Metacognition and Meta-assessment in Engineering Education

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Cognition, Metacognition, and Culture in STEM Education

Part of the book series: Innovations in Science Education and Technology ((ISET,volume 24))

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Abstract

In this chapter, we first discuss metacognition in engineering education. We then focus on meta-assessment in general and on student-oriented meta-assessment in engineering education in particular. We describe studies focusing on metacognition and the three meta-assessment types in engineering education. We describe in detail two studies, in which we have investigated the meta-assessment of engineering students at higher education institutes in two project-based courses with different characteristics. The first study involved a large undergraduate information systems engineering course at the Technion, Israel Institute of Technology, while the second study involved a small graduate model-based systems engineering course at Massachusetts Institute of Technology. We discuss the advantages of incorporating metacognition in general and meta-assessment in particular into engineering education and using it for enhancing students’ metacognitive skills. In this study, formative assessment was made paossible by providing feedback to the teams as they were engaged in the project-based learning. Our meta-assessment approach enables individual summative assessment of each student’s learning outcomes, whereas each team project served as a basis for the collective team's summative assessment.

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Notes

  1. 1.

    The terms personal, task, and strategy knowledge of cognition were coined by Flavell and Wellman (1977). Cross and Paris (1988) changed those terms to declarative, procedural, and conditional.

References

  • ABET. (2014). Criteria for accrediting engineering programs, 2015–2016. Retrieved 1 Jan 2015, from http://www.abet.org/eac-criteria-2015-2016/

  • Akoka, J., Comyn-Wattiau, I., & Cherfi, S.S.S. (2008). Quality of conceptual schemas an experimental comparison. In 2008 Second International Conference on Research Challenges in Information Science (pp. 197–208). IEEE. doi:10.1109/RCIS.2008.4632108

  • Anderson, L.W., Krathwohl, D.R., Airasian, P.W., Cruikshank, K.A., Mayer, R.E., Pintrich, P.R., … Wittrock, M.C. (2001). A taxonomy for learning, teaching, and assessing: A revision of bloom’s taxonomy of educational objectives, abridged edition. White Plains: Longman.

    Google Scholar 

  • Avargil, S., Lavi, R., & Dori, Y. J. (2018). Students’ metacognition and metacognitive strategies in science education. In Y. J. Dori, Z. Mevareach, & D. Bake (Eds.), Cognition, metacognition and culture in STEM education. Springer.

    Google Scholar 

  • Bedford, S., & Legg, S. (2007). Formative peer and self feedback as a catalyst for change within science teaching. Chemistry Education Research and Practice, 8(1), 80. doi:10.1039/b6rp90022d.

    Article  Google Scholar 

  • Boud, D. (1990). Assessment and the promotion of academic values. Studies in Higher Education, 15(1), 101–111. doi:10.1080/03075079012331377621.

    Article  Google Scholar 

  • Bransford, J. D., & Stein, B. S. (1993). The ideal problem solver: A guide to improving thinking, learning, and creativity (2nd ed.). New York: Freeman.

    Google Scholar 

  • Brodeur, D.R., Young, P.W., & Blair, K.B. (2002). Problem-based learning in aerospace engineering education. In Proceedings of the 2002 American society for engineering education annual conference and exposition Montreal, Canada (pp. 16–19).

    Google Scholar 

  • Brown, S., & Knight, P. (1994). Assessing learners in higher education. London: Kogan Page.

    Google Scholar 

  • Carr, R.L., & Strobel, J. (2012). Work in progress: Development of a metacognition scaffold in STEM/P-6 engineering context: MCinEDP. In Frontiers in education conference (FIE), 2012 (pp. 1–2). IEEE.

    Google Scholar 

  • Cheng, W., & Warren, M. (2000). Making a difference: Using peers to assess individual students’ contributions to a group project. Teaching in Higher Education, 5(2), 243–255. doi:10.1080/135625100114885.

    Article  Google Scholar 

  • Covert, S. (2012). OMG’s unified modeling language (UML) celebrates 15th anniversary. Retrieved from http://www.omg.org/news/releases/pr2012/08-01-12-a.htm

  • Crawley, E. F., Brodeur, D. R., & Soderholm, D. H. (2008). The education of euture aeronautical engineers: Conceiving, designing, implementing and operating. Journal of Science Education and Technology, 17(2), 138–151. doi:10.1007/s10956-008-9088-4.

    Article  Google Scholar 

  • Crawley, E. F., Malmqvist, J., Lucas, W. A., & Brodeur, D. R. (2011). The CDIO syllabus v2.0. an updated statement of goals for engineering education. In Proceedings of 7th international CDIO conference. Denmark. Retrieved from http://publications.lib.chalmers.se/records/fulltext/local_143186.pdf

  • Cross, D. R., & Paris, S. G. (1988). Developmental and instructional analyses of children’s metacognition and reading comprehension. Journal of Educational Psychology, 80(2), 131.

    Article  Google Scholar 

  • Cruz-Lemus, J. A., Genero, M., Manso, M. E. E., Morasca, S., & Piattini, M. (2009). Assessing the understandability of UML statechart diagrams with composite states – A family of empirical studies. Empirical Software Engineering, 14(6), 685–719. doi:10.1007/s10664-009-9106-z.

    Article  Google Scholar 

  • Cruz-Lemus, J. A., Maes, A., Genero, M., Poels, G., & Piattini, M. (2010). The impact of structural complexity on the understandability of UML statechart diagrams. Information Sciences, 180(11), 2209–2220. doi:10.1016/j.ins.2010.01.026.

    Article  Google Scholar 

  • De Graaff, E., & Christensen, H. P. (2004). Editorial: Theme issue on active learning in engineering education. European Journal of Engineering Education, 29(4), 461–463.

    Article  Google Scholar 

  • Dewey, J. (1934). Art as experience. New York: Minton, Balch and Company. Retrieved from http://dcg.mit.edu/wp-content/uploads/2011/10/DEWEY_expressiveObject.pdf.

    Google Scholar 

  • Dori, D. (2002a). Object-process methodology. Berlin: Berlin/Heidelberg. doi:10.1007/978-3-642-56209-9.

    Book  Google Scholar 

  • Dori, D. (2002b). Why significant UML change is unlikely. Communications of the ACM, 45(11), 82–85. doi:10.1145/581571.581599.

    Article  Google Scholar 

  • Dori, Y. J. (2003). From nationwide standardized testing to school-based alternative embedded assessment in Israel: Students’ performance in the matriculation 2000 project. Journal of Research in Science Teaching, 40(1), 34–52.

    Article  Google Scholar 

  • Dori, Y. J., & Sasson, I. (2013). A three-attribute transfer skills framework–part I: Establishing the model and its relation to chemical education. Chemistry Education Research and Practice, 14(4), 363–375.

    Google Scholar 

  • Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of Engineering Education, 94(1), 103–120.

    Article  Google Scholar 

  • Flavell, J. H., & Wellman, H. M. (1977). Metamemory. In R. V. Kail & J. W. Hagen (Eds.), Perspectives on the development of memory and cognition (pp. 3–33). Hillsdale: Erlbaum.

    Google Scholar 

  • Ford, C. L., & Yore, L. D. (2012). Toward convergence of critical thinking, metacognition, and reflection: Illustrations from natural and social sciences, teacher education, and classroom practice. In A. Zohar & Y. J. Dori (Eds.), Metacognition in science education (pp. 251–271). Dordrecht: Springer-Verlag.

    Chapter  Google Scholar 

  • Fulcher, K. H., & Good, M. R. (2013). The surprisingly useful practice of meta-assessment to title. [Web log post]. National Institute for Learning Outcomes Assessment. Retrieved from http://illinois.edu/blog/view/915/99344

  • Fulcher, K. H., Swain, M., & Orem, C. D. (2012). Expectations for assessment reports: A descriptive analysis. Assessment Update, 24(1), 1–16. doi:http://doi.wiley.com/10.1002/au.241.

    Article  Google Scholar 

  • Hadim, H. A., & Esche, S. K. (2002). Enhancing the engineering curriculum through project-based learning. In Frontiers in education, 2002. FIE 2002. 32nd Annual (Vol. 2, pp. F3F–1). IEEE.

    Google Scholar 

  • Herscovitz, O., Kaberman, Z., Saar, L., & Dori, Y. J. (2012). The relationship between metacognition and the ability to pose questions in chemical education. In A. Zohar & Y. J. Dori (Eds.), Metacognition in science education (pp. 165–195). Dordrecht: Springer-Verlag.

    Chapter  Google Scholar 

  • ISO. (2015). ISO/PAS 19450 –Automation systems and integration – Object-process methodology. Retrieved July 27, 2015, from http://www.iso.org/iso/catalogue_detail.htm?csnumber=62274/

  • Johri, A., & Olds, B. M. (2011). Situated engineering learning: Bridging engineering education research and the learning sciences. Journal of Engineering Education, 100(1), 151–185.

    Article  Google Scholar 

  • Kohen, Z., & Kramarski, B. (2018). Promoting mathematics teachers’ metacognition. In Y. J. Dori, Z. Mevareach, & D. Bake (Eds.), Cognition, metacognition and culture in STEM education. Springer.

    Google Scholar 

  • Kollar, I., & Fischer, F. (2010). Peer assessment as collaborative learning: A cognitive perspective. Learning and Instruction, 20(4), 344–348. doi:10.1016/j.learninstruc.2009.08.005.

    Article  Google Scholar 

  • Kuhn, D. (2000). Metacognitive development. Current Directions in Psychological Science, 9(5), 178–181.

    Article  Google Scholar 

  • Lawanto, O. (2009). Metacognition changes during an engineering design project. In Frontiers in education conference, 2009. FIE’09. 39th IEEE (pp. 1–5). IEEE.

    Google Scholar 

  • Lewis, P., Aldridge, D., & Swamidass, P. M. (1998). Assessing teaming skills acquisition on undergraduate project teams. Journal of Engineering Education, 87(2), 149–155.

    Article  Google Scholar 

  • Lin, X. (2001). Designing metacognitive activities. Educational Technology Research and Development, 49(2), 23–40.

    Article  Google Scholar 

  • Lindland, O. I., Sindre, G., & Solvberg, A. (1994). Understanding quality in conceptual modeling. IEEE Software, 11(2), 42–49. doi:10.1109/52.268955.

    Article  Google Scholar 

  • Liu, N.-F., & Carless, D. (2006). Peer feedback: The learning element of peer assessment. Teaching in Higher Education, 11(3), 279–290. doi:10.1080/13562510600680582.

    Article  Google Scholar 

  • McDonald, B. (2010). Improving learning through meta assessment. Active Learning in Higher Education, 11(2), 119–129. doi:10.1177/1469787410365651.

    Article  Google Scholar 

  • Mills, J. E., & Treagust, D. F. (2003). Engineering education – Is problem-based or project-based learning the answer? Australasian Journal of Engineering Education, 3, 2–16.

    Google Scholar 

  • MIT SDM. (2015). MIT system design & management (SDM). Retrieved July 27, 2015, from https://sdm.mit.edu/

  • Mohagheghi, P., & Aagedal, J. (2007). Evaluating quality in model-driven engineering. In International workshop on modeling in software engineering (MISE’07: ICSE Workshop 2007) (pp. 6–6). IEEE. doi:10.1109/MISE.2007.6.

  • Newell, J., Dahm, K., Harvey, R., & Newell, H. (2004). Develo** metacognitive engineering teams. Chemical Engineering Education, 38(4), 316–320.

    Google Scholar 

  • NSF. (1998). The action agenda for systemic engineering education reform – NSF 98–27. Retrieved from http://www.nsf.gov/pubs/1998/nsf9827/nsf9827.htm

  • Olds, B. M., Moskal, B. M., & Miller, R. L. (2005). Assessment in engineering education: Evolution, approaches and future collaborations. Journal of Engineering Education, 94(1), 13–25. doi:10.1002/j.2168-9830.2005.tb00826.x.

    Article  Google Scholar 

  • OMG SysML. (2015). Documents associated with systems modeling language (SysML), Version 1.3. Retrieved 27 Jul 2015, from http://www.omg.org/spec/SysML/1.3/

  • OMG UML. (2015). Unified modeling language™ (UML®) Version 2.5. Retrieved 27 Jul 2015, from http://www.omg.org/spec/UML/

  • Orem, C. D. (2012). Demonstrating validity evidence of meta-assessment scores using generalizability theory. Harrisonburg: James Madison University.

    Google Scholar 

  • Ory, J. C. (1992). Meta-assessment: Evaluating assessment activities. Research in Higher Education, 33(4), 467–481.

    Article  Google Scholar 

  • Peleg, M., & Dori, D. (2000). The model multiplicity problem: Experimenting with real-time specification methods. Software Engineering, IEEE Transactions, 26(6), 742–759.

    Article  Google Scholar 

  • Pintrich, P. R. (1999). The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research, 31(6), 459–470.

    Article  Google Scholar 

  • Popham, W. J. (2004). Curriculum, instruction, and assessment: Amiable allies or phony friends? The Teachers College Record, 106(3), 417–428.

    Article  Google Scholar 

  • Reinhartz-Berger, I., & Dori, D. (2005). OPM vs. UML: Experimenting with comprehension and construction of web application models. Empirical Software Engineering, 10(1), 57–80.

    Article  Google Scholar 

  • Resnick, L. B. (1987). Education and learning to think. Washington, DC: National Academy Press.

    Google Scholar 

  • Rugarcia, A., Felder, R. M., Woods, D. R., & Stice, J. E. (2000). The future of engineering education I. A vision for a new century. Chemical Engineering Education, 34(1), 16–25.

    Google Scholar 

  • Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting self-regulation in science education: Metacognition as part of a broader perspective on learning. Research in Science Education, 36(1–2), 111–139.

    Article  Google Scholar 

  • Schraw, G., Olafson, L., Weibel, M., & Sewing, D. (2012). Metacognitive knowledge and field-based science learning in an outdoor environmental education program. In A. Zohar & Y. J. Dori (Eds.), Metacognition in science education (pp. 57–88). Dordrecht: Springer-Verlag.

    Chapter  Google Scholar 

  • Schunk, D. H., & Zimmerman, B. J. (2003). Self regulation and learning. In W. M. Reynolds & G. E. Miller (Eds.), Handbook of psychology – volume 7 (pp. 59–78). Hoboken: Wiley.

    Google Scholar 

  • Selic, B. (2003). The pragmatics of model-driven development. Software, IEEE, 20(5), 19–25.

    Article  Google Scholar 

  • Sluijsmans, D. M. A., Brand-Gruwel, S., & van Merriënboer, J. J. G. (2002). Peer assessment training in teacher education: Effects on performance and perceptions. Assessment & Evaluation in Higher Education, 27(5), 443–454. doi:10.1080/0260293022000009311.

    Article  Google Scholar 

  • Thomas, D. (2004). MDA: Revenge of the modelers or UML utopia? IEEE Software, 21(3), 15–17.

    Article  Google Scholar 

  • Top**, K. J. (1998). Peer assessment between students in colleges and universities. Review of Educational Research, 68(3), 249–276.

    Article  Google Scholar 

  • Top**, K. J. (2010). Methodological quandaries in studying process and outcomes in peer assessment. Learning and Instruction, 20(4), 339–343. doi:10.1016/j.learninstruc.2009.08.003.

    Article  Google Scholar 

  • Van Zundert, M., Sluijsmans, D., & van Merriënboer, J. (2010). Effective peer assessment processes: Research findings and future directions. Learning and Instruction, 20(4), 270–279. doi:10.1016/j.learninstruc.2009.08.004.

    Article  Google Scholar 

  • Veenman, M. V. J. (2012). Metacognition in science education: Definitions, constituents, and their intricate relation with cognition. In A. Zohar & Y. J. Dori (Eds.), Metacognition in science education (pp. 21–36). Dordrecht: Springer-Verlag.

    Chapter  Google Scholar 

  • Vos, H., & De Graaff, E. (2004). Develo** metacognition: A basis for active learning. European Journal of Engineering Education, 29(4), 543–548.

    Article  Google Scholar 

  • Vrugt, A., & Oort, F. J. (2008). Metacognition, achievement goals, study strategies and academic achievement: Pathways to achievement. Metacognition and Learning, 3(2), 123–146.

    Article  Google Scholar 

  • Wengrowicz, N., Dori, Y. J., & Dori, D. (2014). Transactional distance in an undergraduate project-based systems modeling course. Knowledge-Based Systems, 71(6), 41–51. doi:10.1016/j.knosys.2014.05.022.

    Article  Google Scholar 

  • Zohar, A., & Dori, Y. J. (2003). Higher order thinking skills and low-achieving students: Are they mutually exclusive? The Journal of the Learning Sciences, 12(2), 145–181.

    Article  Google Scholar 

  • Zugal, S., **gera, J., Weber, B., Mendling, J., & Reijers, H. A. (2012). Assessing the impact of hierarchy on model understandability – A cognitive perspective. In J. Kienzle (Ed.), Models in software engineering. Berlin: Springer. doi:10.1007/978-3-642-29645-1_14.

    Chapter  Google Scholar 

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Correspondence to Niva Wengrowicz .

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Appendix: Students’ Reflections on the Course as a Whole

Appendix: Students’ Reflections on the Course as a Whole

Students’ reflections on the course as a whole in the large-scale undergraduate course revolved around the high demands of the course and the effort they had to spend on the one hand and the lack of familiarity with this kind of tasks on the other hand:

  • “The course requires a lot of work.”

  • “Where is the peace of mind? I invested in this course more than any other course of the semester.”

  • “Takes up a lot of time.”

  • “The course is a time and effort consumer.”

  • “The course is not an easy course; it required a very large investment relative to other courses I’ve taken to date.”

  • “Busy course relative to other courses.”

  • “High level of time and effort investment than any other course.”

  • “This is the first time I had to perform such a task.”

  • “We do not have adequate tools to analyze our peer projects.”

  • “I’m not sure that I have classified my findings to the appropriate categories since I have never done it before.”

Reflection on the course as a whole in the small-scale graduate course revolved around the learning of the two modeling languages in parallel. Students emphasized that this method helped them to better understand the uniqueness of each language:

  • “I thoroughly enjoyed this class. … not only the syntax of both modeling languages, but also how they compare to each other.”

  • “I like presenting both OPM and SysML in the class, not necessarily so I can efficiently use both, but so I could understand their differences, strengths and weaknesses.”

  • “I was able to develop a good understanding of the various types of modeling language over the course.”

  • “Very useful, and helped solidify understanding … when translating between the two modeling languages.”

  • “The hands-on session and converting other standard diagrams with class discussion are awesome experiences.”

  • “The exercises of converting other diagrams are really good ways to understand other diagrams and at the same time improve students’ understanding and skill.”

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Wengrowicz, N., Dori, Y.J., Dori, D. (2018). Metacognition and Meta-assessment in Engineering Education. In: Dori, Y.J., Mevarech, Z.R., Baker, D.R. (eds) Cognition, Metacognition, and Culture in STEM Education. Innovations in Science Education and Technology, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-66659-4_9

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