Abstract
In this exploratory study, we investigated students’ design thinking strategies during a challenge involving the design of an energy-efficient house. We used the Informed Design Teaching and Learning Matrix as a framework for characterizing the students’ design thinking, focusing on four specific strategies—generating ideas, conducting experiments, revising and iterating, and troubleshooting. To elicit the use of design thinking strategies, we employed two pedagogical approaches—tell-and-practice (T&P) and contrasting cases (CC)—as conditions in a within-subjects design, where participants were exposed to one approach first and then the other. Findings suggest that students exposed to T&P then CC had more balanced use of all four design strategies as compared to the students exposed to CC first then T&P. Regarding changes in strategies used, there was a significant increase in conducting experiments, but a significant decrease in troubleshooting, after students were exposed to both approaches. This finding suggests that students spent more time experimenting and understanding how the system works rather than focusing on problematic areas and finding solutions to the problems they faced during the design process. Implications of the study include recommendations for using T&P and CC to elicit design strategies during design thinking.
Similar content being viewed by others
References
Akerlind, G. (2015). From phenomenography to variation theory: A review of the development of the variation theory of learning and implications for pedagogical design in higher education. HERDSA Review of Higher Education, 2, 5–26.
Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383–398.
Arastoopour, G., Shaffer, D. W., Swiecki, Z., Ruis, A. R., & Chesler, N. C. (2016). Teaching and assessing engineering design thinking with virtual internships and epistemic network analysis. International Journal of Engineering Education, 32(2), 1492–1501.
Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379.
Ball, L. J., & Christensen, B. T. (2019). Advancing an understanding of design cognition and design metacognition: Progress and prospects. Design Studies, 65, 35–59.
Becker, K., & Mentzer, N. (2015). Engineering design thinking: High school students’ performance and knowledge. In: Proceedings of the 2015 International Conference on Interactive Collaborative Learning (ICL), pp. 5–12.
Belenky, D. M., & Nokes-Malach, T. J. (2012). Motivation and transfer: The role of mastery-approach goals in preparation for future learning. Journal of the Learning Sciences, 21(3), 399–432. https://doi.org/10.1080/10508406.2011.651232
Boling, E. (2010). The need for design cases: Disseminating design knowledge. International Journal of Designs for Learning, 1, 1.
Brown, P. (2009). CAD: Do Computers aid the design process after all? Intersect.
Bussey, T. J., Orgill, M., & Crippen, K. J. (2013). Variation theory: A theory of learning and a useful theoretical framework for chemical education research. Chemistry Education Research and Practice, 14(1), 9–22.
Cagan, J., Dinar, M., Shah, J. J., Leifer, L., Linsey, J., Smith, S., & Vargas-Hernandez, N. (2013). Empirical studies of design thinking: Past, present, future. International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 55928, V005T06A020.
Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127(4), 355.
Charness, G., Gneezy, U., & Kuhn, M. A. (2012). Experimental methods: Between-subject and within-subject design. Journal of Economic Behavior and Organization, 81(1), 1–8.
Cobern, W. W., Schuster, D., Adams, B., Applegate, B., Skjold, B., Undreiu, A., Loving, C. C., & Gobert, J. D. (2010). Experimental comparison of inquiry and direct instruction in science. Research in Science and Technological Education, 28(1), 81–96. https://doi.org/10.1080/02635140903513599
Cramer-Petersen, C. L., Christensen, B. T., & Ahmed-Kristensen, S. (2019). Empirically analysing design reasoning patterns: Abductive-deductive reasoning patterns dominate design idea generation. Design Studies, 60, 39–70.
Crismond, D. P., & Adams, R. S. (2012). The informed design teaching and learning matrix. Journal of Engineering Education, 101(4), 738–797. https://doi.org/10.1002/j.2168-9830.2012.tb01127.x
Cross, N. (2001). Design cognition: Results from protocol and other empirical studies of design activity. In Design knowing and learning: Cognition in design education (pp. 79–103). Elsevier.
Cross, N. (2011). Design thinking: Understanding how designers think and work. Berg.
Daly, S. R., Yilmaz, S., Christian, J. L., Seifert, C. M., & Gonzalez, R. (2012). Design heuristics in engineering concept generation.
Daly, S. R., Seifert, C. M., Yilmaz, S., & Gonzalez, R. (2016). Comparing Ideation Techniques for Beginning Designers. Journal of Mechanical Design. https://doi.org/10.1115/1.4034087
Damle, A., & Smith, P. J. (2009). Biasing cognitive processes during design: The effects of color. Design Studies, 30(5), 521–540.
Dorst, K. (2011). The core of ‘design thinking’and its application. Design Studies, 32(6), 521–532.
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.
Ericson, Å., Bergström, M., Larsson, A., & Törlind, P. (2009). Design thinking challenges in education. International Conference on Engineering Design: 24/08/2009–27/08/2009, 89–100.
Everitt, B., & Skrondal, A. (2002). The Cambridge dictionary of statistics (Vol. 106). Cambridge University Press, Cambridge.
Goldschmidt, G. (2014). Linkography: Unfolding the design process. MIT Press.
Goldstein, M. H., Purzer, Ş., Mejia, C. V., Zielinski, M., & Douglas, K. A. (2015). Assessing idea fluency through the student design process. In: Proceedings of the 2015 IEEE Frontiers in Education Conference (FIE), pp. 1–5.
Groover, M. P., & Zimmers, E. W. (1983). CAD/CAM: Computer-aided design and manufacturing. Pearson Education.
Hekkenberg, A. (2012). Addressing misconceptions about electric and magnetic fields: A variation theory analysis of a lecture’s learning space [Master’s Thesis].
Honey, M. A., & Hilton, M. L. (2011). Learning science through computer games. National Academies Press.
Höök, K., & Löwgren, J. (2012). Strong concepts: Intermediate-level knowledge in interaction design research. ACM Transactions on Computer-Human Interaction (TOCHI), 19(3), 1–18.
Jääskeläinen, R. (2002). Think-aloud protocol studies into translation: An annotated bibliography. Target International Journal of Translation Studies, 14(1), 107–136.
Jaiswal, A., Karabiyik, T., Thomas, P., & Magana, A. J. (2021a). Characterizing team orientations and academic performance in cooperative project-based learning environments. Education Sciences, 11(9), 520. https://doi.org/10.3390/educsci11090520
Jaiswal, A., Lyon, J. A., Zhang, Y., & Magana, A. J. (2021b). Supporting student reflective practices through modelling-based learning assignments. European Journal of Engineering Education, 46(6), 987–1006. https://doi.org/10.1080/03043797.2021.1952164
Johansson-Sköldberg, U., Woodilla, J., & Çetinkaya, M. (2013). Design thinking: Past, present and possible futures. Creativity and Innovation Management, 22(2), 121–146.
Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93(3), 579.
Kaufman, L., & Rousseeuw, P. J. (2009). Finding groups in data: An introduction to cluster analysis (Vol. 344). Wiley.
Keengwe, J., Pearson, D., & Smart, K. (2009). Technology integration: Mobile devices (iPods), constructivist pedagogy, and student learning. AACE Journal.
Koehler, M. J., Mishra, P., & Cain, W. (2013). What is technological pedagogical content knowledge (TPACK)? Journal of Education. https://doi.org/10.1177/002205741319300303
Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., Puntambekar, S., & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design™ into practice. Journal of the Learning Sciences, 12(4), 495–547. https://doi.org/10.1207/S15327809JLS1204_2
Kruger, C., & Cross, N. (2006). Solution driven versus problem driven design: Strategies and outcomes. Design Studies, 27(5), 527–548. https://doi.org/10.1016/j.destud.2006.01.001
Lawson, B., & Dorst, K. (2013). Design expertise. Routledge.
Lee, W. C., Neo, W. L., Chen, D.-T., & Lin, T.-B. (2021). Fostering changes in teacher attitudes toward the use of computer simulations: Flexibility, pedagogy, usability and needs. Education and Information Technologies, 26(4), 4905–4923. https://doi.org/10.1007/s10639-021-10506-2
Loibl, K., Roll, I., & Rummel, N. (2017). Towards a theory of when and how problem solving followed by instruction supports learning. Educational Psychology Review, 29(4), 693–715.
Loibl, K., Tillema, M., Rummel, N., & van Gog, T. (2020). The effect of contrasting cases during problem solving prior to and after instruction. Instructional Science, 48(2), 115–136. https://doi.org/10.1007/s11251-020-09504-7
Magana, A. J., Jaiswal, A., Madamanchi, A., Parker, L. C., Gundlach, E., & Ward, M. D. (2021). Characterizing the psychosocial effects of participating in a year-long residential research-oriented learning community. Current Psychology, 1–18.
Marton, F. (1986). Phenomenography—A research approach to investigating different understandings of reality. Journal of Thought, 28–49.
Marton, F., & Pang, M. F. (2007). Connecting student learning and classroom teaching through the variation framework. Biennial Conference for Research on Learning and Instruction.
Marton, F. (1981). Phenomenography—Describing conceptions of the world around us. Instructional Science, 10(2), 177–200.
Marton, F. (2006). Sameness and difference in transfer. The Journal of the Learning Sciences, 15(4), 499–535.
Marton, F., & Booth, S. A. (1997). Learning and awareness. Psychology Press.
Medová, J., & Bakusová, J. (2019). Application of hierarchical cluster analysis in educational research: Distinguishing between transmissive and constructivist oriented mathematics teachers. Staticstical on the Staticstics Economics Journal, 99, 142–150.
Mok, I. A. C. (2009). In search of an exemplary mathematics lesson in Hong Kong: An algebra lesson on factorization of polynomials. ZDM Mathematics Education, 41(3), 319–332.
Nathan, M. J. (2012). Rethinking formalisms in formal education. Educational Psychologist, 47(2), 125–148.
Nielsen, F. (2016). Hierarchical clustering. In Introduction to HPC with MPI for Data Science (pp. 195–211). Springer.
Open Broadcaster Software®|OBS. (2018). Retrieved September 10, 2020, from https://obsproject.com/
Orgill, M. (2012). Variation theory. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 3391–3393). Springer, New York. doi:https://doi.org/10.1007/978-1-4419-1428-6_272
Owen, C. (2007). Design thinking: Notes on its nature and use. Design Research Quarterly, 2(1), 16–27.
Pang, M. F., & Marton, F. (2003). Beyond“lesson study”: Comparing two ways of facilitating the grasp of some economic concepts. Instructional Science, 31(3), 175–194.
Peterson, B. E., & Williams, S. R. (2008). Learning mathematics for teaching in the student teaching experience: Two contrasting cases. Journal of Mathematics Teacher Education, 11(6), 459–478. https://doi.org/10.1007/s10857-008-9085-9
Price, P. C., Jhangiani, R. S., Chiang, I. A., Leighton, D. C., & Cuttler, C. (2017). Research Methods in Psychology (3rd American). PressBooksPublications.
Purzer, S., & Quintana-Cifuentes, J. P. (2019). Integrating engineering in K-12 science education: Spelling out the pedagogical, epistemological, and methodological arguments. Disciplinary and Interdisciplinary Science Education Research, 1(1), 13.
Razzouk, R., & Shute, V. (2012a). What is design thinking and why is it important? Review of Educational Research, 82(3), 330–348.
Razzouk, R., & Shute, V. (2012b). What is design thinking and why is it important? Review of Educational Research. https://doi.org/10.3102/0034654312457429
Rokach, L., & Maimon, O. (2005). Clustering methods. In Data mining and knowledge discovery handbook (pp. 321–352). Springer.
Rotherham, A. J., & Willingham, D. (2009). To work, the 21st century skills movement will require keen attention to curriculum, teacher quality, and assessment. Educational Leadership, 9(1), 15–20.
Runesson, U. (2005). Beyond discourse and interaction. Variation: A critical aspect for teaching and learning mathematics. Cambridge Journal of Education, 35(1), 69–87.
Schaafstal, A., Schraagen, J. M., & Van Berl, M. (2000). Cognitive task analysis and innovation of training: The case of structured troubleshooting. Human Factors, 42(1), 75–86.
Schalk, L., Schumacher, R., Barth, A., & Stern, E. (2018). When problem-solving followed by instruction is superior to the traditional tell-and-practice sequence. Journal of Educational Psychology, 110(4), 596.
Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–5223.
Schwartz, D. L., Chase, C. C., & Bransford, J. D. (2012). Resisting overzealous transfer: Coordinating previously successful routines with needs for new learning. Educational Psychologist, 47(3), 204–214.
Schwartz, D. L., Chase, C. C., Oppezzo, M. A., & Chin, D. B. (2011). Practicing versus inventing with contrasting cases: The effects of telling first on learning and transfer. Journal of Educational Psychology, 103(4), 759.
Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction. Cognition and Instruction, 22(2), 129–184.
Seah, Y. Y., & Magana, A. J. (2019). Exploring students’ experimentation strategies in engineering design using an educational CAD tool. Journal of Science Education and Technology, 28(3), 195–208.
Smetana, L. K., & Bell, R. L. (2012). Computer Simulations to Support Science Instruction and Learning: A critical review of the literature. International Journal of Science Education, 34(9), 1337–1370. https://doi.org/10.1080/09500693.2011.605182
Stigler, J. W., & Hiebert, J. (2004). Improving mathematics teaching. Educational Leadership, 61(5), 12–17.
Van Gog, T., Paas, F., & Van Merriënboer, J. J. (2005). Uncovering expertise-related differences in troubleshooting performance: Combining eye movement and concurrent verbal protocol data. Applied Cognitive Psychology, 19(2), 205–221.
Vieira, C., Seah, Y. Y., & Magana, A. J. (2018). Students’ experimentation strategies in design: Is process data enough? Computer Applications in Engineering Education, 26(5), 1903–1914.
Ward, J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244.
Wieman, C. E., Adams, W. K., & Perkins, K. K. (2008). Physics. PhET: Simulations that enhance learning. In Science. doi:https://doi.org/10.1126/science.1161948
Wrigley, C., & Straker, K. (2017). Design thinking pedagogy: The educational design ladder. Innovations in Education and Teaching International, 54(4), 374–385.
Xie, C., Schimpf, C., Chao, J., Nourian, S., & Massicotte, J. (2018a). Learning and teaching engineering design through modeling and simulation on a CAD platform. Computer Applications in Engineering Education. https://doi.org/10.1002/cae.21920
Xie, C., Schimpf, C., Chao, J., Nourian, S., & Massicotte, J. (2018b). Learning and teaching engineering design through modeling and simulation on a CAD platform. Computer Applications in Engineering Education, 26(4), 824–840.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. The study was approved by the Human Research Protection Program Institutional Review Boards of the Purdue University (Date: 07/02/2019, IRB Protocol #: 1906022338).
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Appendix
Appendix
Appendix A: Science concepts handout
Appendix B: Test for assumptions for statistical tests
Appendix C: \(T\&P\to CC\) Students’ generating idea, conducting experiment, revising and iterating, troubleshooting strategies use percentage per student after the first and second intervention.
\(T\&P\to CC\) | After the first intervention | After the second intervention | ||||||
---|---|---|---|---|---|---|---|---|
Student # | Generate ideas | Conduct exp | Revise and iterate | Trouble-shoot | Generate ideas | Conduct exp | Revise and iterate | Trouble-shoot |
S1 | 20.00% | 0.00% | 30.00% | 50.00% | 25.00% | 30.00% | 30.00% | 15.00% |
S2 | 10.00% | 25.00% | 55.00% | 10.00% | 35.00% | 30.00% | 25.00% | 10.00% |
S3 | 15.00% | 35.00% | 20.00% | 30.00% | 20.00% | 35.00% | 25.00% | 20.00% |
S4 | 10.00% | 15.00% | 30.00% | 45.00% | 10.00% | 20.00% | 42.50% | 27.50% |
S5 | 35.00% | 25.00% | 12.50% | 27.50% | 30.00% | 31.65% | 16.65% | 21.65% |
S6 | 35.00% | 20.00% | 27.50% | 17.50% | 25.00% | 10.00% | 32.50% | 32.50% |
S7 | 35.00% | 40.00% | 12.50% | 12.50% | 45.00% | 40.00% | 15.00% | 0.00% |
S8 | 45.00% | 35.00% | 15.00% | 5.00% | 10.00% | 44.15% | 14.15% | 31.65% |
S9 | 15.00% | 25.00% | 37.50% | 22.50% | 25.00% | 55.00% | 10.00% | 10.00% |
S10 | 22.50% | 20.00% | 50.00% | 7.50% | 35.00% | 35.00% | 25.00% | 5.00% |
S11 | 22.50% | 25.00% | 35.00% | 17.50% | 20.00% | 40.00% | 30.00% | 10.00% |
S12 | 0.00% | 32.50% | 47.50% | 20.00% | 4.15% | 40.75% | 1.65% | 43.30% |
Appendix D: \(CC\to T\&P\) students’ generating idea, conducting experiment, revising and iterating, troubleshooting strategies use percentage per student after the first and second intervention.
\(C\to T\&P\) | After the first intervention | After the second intervention | ||||||
---|---|---|---|---|---|---|---|---|
Student # | Generate ideas | Conduct exp | Revise and iterate | Trouble-shoot | Generate ideas | Conduct exp | Revise and iterate | Trouble-shoot |
S13 | 0.00% | 0.00% | 65.00% | 35.00% | 17.50% | 37.50% | 37.50% | 7.50% |
S14 | 0.00% | 35.00% | 25.00% | 40.00% | 30.00% | 42.50% | 27.50% | 0.00% |
S15 | 20.00% | 25.00% | 15.00% | 40.00% | 10.00% | 40.00% | 30.00% | 20.00% |
S16 | 25.00% | 32.50% | 22.50% | 20.00% | 27.50% | 26.65% | 29.15% | 16.65% |
S17 | 42.50% | 32.50% | 17.50% | 7.50% | 37.50% | 45.00% | 17.50% | 0.00% |
S18 | 30.00% | 5.00% | 55.00% | 10.00% | 22.50% | 32.50% | 42.50% | 2.50% |
S19 | 22.50% | 40.00% | 27.50% | 10.00% | 20.00% | 50.00% | 30.00% | 0.00% |
S20 | 5.00% | 15.00% | 77.50% | 2.50% | 14.15% | 44.15% | 17.50% | 24.15% |
S21 | 10.00% | 15.00% | 65.00% | 10.00% | 2.50% | 40.00% | 47.50% | 10.00% |
S22 | 15.00% | 51.65% | 19.15% | 14.15% | 15.00% | 45.00% | 37.50% | 2.50% |
S23 | 22.50% | 62.50% | 5.00% | 10.00% | 15.00% | 50.00% | 32.50% | 2.50% |
S24 | 15.00% | 17.50% | 32.50% | 35.00% | 5.00% | 32.50% | 57.50% | 5.00% |
S25 | 10.00% | 30.00% | 30.00% | 30.00% | 12.50% | 27.50% | 57.50% | 2.50% |
Appendix E: Pairwise comparisons for CE, RI and TS strategies from Generalized Linear Models
Rights and permissions
About this article
Cite this article
Karabiyik, T., Magana, A.J., Parsons, P. et al. Pedagogical approaches for eliciting students’ design thinking strategies: tell-and-practice vs. contrasting cases. Int J Technol Des Educ 33, 1087–1119 (2023). https://doi.org/10.1007/s10798-022-09757-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10798-022-09757-y