Abstract
One of the greatest mistakes instructional designers make is to create instruction based on simplistic strategies without giving much thought to a systematic overarching framework. The benefit of an overarching framework for any instruction is that there will be coherency and consistency in the design, planning, implementation, and evaluation. In this chapter, we argue for the importance of problem solving as the centre of instructional design in the identified high-stakes learning contexts and provide a variety of instructional design guidelines. Moreover, assessing learners’ performance is one of the most important – if not the most important – component of instruction. To meaningfully assess performance, instructional designers need to clearly identify the descriptors of the required actions or thoughts and align these with the learning outcomes. These descriptors usually take the form of rubrics, and rubrics can be used to observe learners’ performance and assess their articulation of their thoughts. In this chapter, we discuss the important elements of rubrics and the components of rubrics in relation to the conditions of various high-stakes learning environments.
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References
Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. Artificial Intelligence Communications, 7, 39–59.
Alison, L., Van den Heuvel, C., Power, S. W. N., Long, A., O’Hara, T., & Crego, J. (2013). Immersive simulated learning environments for researching critical incidents. Journal of Cognitive Engineering and Decision Making, 7, 255–272. https://doi.org/10.1177/1555343412468113
Berentson, L. (2007). Using rubrics for assessing student projects in FAR part 147 programs. Collegiate Aviation Review, 25, 18–29.
Bjork, R. A., Dunlosky, J., & Kornell, N. (2013). Self-regulated learning: Beliefs, techniques, and illusions. Annual Review of Psychology, 64, 417–444. https://doi.org/10.1146/annurev-psych-113011-143823
Borko, H. (1997). New forms of classroom assessment: Implications for staff development. Theory Into Practice, 36, 231–238. https://doi.org/10.1080/00405849709543773
Boulton, L., & Cole, J. (2016). Adaptive flexibility: Examining the role of adaptive expertise in the decision making of authorized firearms officers during armed confrontation. Journal of Cognitive Engineering and Decision Making, 10, 291–308. https://doi.org/10.1177/1555343416646684
Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn: Brain, mind, experience and school (pp. 3–23). Washington, DC: National Academy Press.
Brehmer, B. (1992). Dynamic decision making: Human control of complex systems. Acta Psychologica, 81, 211–241. https://doi.org/10.1016/0001-6918(92)90019-A
Brookhart, S. (2003). Developing measurement theory for classroom assessment purposes and uses. Educational Measurement: Issues and Practice, 22, 5–12. https://doi.org/10.1111/j.1745-3992.2003.tb00139.x
Busemeyer, J. R. (2002). Dynamic decision making. In N. J. Smelser & P. B. Bates (Eds.), International encyclopedia of the social and behavioral sciences: Methodology, mathematics and computer science (pp. 3903–3908). Oxford, UK: Elsevier.
Cyr, P., Smith, K., Broyles, I., & Holt, C. (2014). Developing, evaluating and validating a scoring rubric for written case reports. International Journal of Medical Education, 5, 18–23. https://doi.org/10.5116/ijme.52c6.d7ef
Dougan, A. M. (1996). Student assessment by portfolio: One institution’s journey. They History Teacher, 29, 171–178. https://doi.org/10.2307/494738
Dunbar, N., Brooks, C., & Kubicka-Miller, T. (2006). Oral communication skills in higher education: Using a performance-based evaluation rubric to assess communication skills. Innovative Higher Education, 31, 115–128. https://doi.org/10.1007/s10755-006-9012-x
Fiore, S. M., Ross, K., & Jentsch, F. (2012). A team cognitive readiness framework for small unit training. Journal of Cognitive Engineering and Decision Making, 6, 325–349. https://doi.org/10.1177/1555343412449626
Fraser, K., Huffman, J., Ma, I., Sobczak, M., McIlwrick, J., Wright, B., et al. (2014). The emotional and cognitive impact of unexpected simulated patient death: A randomized controlled trial. Chest, 145, 958–963.
Fraser, K., Ma, I., Teteris, E., Baxter, H., Wright, B., & McLaughlin, K. (2012). Emotion, cognitive load and learning outcomes during simulation training. Medical Education, 46, 1055–1062.
Glaser, R., & Chi, M. T. H. (1988). Overview. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.), The nature of expertise (pp. xv–xxviii). Hillsdale, MI: Erlbaum.
Glasspool, D. W., & Fox, J. (2005). Knowledge, argument, and meta-cognition in routine decision making. In T. Betsch & S. Haberstroh (Eds.), The routines of decision making (pp. 343–358). New York: Psychology Press.
Harenčárová, H. (2017). Managing uncertainty in paramedics’ decision making. Journal of Cognitive Engineering and Decision Making, 11, 42–62. https://doi.org/10.1177/1555343416674814
Hernandez-Serrano, J., & Jonassen, D. H. (2013). The effects of case libraries on problem solving. Journal of Computer Assisted Learning, 19, 103–114. https://doi.org/10.1046/j.0266-4909.2002.00010.x
Hoffman, R. R., & Klein, G. L. (2017). Challenges and prospects for the paradigm of naturalistic decision making. Journal of Cognitive Engineering and Decision Making, 11, 97–104. https://doi.org/10.1177/1555343416689646
Jonassen, D. H. (2004). Learning to solve problems: An instructional design guide. San Francisco: Pfeiffer.
Jonassen, D. H. (2007). Learning to solve complex scientific problems. Mahwah, NJ: Erlbaum.
Jonassen, D. H. (2011). Learning to solve problems: A handbook for designing problem-solving learning environments. New York: Routledge.
Jonassen, D. H., Howland, J., Moore, J., & Marra, R. M. (2003). Learning to solve problems with technology: A constructivist perspective. Columbus, OH: Merrill.
Kalyuga, S., & Hanham, J. (2011). Instructing in generalized knowledge structures to develop flexible problem solving skills. Computers in Human Behavior, 27, 63–68. https://doi.org/10.1016/j.chb.2010.05.024
Kalyuga, S., & Singh, A. M. (2015). Rethinking the boundaries of cognitive load theory in complex learning. Educational Psychology Review, 2015. https://doi.org/10.1007/s10648-015-9352-0
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26, 379–424. https://doi.org/10.1080/07370000802212669
Kapur, M. (2011). A further study of productive failure in mathematical problem solving: Unpacking the design components. Instructional Science, 39, 561–579. https://doi.org/10.1007/s11251-010-9144-3
Kapur, M. (2014). Productive failure in learning math. Cognitive Science, 38, 1008–1022. https://doi.org/10.1111/cogs.12107
Kapur, M., & Rummel, N. (2012). Productive failure in learning from generation and invention activities. Instructional Science, 40, 645–650. https://doi.org/10.1007/s11251-012-9235-4
Klein, G. (2008). Naturalistic decision making. Human Factors, 50, 456–460. https://doi.org/10.1518/001872008X288385
Klein, G., Calderwood, R., & Clinton-Cirocco, A. (2010). Rapid decision making on the ground: The original study plus a postscript. Journal of Cognitive Engineering and Decision Making, 4, 186–209. https://doi.org/10.1518/155534310X12844000801203
Klein, G. A., & Hoffman, R. (1993). Seeing the invisible: Perceptual/cognitive aspects of expertise. In M. Rabinowitz (Ed.), Cognitive science foundations of instruction (pp. 203–226). Mahwah, NJ: Erlbaum.
Lafleur, A., Côté, L., & Leppink, J. (2015). Influences of OSCE design on students’ diagnostic reasoning. Medical Education, 49, 203–214. https://doi.org/10.1111/medu.12635
Lajoie, S. P. (2003). Transitions and trajectories for studies of expertise. Educational Researcher, 32, 21–25. https://doi.org/10.3102/0013189X032008021
Leppink, J. (2017). Cognitive load theory: Practical implications and an important challenge. Journal of Taibah University Medical Sciences, 12, 385–391. 10/1016/j.jtumed.2017.05.003
Leppink, J., & Duvivier, R. (2016). Twelve tips for medical curriculum design from a cognitive load theory perspective. Medical Teacher, 38, 669–674. https://doi.org/10.3109/0142159X.2015.1132829
Leppink, J., & Van den Heuvel, J. (2015). The evolution of cognitive load theory and its application to medical education. Perspectives on Medical Education, 4, 119–127. https://doi.org/10.1007/s40037-015-0192-x
Leppink, J., Van Gog, T., Paas, F., & Sweller, J. (2015). Cognitive load theory: Researching and planning teaching to maximise learning. In J. Cleland & S. J. Durning (Eds.), Researching medical education, Chapter 18 (pp. 207–218). Chichester, UK: Wiley & Blackwell.
Lipshitz, R., & Shaul, O. B. (1997). Schemata and mental models in recognition-primed decision making. In C. E. Zsambok & G. Klein (Eds.), Expertise: Research and applications. Naturalistic decision making (pp. 293–303). Hillsdale, MI: Erlbaum.
Nitko, A. (2001). Educational assessment of students (3rd ed.). Upper Saddle River, NJ: Prentice Hall.
Orasanu, J., & Connolly, T. (1993). The reinvention of decision making. In G. A. Klein, J. Orasanu, R. Calderwood, & C. E. Zsambok (Eds.), Decision making in action: Models and methods (pp. 3–20). Norwood, NJ: Ablex.
Patterson, R., Pierce, B., Bell, H. H., Andrews, D., & Winterbottom, M. (2009). Training robust decision making in immersive environments. Journal of Cognitive Engineering and Decision Making, 3, 331–361. https://doi.org/10.1518/155534309X12599553478836
Pfaff, M. S., Klein, G. L., Drury, J. L., Moon, S. P., Liu, Y., & Entezari, S. (2013). Supporting complex decision making through option awareness. Journal of Cognitive Engineering and Decision Making, 7, 123–140. https://doi.org/10.1177/1555343412455799
Schmidt, H. G. (1983). Problem-based learning: Rationale and description. Medical Education, 17, 11–16. https://doi.org/10.1111/j.1365-2923.1983.tb01086.x
Smith, M. U. (1991). A view from biology. In M. U. Smith (Ed.), Toward a unified theory of problem solving (pp. 1–20). Hillsdale, NJ: Erlbaum.
Tremblay, M. L., Lafleur, A., Leppink, J., & Dolmans, D. H. J. M. (2017). The simulated clinical environment: Cognitive and emotional impact among undergraduates. Medical Teacher, 39, 181–187. https://doi.org/10.1080/0142159X.2016.1246710
Van Merriënboer, J. J. G., & Kirschner, P. A. (2018). Ten steps to complex learning (3rd ed.). New York: Routledge.
Van Merriënboer, J. J. G., & Sweller, J. (2010). Cognitive load theory in health professions education: Design principles and strategies. Medical Education, 44, 85–93. https://doi.org/10.1111/j.1365-2923.2009.03498.x
Vygotsky, L. S. (1978). Mind in society: Development of higher psychological processes. Boston: Harvard University Press.
Williams, J. (2002). The engineering portfolio: Communication, reflection, and student learning outcomes assessment. International Journal of Engineering Education, 18, 197–207.
Yudkowsky, R., Otaki, J., Lowenstein, T., Riddle, J., Nishigori, H., & Bordage, G. (2009). A hypothesis-driven physical examination learning and assessment procedure for medical students: Initial validity evidence. Medical Education, 43, 729–740.
Zimmerman, B., & Schunk, D. (2001). Self-regulated learning and academic achievement: Theoretical perspectives. Mahwah, NJ: Erlbaum.
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Lee, C.B., Leppink, J., Hanham, J. (2019). On the Design of Instruction and Assessment. In: Instructional Design Principles for High-Stakes Problem-Solving Environments. Springer, Singapore. https://doi.org/10.1007/978-981-13-2808-4_11
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