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What can we learn from recommendations of early-career engineers? Assessing computing and software engineering education using a career monitoring survey

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Published:01 September 2022Publication History

ABSTRACT

This paper presents an analysis of the skills and professional competencies that recent graduates from computing and software engineering programmes recommend for current students. Previous studies have not investigated the viewpoints of early-career engineers, and the current study addresses this research gap. The data used in this study comes from nationwide career monitoring surveys for former university students who graduated five years earlier. We analyzed the responses to questions about the skills and competencies needed in the software or computing jobs and compared them with the satisfaction and career paths of the respondents. According to the results, three types of skills and competencies are paramount: Soft skills in general, programming skills, and the practical experience gained during university studies. A logistic regression analysis revealed that soft skills are recommended by those who are most satisfied with their careers. Practical skills are more likely to be recommended if the respondent is less satisfied with their studies. Based on the findings, we concluded that the responses from the career monitoring survey could be used as an indicator of how well studies prepare graduates for the industry.

References

  1. Faheem Ahmed, Luiz Fernando Capretz, and Piers Campbell. 2012. Evaluating the demand for soft skills in software development. It Professional 14, 1 (2012), 44–49.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Mark Ardis, David Budgen, Gregory W Hislop, Jeff Offutt, Mark Sebern, and Willem Visser. 2015. SE 2014: Curriculum guidelines for undergraduate degree programs in software engineering. Computer11(2015), 106–109.Google ScholarGoogle Scholar
  3. Andrew Begel and Nachiappan Nagappan. 2008. Pair programming: what’s in it for me?. In Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement. 120–128.Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Pierre Bourque, Richard E Fairley, 2014. Guide to the software engineering body of knowledge (SWEBOK (R)): Version 3.0. IEEE Computer Society Press.Google ScholarGoogle Scholar
  5. Vahid Garousi, Görkem Giray, Eray Tüzün, Cagatay Catal, and Michael Felderer. 2019. Aligning software engineering education with industrial needs: a meta-analysis. Journal of Systems and Software 156 (2019), 65–83.Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Vahid Garousi, Gorkem Giray, Eray Tuzun, Cagatay Catal, and Michael Felderer. 2019. Closing the gap between software engineering education and industrial needs. IEEE Software 37, 2 (2019), 68–77.Google ScholarGoogle ScholarCross RefCross Ref
  7. Michael Hewner and Mark Guzdial. 2010. What game developers look for in a new graduate: interviews and surveys at one game company. In Proceedings of the 41st ACM technical symposium on Computer science education. 275–279.Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. David W Hosmer Jr, Stanley Lemeshow, and Rodney X Sturdivant. 2013. Applied logistic regression. Vol. 398. John Wiley & Sons.Google ScholarGoogle ScholarCross RefCross Ref
  9. Amy J Ko, Robert DeLine, and Gina Venolia. 2007. Information needs in collocated software development teams. In 29th International Conference on Software Engineering (ICSE’07). IEEE, 344–353.Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Paul Luo Li, Amy J Ko, and Andrew Begel. 2020. What distinguishes great software engineers?Empirical Software Engineering 25, 1 (2020), 322–352.Google ScholarGoogle Scholar
  11. Paul Luo Li, Amy J Ko, and Jiamin Zhu. 2015. What makes a great software engineer?. In 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol. 1. IEEE, 700–710.Google ScholarGoogle ScholarCross RefCross Ref
  12. Stephanie Lunn and Monique Ross. [n. d.]. Ready to Work: Evaluating the Role of Community Cultural Wealth during the Hiring Process in Computing. ([n. d.]).Google ScholarGoogle Scholar
  13. Stephanie J Lunn and Monique S Ross. 2021. Cracks in the Foundation: Issues with Diversity and the Hiring Process in Computing Fields. In 2021 ASEE Virtual Annual Conference Content Access.Google ScholarGoogle Scholar
  14. Herbert W Marsh. 1987. Students’ evaluations of university teaching: Research findings, methodological issues, and directions for future research. International journal of educational research 11, 3 (1987), 253–388.Google ScholarGoogle Scholar
  15. Gerardo Matturro, Florencia Raschetti, and Carina Fontán. 2015. Soft skills in software development teams: A survey of the points of view of team leaders and team members. In 2015 IEEE/ACM 8th International Workshop on Cooperative and Human Aspects of Software Engineering. IEEE, 101–104.Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Gerardo Matturro, Florencia Raschetti, and Carina Fontán. 2019. A systematic mapping study on soft skills in software engineering. JUCS-Journal of Universal Computer Science 25 (2019), 16.Google ScholarGoogle Scholar
  17. Maria Papoutsoglou, Apostolos Ampatzoglou, Nikolaos Mittas, and Lefteris Angelis. 2019. Extracting knowledge from on-line sources for software engineering labor market: A mapping study. IEEE Access 7(2019), 157595–157613.Google ScholarGoogle ScholarCross RefCross Ref
  18. James S Pounder. 2007. Is student evaluation of teaching worthwhile?Quality Assurance in Education(2007).Google ScholarGoogle Scholar
  19. Johnny Saldaña. 2021. The coding manual for qualitative researchers. sage.Google ScholarGoogle Scholar
  20. Pieter Spooren. 2010. On the credibility of the judge: A cross-classified multilevel analysis on students’ evaluation of teaching. Studies in educational evaluation 36, 4 (2010), 121–131.Google ScholarGoogle ScholarCross RefCross Ref
  21. Pieter Spooren, Bert Brockx, and Dimitri Mortelmans. 2013. On the validity of student evaluation of teaching: The state of the art. Review of Educational Research 83, 4 (2013), 598–642.Google ScholarGoogle ScholarCross RefCross Ref
  22. Bob Uttl, Carmela A White, and Daniela Wong Gonzalez. 2017. Meta-analysis of faculty’s teaching effectiveness: Student evaluation of teaching ratings and student learning are not related. Studies in Educational Evaluation 54 (2017), 22–42.Google ScholarGoogle ScholarCross RefCross Ref
  23. Sergey Voitenko, Lydmila Gadasina, and Lene Sørensen. 2018. The need for soft skills for ph. d.’s in software engineering. In CEUR Workshop Proceedings, Vol. 2256. CEUR Workshop Proceedings.Google ScholarGoogle Scholar
  24. Howard K Wachtel. 1998. Student evaluation of college teaching effectiveness: A brief review. Assessment & Evaluation in Higher Education 23, 2 (1998), 191–212.Google ScholarGoogle ScholarCross RefCross Ref

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      cover image ACM Other conferences
      UKICER '22: Proceedings of the 2022 Conference on United Kingdom & Ireland Computing Education Research
      September 2022
      90 pages
      ISBN:9781450397421
      DOI:10.1145/3555009

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      • Published: 1 September 2022

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