Engineering Education Research and Skills for Future Development: A Comparative Modelling Study of Curriculum Configurations Under Varying Institutional Capacity

References

[1] ABET Engineering Accreditation Commission. Criteria for accrediting engineering programs. Baltimore, MD: ABET; 2023.

[2] International Engineering Alliance. Graduate attributes and professional competencies, version 4. Wellington: International Engineering Alliance; 2021.

[3] Ministry of Education, Government of India. National Education Policy 2020. New Delhi: Government of India; 2020.

[4] World Economic Forum. The future of jobs report 2023. Geneva: World Economic Forum; 2023.

[5] Freeman S, Eddy SL, McDonough M, Smith MK, Okoroafor N, Jordt H, et al. Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences. 2014;111(23):8410–8415. https://doi.org/10.1073/pnas.1319030111

[6] Prince M. Does active learning work? A review of the research. Journal of Engineering Education. 2004;93(3):223–231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x

[7] Strobel J, van Barneveld A. When is PBL more effective? A meta-synthesis of meta-analyses comparing PBL to conventional classrooms. Interdisciplinary Journal of Problem-Based Learning. 2009;3(1):44–58. https://doi.org/10.7771/1541-5015.1046

[8] Dochy F, Segers M, Van den Bossche P, Gijbels D. Effects of problem-based learning: A meta-analysis. Learning and Instruction. 2003;13(5):533–568. https://doi.org/10.1016/S0959-4752(02)00025-7

[9] Chen J, Kolmos A, Guerra A. Implementation and challenges of problem-based learning in higher education: A scoping review. European Journal of Engineering Education. 2021;46(1):90–115. https://doi.org/10.1080/03043797.2020.1718615

[10] Borrego M, Douglas EP, Amelink CT. Quantitative, qualitative, and mixed research methods in engineering education. Journal of Engineering Education. 2009;98(1):53–66. https://doi.org/10.1002/j.2168-9830.2009.tb01005.x

[11] National Academy of Engineering. The engineer of 2020: Visions of engineering in the new century. Washington, DC: National Academies Press; 2004.

[12] Sheppard SD, Macatangay K, Colby A, Sullivan WM. Educating engineers: Designing for the future of the field. San Francisco, CA: Jossey-Bass; 2008.

[13] Passow HJ, Passow CH. What competencies should undergraduate engineering programs emphasize? A systematic review. Journal of Engineering Education. 2017;106(3):475–526. https://doi.org/10.1002/jee.20171

[14] Male SA, Bush MB, Chapman ES. An Australian study of generic competencies required by engineers. European Journal of Engineering Education. 2011;36(2):151–163. https://doi.org/10.1080/03043797.2011.569703

[15] Smith KA, Sheppard SD, Johnson DW, Johnson RT. Pedagogies of engagement: Classroom-based practices. Journal of Engineering Education. 2005;94(1):87–101. https://doi.org/10.1002/j.2168-9830.2005.tb00831.x

[16] Felder RM, Silverman LK. Learning and teaching styles in engineering education. Engineering Education. 1988;78(7):674–681.

[17] Crawley EF, Malmqvist J, Östlund S, Brodeur DR, Edström K. Rethinking engineering education: The CDIO approach. 2nd ed. Cham: Springer; 2014.

[18] Graham R. The global state of the art in engineering education. Cambridge, MA: Massachusetts Institute of Technology; 2018.

[19] Winberg C, Bramhall M, Greenfield D, Johnson P, Rowlett P, Lewis O, et al. Developing employability in engineering education: A systematic review of the literature. European Journal of Engineering Education. 2020;45(2):165–180. https://doi.org/10.1080/03043797.2018.1534086

[20] Black P, Wiliam D. Assessment and classroom learning. Assessment in Education: Principles, Policy and Practice. 1998;5(1):7–74. https://doi.org/10.1080/0969595980050102

[21] Hattie J. Visible learning: A synthesis of over 800 meta-analyses relating to achievement. London: Routledge; 2009.

[22] Litzinger TA, Lattuca LR, Hadgraft RG, Newstetter WC. Engineering education and the development of expertise. Journal of Engineering Education. 2011;100(1):123–150. https://doi.org/10.1002/j.2168-9830.2011.tb00006.x

[23] Kolb DA. Experiential learning: Experience as the source of learning and development. Englewood Cliffs, NJ: Prentice Hall; 1984.

[24] Vygotsky LS. Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press; 1978.

[25] Jamieson LH, Lohmann JR. Creating a culture for scholarly and systematic innovation in engineering education. Washington, DC: American Society for Engineering Education; 2009.

[26] Cohen J. Statistical power analysis for the behavioral sciences. 2nd ed. Hillsdale, NJ: Lawrence Erlbaum Associates; 1988.

[27] Kendall MG, Babington Smith B. The problem of m rankings. Annals of Mathematical Statistics. 1939;10(3):275–287. https://doi.org/10.1214/aoms/1177732186

[28] Friedman M. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association. 1937;32(200):675–701. https://doi.org/10.1080/01621459.1937.10503522

[29] Wilcoxon F. Individual comparisons by ranking methods. Biometrics Bulletin. 1945;1(6):80–83. https://doi.org/10.2307/3001968

[30] Holm S. A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics. 1979;6(2):65–70.

@article{1724916,
      author = {Navneet Kumar, Palak Jain, Shrashti Jaiswal, Tushar Sehrawat},
      title = {Engineering Education Research and Skills for Future Development: A Comparative Modelling Study of Curriculum Configurations Under Varying Institutional Capacity},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {5},
      pages = {2615-2637},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1724916.pdf},
      abstract = {Engineering graduates are entering a workplace where knowing the technical fundamentals is no longer enough. Employers increasingly look for graduates who can solve unfamiliar problems, make sound design decisions, work confidently with digital tools, communicate clearly, and continue learning without constant supervision. This paper describes a controlled modelling study that compares four curriculum approaches: a lecture-centred baseline, active learning augmentation, project-based learning organised around CDIO principles, and industry-integrated project-based learning. All four are assessed using the same seven-domain competency framework, developed from accreditation criteria and published evidence on employer skill needs. To keep the comparison fair, the same simulated learner cohort is passed through every approach rather than generating a different cohort for each one. Three hundred cohorts are simulated under each of three institutional capacity conditions: well-resourced, moderately-resourced, and constrained. The study reports a composite competency attainment index, the percentage of the employer-referenced gap that is closed, a simulated delivery-cost index, ninety-five percent confidence intervals, and matched non-parametric statistical tests. Under well-resourced settings, the model gives mean attainment values of 60.11 for the baseline, 62.81 for active learning augmentation, 64.85 for project-based learning, and 65.97 for industry-integrated project-based learning, with Kendall's W = 0.429 and p < 0.001 in the Friedman test. As institutional resources become constrained, however, this ordering becomes less distinct. In the constrained scenario, the advantage of industry-integrated delivery falls to 2.70 index points, and the difference from project-based learning is no longer statistically separable after Holm adjustment at the 0.05 level. Active learning augmentation also retains much of its benefit while requiring considerably less simulated cost. The sensitivity analysis places the crossover between industry-integrated and project-based learning at about eighty-five students per staff member. Beyond that point, the most resource-intensive approach no longer produces the highest simulated attainment. These numbers come from a model input-based simulation, not from observations of an actual programme or student cohort. The aim is therefore to make the underlying reasoning transparent and repeatable, and to show when the additional effort required by high-contact teaching is supported by the model.},
      keywords = {Engineering education research, competency development, future skills, project-based learning, CDIO, active learning, work-integrated learning, accreditation outcomes, curriculum modelling, employability},
      month = {November},
  }