1887
  1. Home
  2. A-Z Publications
  3. Engineering Education Letters
  4. Volume 2017, Issue 1
  5. Article
Volume 2017, Issue 1
  • EISSN: 2310-516X

Abstract

This study addresses the learning objectives and the student outcomes of industrial engineering students by examining them at three different levels: course level, program level, and graduate level. Three learning domains are developed and analyzed for this purpose to assess the performance of students during and after graduation. These domains are labeled as the house of cognitive learning, which shows the level of learning, its outcome elements, and the depth of understanding.

In the higher education system, the correct assessment of student learning is always considered as a challenging task. The aim of this study was to develop an integrated integer-programming algorithm to accurately determine the learning level of students. The method incorporates quality control charts and statistical assessment tools to present the findings. In this study, level of learning is calculated as a learning index that presents the contribution of a course to the respective student outcomes. Moreover, it depicts the overall achievements of students during their learning. Therefore, another aim of this study was to explore how to better utilize the collected data for the assessment of learning level. The outcomes of algorithm and statistical approaches are quite encouraging for the evaluation of students' learning, thus improving the quality of engineering program.

© 2017, Taylan, Rizwan, Parsaei, licensee HBKU Press.
Loading

Article metrics loading...

/content/journals/10.5339/eel.2017.2
2017-10-25
2024-11-09
Loading full text...

Full text loading...

/deliver/fulltext/eel/2017/1/eel.2017.2.html?itemId=/content/journals/10.5339/eel.2017.2&mimeType=html&fmt=ahah

References

  1. Anderson L. W.Krathwohl D.. 2001;. A Taxonomy for Learning, Teaching, and Assessing: A revision of Bloom's Taxonomy of Educational Objectives. http://thesecondprinciple.com/optimal-learning/brainbased-education-an-overview/ .
     [Google Scholar]
  2. Baird J.-A. 2013;. Judging students' performances. Assessment in Education: Principles, Policy & Practice, 20:, 247–249.
     [Google Scholar]
  3. Black P. 2015;. Formative assessment – An optimistic but incomplete vision. Assessment in Education: Principles, Policy & Practice, 22:, 161–177.
     [Google Scholar]
  4. Bloom B. S. 1956;. Taxonomy of Educational Objectives, Handbook I: The Cognitive Domain. New York, NY: David McKay Co Inc.
     [Google Scholar]
  5. Brookhart S. M. 2013;. The use of teacher judgement for summative assessment in the USA. Assessment in Education: Principles, Policy & Practice, 20:, 69–90.
     [Google Scholar]
  6. Dadach Z. E. 2013;. Quantifying the effect of an active learning strategy on the motivation of students. International Journal of Engineering Education, 29:, 904–913.
     [Google Scholar]
  7. Dominguez C., Nascimento C., Carreira R. P., Cruz G., Silva H., Lopes J.… Morais E. 2014;. Adding value to the learning process by online peer review activities: Towards the elaboration of a methodology to promote critical thinking in future engineers. European Journal of Engineering Education. Advance online publication. doi:10.1080/03043797.2014.987649.
     [Google Scholar]
  8. European Commission Using SOs 2011;. European Qualification Framework Series: Note 4, Luxembourg, 1–48.
  9. Freeman R.Dobbins K. 2013;. Are we serious about enhancing courses? Using the principles of assessment for learning to enhance course evaluation. Assessment & Evaluation in Higher Education, 38:, 142–151.
     [Google Scholar]
  10. Grez L. D.Valcke M. 2013;. Student response system and how to make engineering students learn oral presentation skills. International Journal of Engineering Education, 29:, 940–947.
     [Google Scholar]
  11. Grissom J. A.Loeb D. K. S. 2015;. Using student test scores to measure principal performance. Educational Evaluation and Policy Analysis, 37:, 3–28.
     [Google Scholar]
  12. Hargreaves E. 2007;. The validity of collaborative assessment for learning. Assessment in Education: Principles, Policy & Practice, 14:, 185–199.
     [Google Scholar]
  13. Hayward L. 2015;. Assessment is learning: The preposition vanishes. Assessment in Education: Principles, Policy & Practice, 22:, 27–43.
     [Google Scholar]
  14. Klenowski V., Askew S.Carnell E. 2006;. Portfolios for learning, assessment and professional development in higher education. Assessment & Evaluation in Higher Education, 31:, 267–286.
     [Google Scholar]
  15. Krathwohl D. R. 2002;. A revision of Bloom's taxonomy: An overview. Theory into Practice (Routledge), 41:, 212–218.
     [Google Scholar]
  16. Law C. K. 1995;. Using fuzzy numbers in educational grading system. Fuzzy Set and Systems, 83:, 311–323.
     [Google Scholar]
  17. Liu O. L., Frankel L.Roohr K. C. 2014;. Assessing critical thinking in higher education: Current state and directions for next-generation assessment. ETS Research Report Series, 1:, 1–23.
     [Google Scholar]
  18. Lopes A. L. M., Lanzer E. A.Barcia R. M. 1997;. Fuzzy cross-evaluation of the performance of academic departments within a university. Proceedings of the Canadian Institutional Research and Planning Association Conference, Toronto, Canada, October, 19–21.
     [Google Scholar]
  19. Ma J.Zhou D. 2000;. Fuzzy set approach to the assessment of student-centered learning. IEEE Transactions on Education, 43:, 237–241.
     [Google Scholar]
  20. Meyer J. H. F., Knight D. B., Callaghan D. P.Baldock T. E. 2014;. An empirical exploration of metacognitive assessment activities in a third-year civil engineering hydraulics course. European Journal of Engineering Education, Advance online publication. doi:10.1080/03043797.2014.96036.
     [Google Scholar]
  21. Northwood D. O. 2013;. SOs – some reflections on their value and potential drawbacks. World Transactions on Engineering and Technology Education, 11:, 137–142.
     [Google Scholar]
  22. Palermo J. 2011;. Linking student evaluations to institutional goals: A change story. Assessment & Evaluation in Higher Education, 38:, 211–223.
     [Google Scholar]
  23. Polikoff M. S.Porter A. C. 2014;. Instructional alignment as a measure of teaching quality. Educational Evaluation and Policy Analysis, 36:, 399–416.
     [Google Scholar]
  24. Reich A., Rooney D., Gardner A., Willey K., Boud D.Fitzgerald T. 2014;. Engineers' professional learning: A practice-theory perspective. European Journal of Engineering Education, Advance online publication. doi:10.1080/03043797.2014.967181.
     [Google Scholar]
  25. Rogers G. 2003;. Do grades make the grade for program assessment? Retrieved from www.uri.edu..
  26. Spelt E. J. H., Luning P. A., Boekel S.Mulder M. 2014;. Constructively aligned teaching and learning in higher education in engineering: What do students perceive as contributing to the learning of interdisciplinary thinking? European Journal of Engineering Education, Advance online publication. doi:10.1080/03043797.2014.987647.
     [Google Scholar]
  27. Svinicki M. 2005;. Evaluating and Grading Students. Center for Teaching Effectiveness. Austin, TX: The University of Texas at Austin.
     [Google Scholar]
  28. Tamburri R. 2013;. Trend to measure SOs gains proponents, Canadian University Affair, February 26, Article, Toronto.
  29. Taylan O.Karagozoglu B. 2009;. An adaptive neuro-fuzzy model for prediction of student's academic performance. Computers & Industrial Engineering, 57:, 732–741.
     [Google Scholar]
  30. Tian M.Lowe J. 2013;. The role of feedback in cross-cultural learning: A case study of Chinese taught postgraduate students in a UK university. Assessment & Evaluation in Higher Education, 38:, 580–598.
     [Google Scholar]
  31. Wang C. C., Kang Y., Chang Y. J.Chang Y. P. 2007;. Application of fuzzy neural networks for grading. 25th IASTED International Multi Conference, Artificial Intelligence & Applications, February 12–14 , 78–83.
     [Google Scholar]
/content/journals/10.5339/eel.2017.2
Loading
Optimization of engineering student learning and assessment by cognitive methods
Engineering Education Letters 2017, 2 (2017); https://doi.org/10.5339/eel.2017.2
/content/journals/10.5339/eel.2017.2
/content/journals/10.5339/eel.2017.2
Loading

Data & Media loading...

  • Article Type: Research Article
Keyword(s): Learning objectives, Learning quality, Qualitative assessment and Student outcomes

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error