learning support deemed valuable within the discipline by juxtaposing students’ perceptions of their coursework challenges, study strategies, motivation, and attitudes with instructors’ expectations for student learning. Aligned with a design-based orientation, our second research question explored those aspects of learning that the design of our learning support workshop should address. One hundred fifty-four students responded to an online questionnaire and five instructors were interviewed. Our findings provided us with insights on disciplinary learning, which are to be supported by our workshop design. Specifically, the meta-inference themes of give it a real try; disciplinary craft; and learn from/with others reflect aspects of learning that computing science and engineering students are encouraged to develop. We recommend future research into instructors’ disciplinary learning beliefs and how paired learning support can be designed to initiate first-year students into those aspects of learning valued by their respective disciplinary fields.


Postsecondary STEM education Mixed methods research Design-based research Transitional programs Instructor beliefs Student needs 


  1. Arendale, D. (2002). History of supplemental instruction (SI): mainstreaming of developmental education. In D. B. Lundell and J. L. Higbee (Eds.). Histories of Developmental Education. (pp. 15-27). Minneapolis, MN: Center for Research on Developmental Education and Urban Literacy, General College, University of Minnesota.Google Scholar
  2. Barab, S., & Squire, K. (2004). Design-based research: putting a stake in the ground. J Learn Sci, 13(1), 1–14.Google Scholar
  3. Bennett, R. (2003). Determinants of undergraduate student drop out rates in a university studies department. J Furth High Educ, 27(2), 123–141.CrossRefGoogle Scholar
  4. Brown, A. (1992). Design experiments: theoretical and methodological challenges in creating complex interventions in classroom settings. J Learn Sci, 2(2), 141–178.Google Scholar
  5. Camargo, P.,Curione, K., & Míguez, M. (2008). Estrategias de aprendizaje: aportes de la psicología cognitiva a la enseñanza de la matemática. [Learning strategies: Bringing cognitive psychology to mathematics instruction.] IX Jornadas de Psicología Universitaria. Psicolibros, Waslala, Montevideo. http://www.bvspsi.org.uy/local/TextosCompletos/udelar/99748126592008IX1.pdf. Accessed 30 Sept. 2015.
  6. Chan, V. (2001). Learning autonomously: the learners’ perspectives. J Furth High Educ, 25(3), 285–300.CrossRefGoogle Scholar
  7. Charmaz, K. (2008). Constructionism and the grounded theory method. In J. A. Holstein & J. F. Gubrium (Eds.), Handbook of constructionist research (pp. 397–412). New York: The Guilford Press.Google Scholar
  8. Cook, A., & Leckey, J. (1999). Do expectations meet reality? A survey of changes in first-year student opinion. J Furth High Educ, 23(2), 157–171.CrossRefGoogle Scholar
  9. Cukierman, D., McGee Thomson, D., Guloy, S., Salimi, F., & Karpilovsky, M. (2014). Addressing challenges students face in first-year university computing science and engineering science courses: overview of a needs assessment and workshop. In Proceedings of the Western Canadian Conference on Computing Education, ACM, doi: 10.1007/s001090000086.
  10. Drisko, J. W., & Maschi, T. (2016). Content analysis. New York: Oxford University Press.Google Scholar
  11. Egan, R., Cukierman, D., & McGee Thompson, D. (2011, June). The academic enhancement program in introductory CS: a workshop framework description and evaluation. In Proceedings of the 16th Annual Joint Conference on Innovation and Technology in Computer Science Education, 278–282. ACM.Google Scholar
  12. Greene, J. C., Benjamin, L., & Goodyear, L. (2001). The merits of mixing methods in evaluation. Evaluation, 7(1), 25–44.CrossRefGoogle Scholar
  13. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6).Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
  14. Halcomb, E. J., & Davidson, P. M. (2006). Is verbatim transcription of interview data always necessary? Appl Nurs Res, 19(1), 38–42.CrossRefGoogle Scholar
  15. Higbee, J. L., Arendale, D. R., & Lundell, D. B. (2005). Using theory and research to improve access and retention in developmental education. New Directions for Community Colleges, 2005(129), 5–15.CrossRefGoogle Scholar
  16. Jones, T., & Cuthrell, K. (2011). YouTube: educational potentials and pitfalls. Comput Sch, 28(1), 75–85.CrossRefGoogle Scholar
  17. Kesici, S., & Erdoğan, A. (2009). Predicting college students’ mathematics anxiety by motivational beliefs and self regulated learning strategies. Coll Stud J, 43(2), 631–642.Google Scholar
  18. Klassen, A. C., Creswell, J., Plano Clark, V. L., Clegg Smith, K., & Meissner, H. I. (2012). Best practices in mixed methods for quality of life research. Qual Life Res, 21(3), 377–380.CrossRefGoogle Scholar
  19. Levitz, R. S., Noel, L., & Richter, B. J. (1999). Strategic moves for retention success. N Dir High Educ, 1999(108), 31–49.CrossRefGoogle Scholar
  20. Lord, S. M., Prince, M. J., Stefanou, C. R., Stolk, J. D., & Chen, J. C. (2012). The effect of different active learning environments on student outcomes related to lifelong learning. Int J Eng Educ, 28(3), 606–620.Google Scholar
  21. Lowe, H. & Cook, A. (2003). Mind the gap: are students prepared for higher education? J Furth High Educ, 27(1), 53-76.Google Scholar
  22. Malm, J., Bryngfors, L., & Mörner, L. L. (2012). Supplemental instruction for improving first year results in engineering studies. Stud High Educ, 37(6), 655–666.CrossRefGoogle Scholar
  23. McCarthy, A., Smuts, B., & Cosser, M. (1997). Assessing the effectiveness of supplemental instruction: a critique and a case study. Stud High Educ, 22(2), 221–231.CrossRefGoogle Scholar
  24. National Audit Office (2002). Improving student achievement in English Higher Education. Report by theComptroller and Auditor General. London: The Stationery Office. https://www.nao.org.uk/report/improving-student-achievement-in-english-higher-education. Accessed 26 Jan. 2016.
  25. Onwuegbuzie, A. J., Slate, J. R., Leech, N. L., & Collins, K. M. (2009). Mixed data analysis: advanced integration techniques. International Journal of Multiple Research Approaches, 3(1), 13–33.CrossRefGoogle Scholar
  26. Onwuegbuzie, A. J., Burke Johnson, R., & Collins, K. M. T. (2011). Assessing legitimation in mixed research: a new framework. Quality & Quantity, 45(6), 1253–1271.CrossRefGoogle Scholar
  27. Peshkin, A. (1988). In search of subjectivity—one’s own. Educ Res, 17(7), 17–21.Google Scholar
  28. Pintrich, P., Smith, D., Garcia, T. & McKeachie, W. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). Ann Arbor, MI: NCRIPTAL, School of Education, University of Michigan.Google Scholar
  29. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am Psychol, 55(1), 68–78.CrossRefGoogle Scholar
  30. Sandoval, W. (2014). Conjecture mapping: an approach to systematic educational design research. J Learn Sci, 23(1), 18–36.Google Scholar
  31. SFU Admission Averages page (2015). Fall 2015 Secondary School Averages. Resource document. Simon Fraser University. https://www.sfu.ca/students/admission-requirements/admission-averages.html. Accessed 20 Dec. 2015.
  32. Tashakkori, A. & Teddlie, C. (2008). Quality of inferences in mixed methods research: calling for an integrative framework. In M. M. Bergman, Advances in Mixed Methods Research: Theories and Applications (pp. 101–120). London: 2008.Google Scholar
  33. Taylor, D. V. (2002). Supporting the research mission. In D. B. Lundell and J. L. Higbee, Histories of Developmental Education. (pp. 7–10). Minneapolis, MN: Center for Research on Developmental Education and Urban Literacy, General College, University of Minnesota.Google Scholar
  34. Teddlie, C., & Tashakkori, A. (2009). Foundations of mixed methods research: Integrating quantitative and qualitative approaches in the social and behavioural sciences. Thousand Oaks, CA:Sage Publications.Google Scholar
  35. Tinto, V. (2005). Taking student success seriously: rethinking the first year of college. In ninth annual intersession academic affairs forum. Fullerton: California State University.Google Scholar
  36. University of Missouri-Kansas City. (2013).National supplemental instruction report fall 2002–2013. Resource document. http://www.umkc.edu/asm/si/si-docs/National%20Data%20updated%20slides_09-13-2013.pdf. Accessed 30 Sept. 2015.
  37. Virtanen, V., & Lindblom-Ylänne, S. (2010). University students’ and teachers’ conceptions of teaching and learning in the biosciences. Instr Sci, 38(4), 355–370.CrossRefGoogle Scholar
  38. Wang, F., & Hannafin, M. J. (2005). Design-based research and technology-enhanced learning environments. Educ Technol Res Dev, 53(4), 5–23.CrossRefGoogle Scholar
  39. Webster, T. J., & Dee, K. C. (1998). Supplemental instruction integrated into an introductory engineering course. J Eng Educ, 87(4), 377–383.CrossRefGoogle Scholar
  40. Widmar, G. E. (1994). Supplemental instruction: from small beginnings to a national program. New Directions for Teaching and Learning, (60), 3–10.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  • Sheryl Guloy
    • 1
  • Farimah Salimi
    • 1
  • Diana Cukierman
    • 2
  • Donna McGee Thompson
    • 3
  1. 1.Faculty of EducationSimon Fraser UniversityBurnabyCanada
  2. 2.School of Computing ScienceSimon Fraser UniversityBurnabyCanada
  3. 3.Student Learning CommonsSimon Fraser UniversityBurnabyCanada

Personalised recommendations