Top

TechTrends

Published in:

11-10-2022 | Original Paper

Supporting Dynamic Instructional Design Decisions Within a Bounded Rationality

Authors: Jill Stefaniak, Andrew Tawfik, Justin Sentz

Published in: TechTrends | Issue 2/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Various theories and models discuss how instructional designers can develop systems that allow learners to engage in problem-solving. To date, many of these theories and models that guide design often describe how learners engage in meaning-making within a situated context; however, they do not address strategies instructional designers can use to coordinate contextual factors impacting the environment. While these perspectives provide a more authentic view of action, they often overlook the design decision-making processes to support learning that occur within these situated environments. This makes it challenging to design learning systems that support complex interactions. Although studies have begun to emerge focusing on instructional design decisions, there is a need for a framework to guide how instructional designers engage in decision-making while designing for situated, real-world experiences. We then offer a theoretical design framework to facilitate design decision-making by conjecturing within a bounded rationality, exploring through analogical reasoning, and designing-in-action.

previous article The Process of Developing a Digital Repository for Online Teaching Using Design-Based Research

next article The Flipped Classroom Approach in High School Psychology: An Action Research Study

Ackermans, K., Rusman, E., Brand-Gruwel, S., & Specht, M. (2019). Solving instructional design dilemmas to develop a Video Enhanced Rubric with modeling examples to support mental model development of complex skills: The Viewbrics-project use case. Educational Technology Research and Development, 67(4), 983–1002. https://doi.org/10.1007/s11423-019-09668-1CrossRef

Alibali, M. W., & DiRusso, A. A. (1999). The function of gesture in learning to count: More than keeping track. Cognitive Development, 14(1), 37–56. https://doi.org/10.1016/S0885-2014(99)80017-3CrossRef

Alibali, M. W., & Nathan, M. J. (2012). Embodiment in mathematics teaching and learning: Evidence from learners’ and teachers’ gestures. Journal of the Learning Sciences, 21(2), 247–286. https://doi.org/10.1080/10508406.2011.611446CrossRef

Baaki, J., & Luo, T. (2017). Stimulating students’ use of external representations for a distance education time machine design. TechTrends, 61(4), 355–365. https://doi.org/10.1007/s11528-016-0155-zCrossRef

Baaki, J., & Tracey, M. W. (2019). Weaving a localized context of use: What it means for instructional design. Journal of Applied Instructional Design, 8(1), 2–13.

Baaki, J., Tracey, M. W., & Hutchinson, A. (2017). Give us something to react to and make it rich: Designers reflecting-in-action with external representations. International Journal of Technology and Design Education, 27(4), 667–682.CrossRef

Ball, L. J., & Christensen, B. T. (2009). Analogical reasoning and mental simulation in design: Two strategies linked to uncertainty resolution. Design Studies, 30(2), 169–186. https://doi.org/10.1016/j.destud.2008.12.005CrossRef

Ballard, D. H., Hayhoe, M. M., Pook, P. K., & Rao, R. P. N. (1997). Deictic codes for the embodiment of cognition. Behavioral and Brain Sciences, 20(4), 723–767. https://doi.org/10.1017/S0140525X97001611CrossRef

Banathy, B. H. (1997). Designing social systems in a changing world: Contemporary systems thinking. Springer.

Barsalou, L. W. (2014). Steering a course for embodied representation. In Cognitive dynamics: Conceptual and representational change in humans and machines (p. 51).

Bassok, M., & Holyoak, K. J. (1993). Pragmatic knowledge and conceptual structure: Determinants of transfer between quantitative domains. In D. K. Detterman & R. J. Sternberg (Eds.), Transfer on trial: Intelligence, cognition, and instruction (pp. 68–98). Ablex.

Beer, R. D. (2000). Dynamical approaches to cognitive science. Trends in Cognitive Sciences, 4(3), 91–99. https://doi.org/10.1016/S1364-6613(99)01440-0CrossRef

Bogard, T., Liu, M., & Chiang, Y. H. V. (2013). Thresholds of knowledge development in complex problem solving: A multiple-case study of advanced learners’ cognitive processes. Educational Technology Research and Development, 61(3), 465–503. https://doi.org/10.1007/s11423-013-9295-4CrossRef

Boshuizen, H. P. A., van de Wiel, M. W., & Schmidt, H. G. (2012). What and how advanced medical students learn from reasoning through multiple cases. Instructional Science, 40(5), 755–768.CrossRef

Branch, R. M., & Dousay, T. A. (2015). Survey of instructional design models (5th ed.). Association for Educational Communications and Technology.

Brouillet, T., Heurley, L., Martin, S., & Brouillet, D. (2010). The embodied cognition theory and the motor component of “yes” and “no” verbal responses. Acta Psychologica, 134(3), 310–317. https://doi.org/10.1016/j.actpsy.2010.03.003CrossRef

Brown, J. S., Collins, A., & Duguid, A. (1989). Situated cognition and the culture of learning. Educational Researcher, 18(1), 32–42. https://doi.org/10.3102/0013189X018001032CrossRef

Carlson, R. A., Avraamides, M. N., Cary, M., & Strasberg, S. (2007). What do the hands externalize in simple arithmetic? Journal of Experimental Psychology: Learning, Memory, and Cognition, 33(4), 747. https://doi.org/10.1037/0278-7393.33.4.747CrossRef

Christensen, B. T., & Schunn, C. D. (2007). The relationship of analogical distance to analogical function and preinventive structure: The case of engineering design. Memory & Cognition, 35(1), 29–38. https://doi.org/10.3758/BF03195939CrossRef

Christensen, B. T., & Schunn, C. D. (2009). The role and impact of mental simulation in design. Applied Cognitive Psychology, 23(3), 327–344. https://doi.org/10.1002/acp.1464CrossRef

Chu, M., & Kita, S. (2011). The nature of gestures’ beneficial role in spatial problem solving. Journal of Experimental Psychology: General, 140(1), 102. https://doi.org/10.1037/a0021790CrossRef

Churchman, C. W. (1965). On the design of educational systems. Audiovisual Instruction, 10(5), 361–365.

Clark, A. (1999). An embodied cognitive science? Trends in Cognitive Sciences, 3(9), 345–351. https://doi.org/10.1016/S1364-6613(99)01361-3CrossRef

Clark, A., & Grush, R. (1999). Towards a cognitive robotics. Adaptive Behavior, 7(1), 5–16.CrossRef

Cox, R. (1999). Representation construction, externalised cognition and individual differences. Learning and Instruction, 9(4), 343–363. https://doi.org/10.1016/S0959-4752(98)00051-6CrossRef

Crawford, C. (2004). Non-linear instructional design model: Eternal, synergistic design and development. British Journal of Educational Technology, 35(4), 413–420. https://doi.org/10.1111/j.0007-1013.2004.00400.xCrossRef

Custers, E. J. F. M., Boshuizen, H. P. A., & Schmidt, H. G. (1998). The role of illness scripts in the development of medical diagnostic expertise: Results from an interview study. Cognition and Instruction, 16, 367–398. https://doi.org/10.1207/s1532690xci1604_1CrossRef

Daalhuizen, J., Badke-Schaub, P., & Batill, S. (2009). Dealing with uncertainty in design practice: issues for designer-centered methodology. In 17th International Conference on Engineering Design. Design Society.

de Jong, T., & Lazonder, A. W. (2014). 15 The Guided Discovery Learning Principle in Multimedia Learning. The Cambridge handbook of multimedia learning (pp. 371-388). Cambridge University Press.

Ertmer, P. A., & Ottenbreit-Leftwich, A. (2013). Removing obstacles to the pedagogical changes required by Jonassen’s vision of authentic technology-enabled learning. Computers & Education, 64, 175–182. https://doi.org/10.1016/j.compedu.2012.10.008CrossRef

Foglia, L., & Wilson, R. A. (2013). Embodied cognition. Wiley Interdisciplinary Reviews: Cognitive Science, 4(3), 319–325.

Gallagher, R., & Appenzeller, T. (1999). Beyond reductionism. Science, 284(5411), 79–80.CrossRef

Ge, X., & Hardré, P. L. (2010). Self-processes and learning environment as influences in the development of expertise in instructional design. Learning Environments Research, 13(1), 23–41. https://doi.org/10.1007/s10984-009-9064-9CrossRef

Gibbons A. S., Boling E., & Smith K. M. (2014) Instructional Design Models. In: J. Spector, M. Merrill, J. Elen, & M. Bishop (Eds.), Handbook of Research on Educational Communications and Technology (4th ed., pp. 607–615). Springer.

Gick, M. L., & Holyoak, K. J. (1987). The cognitive basis of knowledge transfer. In S. M. Cormier & J. D. Hagman (Eds.), Transfer of learning: Contemporary research and applications (pp. 9–46). Academic Press.CrossRef

Gilbert, S. J. (2015). Strategic offloading of delayed intentions into the external environment. The Quarterly Journal of Experimental Psychology, 68(5), 971–992. https://doi.org/10.1080/17470218.2014.972963CrossRef

Glazewski, K. D., & Hmelo-Silver, C. E. (2019). Scaffolding and supporting use of information for ambitious learning practices. Information and Learning Sciences, 120(1/2), 39–58. https://doi.org/10.1108/ILS-08-2018-0087CrossRef

Goldin-Meadow, S., Nusbaum, H., Kelly, S. D., & Wagner, S. (2001). Explaining math: Gesturing lightens the load. Psychological Science, 12(6), 516–522. https://doi.org/10.1111/1467-9280.00395CrossRef

Gray, W. D., Sims, C. R., Fu, W. T., & Schoelles, M. J. (2006). The soft constraints hypothesis: A rational analysis approach to resource allocation for interactive behavior. Psychological Review, 113(3), 461. https://doi.org/10.1037/0033-295X.113.3.461CrossRef

Gray, C. M., Dagli, C., Demiral-Uzan, M., Ergulec, F., Tan, V., Altuwaijri, A. A., Gyabak, K., Hilligoss, M., Kizilboga, R., Tomita, K., & Boling, E. (2015). Judgment and instructional design: How ID practitioners work in practice. Performance Improvement Quarterly, 28(3), 25–49. https://doi.org/10.1002/piq.21198CrossRef

Greiff, S., Wüstenberg, S., Csapó, B., Demetriou, A., Hautamäki, J., Graesser, A. C., & Martin, R. (2014). Domain-general problem solving skills and education in the 21st century. Educational Research Review, 13, 74–83.CrossRef

Hall, A. D., & Fagen, R. E. (1975). Definition of system. In B. D. Ruben & J. Y. Kin (Eds.), General systems theory and human communications (pp. 52–65). Hayden Book Company Inc.

Hartling, L., Spooner, C., Tjosvold, L., & Oswald, A. (2010). Problem-based learning in pre-clinical medical education: 22 years of outcome research. Medical Teacher, 32(1), 28–35.CrossRef

Hmelo-Silver, C. E., Duncan, R. G., & Chinn, C. A. (2007). Scaffolding and achievement in problem-based and inquiry learning: A response to Kirschner, Sweller, and Clark. Educational Psychologist, 42(2), 99–107. https://doi.org/10.1080/00461520701263368CrossRef

Hoard, B., Stefaniak, J., Baaki, J., & Draper, D. (2019). The influence of multimedia development knowledge and workplace pressures on the design decisions of the instructional designer. Educational Technology Research and Development, 67(6), 1479–1505.CrossRef

Hoban, C. F. Jr. (1977). A systems approach to audiovisual communications. In L. W. Cochran (Ed.), Okoboji: A 20-year review of leadership 1955–1974 (pp. 67–72). Kendall/Hunt.

Hokanson, B., & Gibbons, A. S. (Eds.). (2013). Design in educational technology. Design thinking, design processes, and the design studio. Springer.

Hollan, J. D., & Hutchins, E. L. (2010). Opportunities and challenges for augmented environments: A distributed cognition perspective. In S. Lahlou (Ed.), Designing user friendly augmented work environments (pp. 237–259). Springer.

Hollands, F., & Escueta, M. (2019). How research informs educational technology decision-making in higher education: The role of external research versus internal research. Educational Technology Research and Development, 68(1), 163–180. https://doi.org/10.1007/s11423-019-09678-zCrossRef

Honebein, P. C. (2019). Exploring the galaxy question: The influence of situation and first principles on designers’ judgments about useful instructional methods. Educational Technology Research and Development, 67(3), 665–689. https://doi.org/10.1007/s11423-019-09660-9CrossRef

Huber, O. (1995). Complex problem solving as multistage decision-making. In P. A. Frensch & J. Funke (Eds.), Complex problem solving: The European perspective (pp. 151–173). Lawrence Erlbaum Associates.

Hughes, G., & Hay, D. (2001). Use of concept mapping to integrate the different perspectives of designers and other stakeholders in the development of e-learning materials. British Journal of Educational Technology, 32(5), 557–569. https://doi.org/10.1111/1467-8535.00225CrossRef

Hung, W. (2006). The 3C3R model: A conceptual framework for designing problems in PBL. Interdisciplinary Journal of Problem-Based Learning, 1(1), 55–77.CrossRef

Hutchins, E. (2006). The distributed cognition perspective on human interaction. In N. J. Enfield & S. C. Levinson (Eds.), Roots of human sociality: Culture, cognition and interaction (pp. 375–398). Berg.

Ifenthaler, D. (2010). Relational, structural, and semantic analysis of graphical representations and concept maps. Educational Technology Research and Development, 58(1), 81–97. https://doi.org/10.1007/s11423-008-9087-4CrossRef

Ifenthaler, D. (2011). Identifying cross-domain distinguishing features of cognitive structure. Educational Technology Research and Development, 59(6), 817–840. https://doi.org/10.1007/s11423-011-9207-4CrossRef

Jonassen, D. (1996). Scaffolding causal, diagnostic reasoning in a case-based learning environment in medicine. In D. C. Edelson & E. A. Domeshek (Eds.), International Conference on the Learning Sciences (pp. 439–444). Association for the Advancement of Computing in Education (AACE).

Jonassen, D. H. (1997). Instructional design models for well-structured and III-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94. https://doi.org/10.1007/BF02299613CrossRef

Jonassen, D. H. (2000). Toward a design theory of problem solving. Educational Technology Research and Development, 48(4), 63–85. https://doi.org/10.1007/BF02300500CrossRef

Jonassen, D. H. (2008). Instructional design as design problem solving: An iterative process. Educational Technology, 48(3), 21–26.

Jonassen, D. H. (2010). Learning to solve problems: A handbook for designing problem-solving learning environments. Routledge.CrossRef

Jonassen, D. (2011). Supporting problem solving in PBL. Interdisciplinary Journal of Problem-Based Learning, 5(2), 8.CrossRef

Jonassen, D. H. (2012). Designing for decision making. Educational Technology Research and Development, 60(2), 341–359. https://doi.org/10.1007/s11423-011-9230-5CrossRef

Jonassen, D. H., & Hung, W. (2008). All problems are not equal: Implications for pbl. Interdisciplinary Journal of Problem-Based Learning, 2(2), 10.CrossRef

Ju, H., & Choi, I. (2018). The role of argumentation in hypothetico-deductive reasoning during problem-based learning in medical education: A conceptual framework. Interdisciplinary Journal of Problem-Based Learning, 12(1).

Karpicke, J. D. (2009). Metacognitive control and strategy selection: Deciding to practice retrieval during learning. Journal of Experimental Psychology, 138(4), 469–486. https://doi.org/10.1037/a0017341CrossRef

Kirsh, D. (2010). Thinking with external representations. AI & Society, 25(4), 441–454. https://doi.org/10.1007/s00146-010-0272-8CrossRef

Kirsh, D., & Maglio, P. (1994). On distinguishing epistemic from pragmatic action. Cognitive Science, 18(4), 513–549. https://doi.org/10.1016/0364-0213(94)90007-8CrossRef

Koedinger, K. R., Alibali, M. W., & Nathan, M. J. (2008). Trade-offs between grounded and abstract representations: Evidence from algebra problem solving. Cognitive Science, 32(2), 366–397. https://doi.org/10.1080/03640210701863933CrossRef

Kolodner, J. L. (1991). The journal of the learning sciences: Effecting changes in education. The Journal of the Learning Sciences, 1(1), 1–6. https://doi.org/10.1207/s15327809jls0101_1CrossRef

Kolodner, J., Dorn, B., Owensby, J., & Guzdial, M. (2012). Theory and practice of case-based learning aids. In D. H. Jonassen, & S. Land (Eds.), Theoretical foundations of learning environments (2nd ed., pp. 142–170). Routledge.

Kool, W., McGuire, J. T., Rosen, Z. B., & Botvinick, M. M. (2010). Decision making and the avoidance of cognitive demand. Journal of Experimental Psychology, 139(4), 665–682. https://doi.org/10.1037/a0020198CrossRef

Lachheb, A., & Boling, E. (2021). The role of design judgment and reflection in instructional design. In J. K. McDonald & R. E. West (Eds.), Design for learning: Principles, processes, and praxis. EdTech Books. https://edtechbooks.org/id/design_judgment

Lakoff, G. (2012). Explaining embodied cognition results. Topics in Cognitive Science, 4(4), 773–785. https://doi.org/10.1111/j.1756-8765.2012.01222.xCrossRef

Landsiedel, J., & Gilbert, S. J. (2015). Creating external reminders for delayed intentions: Dissociable influence on “task-positive” and “task-negative” brain networks. NeuroImage, 104, 231–240. https://doi.org/10.1016/j.neuroimage.2014.10.021CrossRef

Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.CrossRef

Lazonder, A. W., & Harmsen, R. (2016). Meta-analysis of inquiry-based learning: Effects of guidance. Review of Educational Research, 86(3), 681–718. https://doi.org/10.3102/0034654315627366CrossRef

Lecoutre, M. P., Clement, E., & Lecoutre, B. (2004). Failure to construct and transfer correct representations across probability problems. Psychological Repeorts, 94(1), 151–162. https://doi.org/10.2466/pr0.94.1.151-162CrossRef

Loyens, S. M., Jones, S. H., Mikkers, J., & van Gog, T. (2015). Problem-based learning as a facilitator of conceptual change. Learning and Instruction, 38, 34–42. https://doi.org/10.1016/j.learninstruc.2015.03.002CrossRef

Marewski, J. N., & Schooler, L. J. (2011). Cognitive niches: An ecological model of strategy selection. Psychological Review, 118(3), 393. https://doi.org/10.1037/a0024143CrossRef

Marshall, J. G., Sollenberger, J., Easterby-Gannett, S., Morgan, L. K., Klem, M. L., Cavanaugh, S. K., ... & Hunter, S. (2013). The value of library and information services in patient care: results of a multisite study. Journal of the Medical Library Association: JMLA, 101(1), 38.

McNeil, S. (2015). Visualizing mental models: Understanding cognitive change to support teaching and learning of multimedia design and development. Educational Technology Research and Development, 63(1), 73–96. https://doi.org/10.1007/s11423-014-9354-5CrossRef

Mizikaci, F. (2006). A systems approach to program evaluation model for quality in higher education. Quality Assurance in Education, 14(1), 37–53. https://doi.org/10.1108/09684880610643601CrossRef

Murty, P. (2009) ‘Comparing paradigms with practice: The design conjecture cycle’. In N. Gu, M. J. Ostwald, & A. Williams (Eds.), Computing, Cognition and Education: Recent Research in the Architectural Sciences. ANZAScA, in association with The University of Newcastle.

Nikolaidis, E. (2005). Types of uncertainty in design decision making. In E. Nikolaidis, D. M. Ghiocel, & S. Singhal (Eds.), Engineering design reliability handbook (pp. 8–1–8–20). CRC Press.

Öllinger, M., Hammon, S., von Grundherr, M., & Funke, J. (2015). Does visualization enhance complex problem solving? The effect of causal mapping on performance in the computer-based microworld Tailorshop. Educational Technology Research and Development, 63(4), 621–637. https://doi.org/10.1007/s11423-015-9393-6CrossRef

Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptive strategy selection in decision making. Journal of Experimental Psychology, 14(3), 534–552. https://doi.org/10.1037/0278-7393.14.3.534CrossRef

Quayle, J. D., & Ball, L. J. (2000). Working memory, metacognitive uncertainty, and belief bias in syllogistic reasoning. The Quarterly Journal of Experimental Psychology, 53(4), 1202–1223. https://doi.org/10.1080/713755945CrossRef

Quintana, R. M., & Tan, Y. (2021). Visualizing course structure: Using course composition diagrams to reflect on design. Tech Trends, 65(4), 562–575. https://doi.org/10.1007/s11528-021-00592-xCrossRef

Rambusch, J., & Ziemke, T. (2005, July). The role of embodiment in situated learning. In Proceedings of the 27th Annual Conference of the Cognitive Science Society (pp. 1803–1808). Lawrence Erlbaum.

Randles, C. A., & Overton, T. L. (2015). Expert vs. novice: approaches used by chemists when solving open-ended problems. Chemistry Education Research and Practice, 16(4), 811–823.CrossRef

Richardson, M. J., & Chemero, A. (2014). Complex dynamical systems and embodiment. In L. Shapiro (Ed.), The Routledge handbook of embodied cognition (pp. 57–68). Routledge.

Risko, E. F., & Dunn, T. L. (2015). Storing information in-the-world: Metacognition and cognitive offloading in a short-term memory task. Consciousness and Cognition, 36, 61–74. https://doi.org/10.1016/j.concog.2015.05.014CrossRef

Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002CrossRef

Risko, E. F., Medimorec, S., Chisholm, J., & Kingstone, A. (2014). Rotating with rotated text: A natural behavior approach to investigating cognitive offloading. Cognitive Science, 38(3), 537–564. https://doi.org/10.1111/cogs.12087CrossRef

Rong, H., & Choi, I. (2019). Integrating failure in case-based learning: A conceptual framework for failure classification and its instructional implications. Educational Technology Research and Development, 67(3), 617–637.

Schank, R. (1999). Dynamic memory revisited (2nd ed.). Cambridge University Press.

Schlosser, J., & Paredis, C. J. J. (2007). Managing multiple sources of epistemic uncertainty in engineering decision making. Paper presented at SAE World Congress and Exhibition. Detroit, MI: SAE International.

Schnotz, W., & Kürschner, C. (2008). External and internal representations in the acquisition and use of knowledge: Visualization effects on mental model construction. Instructional Science, 36(3), 175–190. https://doi.org/10.1007/s11251-007-9029-2CrossRef

Schöner, G. (2008). Dynamical systems approaches to cognition. In J. P. Spencer, M. S. Thomas, & J. L. McLelland (Eds.), Toward a unified theory of development: Connectionism and dynamic systems theory re-considered (pp. 101–126). Oxford University Press.

Sentz, J., Stefaniak, J., Baaki, J., & Eckhoff, A. (2019). How do instructional designers manage learners’ cognitive load? An examination of awareness and application of strategies. Educational Technology Research and Development, 67(1), 199–245.CrossRef

Simon, H. A. (1969). The sciences of the artificial. The M.I.T. Press.

Sparrow, B., Liu, J., & Wegner, D. M. (2011). Google effects on memory: Cognitive consequences of having information at our fingertips. Science, 333(6043), 776–778.CrossRef

Stefaniak, J., Baaki, J., & Stapleton, L. (2022). An exploration of conjecture strategies used by instructional design students to support design decision-making. Educational Technology Research and Development, 1–29.

Storm, B. C., & Stone, S. M. (2015). Saving-enhanced memory: The benefits of saving on the learning and remembering of new information. Psychological Science, 26(2), 182–188. https://doi.org/10.1177/0956797614559285CrossRef

Tawfik, A., & Jonassen, D. (2013). The effects of successful versus failure-based cases on argumentation while solving decision-making problems. Educational Technology Research and Development, 61(3), 385–406.CrossRef

Tawfik, A. A., & Kolodner, J. L. (2016). Systematizing scaffolding for problem-based learning: A view from case-based reasoning. Interdisciplinary Journal of Problem-Based Learning, 10(1), 6.CrossRef

Tawfik, A. A., Rong, H., & Choi, I. (2015). Failing to learn: towards a unified design approach for failure-based learning. Educational Technology Research and Development, 63(6), 975–994.CrossRef

Tessmer, M. (1990). Environment analysis: A neglected stage of instructional design. Educational Technology Research and Development, 38(1), 55–64. https://doi.org/10.1007/BF02298248CrossRef

Tessmer, M., & Richey, R. C. (1997). The role of context in learning and instructional design. Educational Technology Research and Development, 45(2), 85–115. https://doi.org/10.1007/BF02299526CrossRef

Tessmer, M., & Wedman, J. F. (1990). A layers-of-necessity instructional development model. Educational Technology Research and Development, 38(2), 77–85. https://doi.org/10.1007/BF02298271CrossRef

Tracey, M. W., & Baaki, J. (2014). Design, designers, and reflection-in-action. In B. Hokanson, & Gibbons (Eds), Design in educational technology: Design thinking, design process, and the design studio (pp. 1–13). Springer.

Tracey, M. W., & Hutchinson, A. (2016). Uncertainty, reflection, and designer identity development. Design Studies, 42, 86–109. https://doi.org/10.1016/j.destud.2015.10.004CrossRef

Tracey, M. W., & Hutchinson, A. (2018). Uncertainty, agency and motivation in graduate design students. Thinking Skills and Creativity, 29, 196–202. https://doi.org/10.1016/j.tsc.2018.07.004CrossRef

Tracey, M. W., & Hutchinson, A. (2019). Empathic design: Imagining the cognitive and emotional learner experience. Educational Technology Research and Development, 67, 1259–1272. https://doi.org/10.1007/s11423-019-09683-2CrossRef

Turel, Y. K., & Johnson, T. E. (2012). Teachers’ belief and use of interactive whiteboards for teaching and learning. Educational Technology & Society, 15, 381–394.

Tversky, B. (2015). The cognitive design of tools of thought. Review of Philosophy and Psychology, 6(1), 99–116. https://doi.org/10.1007/s13164-014-0214-3CrossRef

Tzeng, J. Y., & Schwen, T. M. (2003). Mental representation-based task analysis for analyzing value-laden performance. Educational Technology Research and Development, 51(3), 5–21. https://doi.org/10.1007/BF02504550CrossRef

Visser, W. (1996). Two functions of analogical reasoning in design: A cognitive-psychology approach. Design Studies, 17(4), 417–434. https://doi.org/10.1016/S0142-694X(96)00020-8CrossRef

von Bertalanffy, L. (1968). General system theory: Foundations, development, applications. George Braziller.

von Bertalanffy, L. (1972). The history and status of general systems theory. The Academy of Management Journal, 15(4), 407–426. https://doi.org/10.5465/255139CrossRef

Wallace, B., Alastair, R., & Davies, J. (2003) Information processing models: Benefits and limitations. In P. Mc Cabe (Ed.), Contemporary ergonomics. Taylor and Francis.

Wilson, M. (2002). Six views of embodied cognition. Psychonomic Bulletin & Review, 9(4), 625–636. https://doi.org/10.3758/BF03196322CrossRef

Wosinski, J., Belcher, A. E., Dürrenberger, Y., Allin, A. C., Stormacq, C., & Gerson, L. (2018). Facilitating problem-based learning among undergraduate nursing students: A qualitative systematic review. Nurse Education Today, 60, 67–74. https://doi.org/10.1016/j.nedt.2017.08.015CrossRef

Zhang, J. (1991). The interaction of internal and external representations in a problem solving task. In Proceedings of the thirteenth annual conference of cognitive science society (pp. 88–91). Erlbaum.

Zhang, J. (1997). The nature of external representations in problem solving. Cognitive Science, 21(2), 179–217. https://doi.org/10.1016/S0364-0213(99)80022-6CrossRef

Zhang, J., & Patel, V. L. (2006). Distributed cognition, representation, and affordance. Pragmatics & Cognition, 14(2), 333–341. https://doi.org/10.1075/pc.14.2.12zhaCrossRef

Title: Supporting Dynamic Instructional Design Decisions Within a Bounded Rationality
Authors: Jill Stefaniak
Andrew Tawfik
Justin Sentz
Publication date: 11-10-2022
Publisher: Springer US
Published in: TechTrends / Issue 2/2023
Print ISSN: 8756-3894
Electronic ISSN: 1559-7075
DOI: https://doi.org/10.1007/s11528-022-00792-z

Springer Professional

Supporting Dynamic Instructional Design Decisions Within a Bounded Rationality

Abstract

Premium Partner

Springer Professional

Abstract

Please log in to get access to your license.

Other articles of this Issue 2/2023

A Validation of AI-Enabled Discussion Platform Metrics and Relationships to Student Efforts

Inclusion Possibilities

Peer Assessment Collaboration Evaluation: An Innovative Assessment Tool for Online Learning Environments

From Crayons to AI: Widening the Lens on Educational Technology and Creativity

Instructional Videos

Introducing the March 2023 Issue