Instructional Science

, Volume 47, Issue 5, pp 551–588 | Cite as

What’s your goal? The importance of shaping the goals of engineering tasks to focus learners on the underlying science

  • Laura J. Malkiewich
  • Catherine C. ChaseEmail author
Original Research


Engaging in engineering tasks can help students learn science concepts. However, many engineering tasks lead students to focus more on the success of their construction than on learning science content, which can hurt students’ ability to learn and transfer scientific principles from them. Two empirical studies investigate how content-focused learning goals and contrasting cases affect how students learn and transfer science concepts from engineering activities. High school students were given an engineering challenge, which involved understanding and applying center of mass concepts. In Study 1, 86 students were divided into four conditions where both goals (content learning vs. outcome) and instructional scaffolds (contrasting cases vs. no cases) were manipulated during the engineering task. Students with both content-focused learning goals and contrasting cases were better able to transfer scientific principles to a new task. Meanwhile, regardless of condition, students who noticed the deep structure in the cases demonstrated greater learning. A second study tried to replicate the goal manipulation findings, while addressing some limitations of Study 1. In Study 2, 78 students received the same engineering task with contrasting cases, while half the students received a learning goal, and half received an outcome goal. Students who were given content-focused learning goals valued science learning resources more and were better able to transfer scientific principles to novel situations on a test. Across conditions, the more students valued resources, the more they learned, and students who noticed the deep structure transferred more. This research underscores the importance of content-focused learning goals for supporting transfer of scientific principles from engineering tasks, when students have access to adequate instructional scaffolds.


Transfer Engineering education Contrasting cases Learning goals Physics learning 



This work was supported by two grants from Teachers College Columbia University (Research Dissertation Fellowship, and the Dean’s Grant for Student Research). We thank the following colleagues for their contributions to various parts of the project, including data collection and general advice: Aakash Kumar, Bryan Keller, Deanna Kuhn, Naomi Choodnovski, Matthew Zellman, Vivian Chang, Li Jiang, Xinxu Shen, Kimberly Zambrano, and Elisabeth Hartman.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

Studies presented in this paper were vetted and approved by the Institutional Review Board at Teachers College, Columbia University, and the school board for the school where the study was performed.

Informed consent

Informed assent was obtained from all student participants, and informed consent was obtained from their parents.


  1. Ahmed, S., Wallace, K. M., & Blessing, L. T. (2003). Understanding the differences between how novice and experienced designers approach design tasks. Research in Engineering Design, 14(1), 1–11.CrossRefGoogle Scholar
  2. Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2016). Help helps, but only so much: Research on help seeking with intelligent tutoring systems. International Journal of Artificial Intelligence in Education, 26(1), 205–223.CrossRefGoogle Scholar
  3. Aleven, V., Stahl, E., Schworm, S., Fischer, F., & Wallace, R. (2003). Help seeking and help design in interactive learning environments. Review of Educational Research, 73(3), 277–320.CrossRefGoogle Scholar
  4. Ames, C., & Archer, J. (1988). Achievement goals in the classroom: Students’ learning strategies and motivation processes. Journal of Educational Psychology, 80(3), 260–267.CrossRefGoogle Scholar
  5. Amrhein, V., Korner-Nievergelt, F., & Roth, T. (2017). The earth is flat (p > 0.05): Significance thresholds and the crisis of unreplicable research. PeerJ, 5, e3544. Scholar
  6. Apedoe, X. S., & Schunn, C. D. (2012). Strategies for success: Uncovering what makes students successful in design and learning. Instructional Science, 41(4), 773–791.CrossRefGoogle Scholar
  7. Arbreton, A. (1998). Student goal orientation and help seeking strategy use. In S. A. Karabenick (Ed.), Strategic help seeking: Implications for learning and teaching (pp. 95–116). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  8. Barnett, S. M., & Ceci, S. J. (2002). When and where do I apply what I learn?: A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637.CrossRefGoogle Scholar
  9. Barron, B. J., Schwartz, D. L., Vye, N. J., Moore, A., Petrosino, A., Zech, L., et al. (1998). Doing with understanding: Lessons from research on problem-and project-based learning. Journal of the Learning Sciences, 7(3–4), 271–311.Google Scholar
  10. Berland, M., Martin, T., Benton, T., Petrick Smith, C., & Davis, D. (2013a). Using learning analytics to understand the learning pathways of novice programmers. Journal of the Learning Sciences, 22(4), 564–599.CrossRefGoogle Scholar
  11. Berland, L., Martin, T., Ko, P., Baker Peacock, S., Rudolph, J., & Golubski, C. (2013b). Student learning in challenge-based engineering curricula. Journal of Pre-College Engineering Education Research, 3(1), 53–64.Google Scholar
  12. Bottoms, G., & Anthony, K. (2005). Project lead the way: A pre-engineering curriculum that works. Southern Regional Education Board. Atlanta, GA. Retrieved June 27, 2014 from
  13. Bransford, J. D., Brown, A., & Cocking, R. (1999). How people learn: Mind, brain, experience, and school. Washington, DC: National Research Council.Google Scholar
  14. Bransford, J. D., Franks, J. J., Vye, N. J., & Sherwood, R. D. (1989). New approaches to instruction: Because wisdom can’t be told. In S. Vosniadou & A. Ortony (Eds.), Similarity and analogical reasoning (pp. 470–497). Cambridge, MA: Cambridge University Press.CrossRefGoogle Scholar
  15. Bransford, J. D., & Schwartz, D. L. (1999). Chapter 3: Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24(1), 61–100.CrossRefGoogle Scholar
  16. Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97(3), 369–387.CrossRefGoogle Scholar
  17. Bundy, B. M. (2013). AP physics 1: Algebra-based course planning and pacing guide. New York: The College Board.Google Scholar
  18. Chase, C. C., Harpstead, E., & Aleven, V. (2017). Inciting out-of-game transfer: Adapting contrast-based instruction for educational games. In Paper presented at Games+Learning+Society, Madison, WI.Google Scholar
  19. Chase, C. C., Malkiewich, L. J., & Kumar, A. (2019). Learning to notice science concepts in engineering activities and transfer situations. Science Education, 103(2), 440–471. Scholar
  20. Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121–152.CrossRefGoogle Scholar
  21. Chi, M. T., & VanLehn, K. A. (2012). Seeing deep structure from the interactions of surface features. Educational Psychologist, 47(3), 177–188.CrossRefGoogle Scholar
  22. Day, S. B., & Goldstone, R. L. (2012). The import of knowledge export: Connecting findings and theories of transfer of learning. Educational Psychologist, 47(3), 153–176.CrossRefGoogle Scholar
  23. Detterman, D. K. (1993). The case for the prosecution: Transfer as an epiphenomenon. In D. K. Detterman & R. J. Sternberg (Eds.), Transfer on trial: Intelligence, cognition, and instruction (pp. 1–24). Westport, CT: Able Publishing.Google Scholar
  24. Dow, S. P., Heddleston, K., & Klemmer, S. R. (2009, October). The efficacy of prototyping under time constraints. In Proceedings of the seventh ACM conference on creativity and cognition (pp. 165–174). ACM.Google Scholar
  25. Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41(10), 1040–1048.CrossRefGoogle Scholar
  26. Elliot, A. J., & Harackiewicz, J. M. (1994). Goal setting, achievement orientation, and intrinsic motivation: A mediational analysis. Journal of Personality and Social Psychology, 66(5), 968–980.CrossRefGoogle Scholar
  27. Fortus, D., Dershimer, R. C., Krajcik, J., Marx, R. W., & Mamlok-Naaman, R. (2004). Design-based science and student learning. Journal of Research in Science Teaching, 41(10), 1081–1110.CrossRefGoogle Scholar
  28. Gardner, A. K., Diesen, D. L., Hogg, D., & Huerta, S. (2016). The impact of goal setting and goal orientation on performance during a clerkship surgical skills training program. The American Journal of Surgery, 211(2), 321–325.CrossRefGoogle Scholar
  29. Gentner, D., Levine, S. C., Ping, R., Isaia, A., Dhillon, S., Bradley, C., et al. (2016). Rapid learning in a children’s museum via analogical comparison. Cognitive Science, 40(1), 224–240.CrossRefGoogle Scholar
  30. Gero, J. S., Jiang, H., & Williams, C. (2013). Design cognition differences when using unstructured, partially structured and structured concept generation creativity techniques. International Journal of Design Creativity and Innovation, 1(4), 196–214.CrossRefGoogle Scholar
  31. Gertzman, A. D., & Kolodner, J. L. (1996, July). A case study of problem-based learning in a middle school science classroom: Lessons learned. In Proceedings of the 1996 international conference on learning sciences (pp. 91–98). International Society of the Learning Sciences.Google Scholar
  32. Gibson, E. J. (1988). Exploratory behavior in the development of perceiving, acting, and the acquiring of knowledge. Annual Review of Psychology, 39(1), 1–41.CrossRefGoogle Scholar
  33. Gibson, J. J., & Gibson, E. J. (1955). Perceptual learning: Differentiation or enrichment? Psychological Review, 62(1), 32–41.CrossRefGoogle Scholar
  34. Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15(1), 1–38.CrossRefGoogle Scholar
  35. Greeno, J. G., Moore, J. L., & Smith, D. R. (1993). Transfer of situated learning. Westport, CT: Ablex Publishing.Google Scholar
  36. Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learning about complex systems. Journal of the Learning Sciences, 9(3), 247–298.CrossRefGoogle Scholar
  37. Holbrook, J., & Kolodner, J. L. (2000). Scaffolding the development of an inquiry-based (science) classroom. In B. Fishman & S. O’Connor-Divelbiss (Eds.), Proceedings of international conference of the learning sciences ICLS 2000 (pp. 221–227). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.Google Scholar
  38. Kanter, D. E. (2010). Doing the project and learning the content: Designing project-based science curricula for meaningful understanding. Science Education, 94(3), 525–551.Google Scholar
  39. Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424.CrossRefGoogle Scholar
  40. Kenny, D. A., Kashy, D. A., Cook, W. L., & Simpson, J. A. (2006). Dyadic data analysis. New York, NY: The Guilford Press.Google Scholar
  41. Klahr, D., Triona, L. M., & Williams, C. (2007). Hands on what? The relative effectiveness of physical versus virtual materials in an engineering design project by middle school children. Journal of Research in Science Teaching, 44(1), 183–203.CrossRefGoogle Scholar
  42. Koh, J. H. L., Chai, C. S., Wong, B., & Hong, H.-Y. (2015). Design thinking for education. Singapore: Springer.CrossRefGoogle Scholar
  43. Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., … & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design (tm) into practice. The Journal of the Learning Sciences, 12(4), 495–547.Google Scholar
  44. Kolodner, J. L., Gray, J., & Fasse, B. (2003b). Promoting transfer through case-based reasoning: Rituals and practices in learning by design™ classrooms. Cognitive Science Quarterly, 3(2), 183–232.Google Scholar
  45. Kurtz, K. J., Miao, C. H., & Gentner, D. (2001). Learning by analogical bootstrapping. The Journal of the Learning Sciences, 10(4), 417–446.CrossRefGoogle Scholar
  46. Lachapelle, C. P., & Cunningham, C. M. (2007, June). Engineering is elementary: Children’s changing understandings of science and engineering. In Paper presented at American Society for Engineering Education Annual Conference & Exposition, Honolulu, HI.Google Scholar
  47. Lammi, M., Denson, C., & Asunda, P. (2018). Search and review of the literature on engineering design challenges in secondary school settings. Journal of Pre-College Engineering Education Research, 8(2), 49–66.CrossRefGoogle Scholar
  48. Latham, G. P., & Brown, T. C. (2006). The effect of learning vs. outcome goals on self-efficacy, satisfaction and performance in an MBA program. Applied Psychology, 55(4), 606–623.CrossRefGoogle Scholar
  49. Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
  50. Lee, A. (2017). Productive responses to failure for future learning. Doctoral dissertation, Columbia University.Google Scholar
  51. Lobato, J., Rhodehamel, B., & Hohensee, C. (2012). “Noticing” as an alternative transfer of learning process. Journal of the Learning Sciences, 21(3), 433–482.CrossRefGoogle Scholar
  52. Locke, E. A., & Bryan, J. (1969). The directing function of goals in task performance. Organizational Behavior and Human Performance, 4(1), 35–42.CrossRefGoogle Scholar
  53. Locke, E. A., & Latham, G. P. (1990). A theory of goal setting & task performance. Englewood Cliffs, NJ: Prentice-Hall Inc.Google Scholar
  54. Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American Psychologist, 57(9), 705–717.CrossRefGoogle Scholar
  55. Locke, E. A., Shaw, K. N., Saari, L. M., & Latham, G. P. (1981). Goal setting and task performance: 1969–1980. Psychological Bulletin, 90(1), 125–152.CrossRefGoogle Scholar
  56. 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.CrossRefGoogle Scholar
  57. Loibl, K., & Rummel, N. (2014). Knowing what you don’t know makes failure productive. Learning and Instruction, 34, 74–85. Scholar
  58. Malkiewich, L. J., & Chase, C. C. (2019). Focusing processes: Potential pathways for transfer of science concepts from an engineering task. International Journal of Science Education. Scholar
  59. Mandl, H., Gräsel, C., & Fischer, F. (2000). Problem-oriented learning: Facilitating the use of domain-specific and control strategies through modeling by an expert. In W. J. Perrig & A. Grob (Eds.), Control of human behavior, mental processes and consciousness (pp. 165–182). Mahwah, NJ: Erlbaum.Google Scholar
  60. Marks, J. (2017). The impact of a brief design thinking intervention on students’ design knowledge, iterative dispositions, and attitudes towards failure. Doctoral dissertation, Columbia University.Google Scholar
  61. Mercier, E. M. (2017). The influence of achievement goals on collaborative interactions and knowledge convergence. Learning and Instruction, 50, 31–43.CrossRefGoogle Scholar
  62. Miller, C. S., Lehman, J. F., & Koedinger, K. R. (1999). Goals and learning in microworlds. Cognitive Science, 23(3), 305–336.CrossRefGoogle Scholar
  63. National Research Council. (2012). Education for life and work: Developing transferable knowledge and skills in the 21st century. Washington, DC: The National Academies Press.Google Scholar
  64. Nokes, T. J., & Belenky, D. M. (2011). Incorporating motivation into a theoretical framework for knowledge transfer. In J. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 109–135)., Cognition in Education San Diego, CA: Academic Press.CrossRefGoogle Scholar
  65. Perkins, D. N., & Salomon, G. (1988). Teaching for transfer. Educational Leadership, 46(1), 22–32.Google Scholar
  66. Petrosino, A. J. (1998). The use of reflection and revision in hands-on experimental activities by at-risk children. Unpublished doctoral dissertation, Vanderbilt University, Nashville, TN.Google Scholar
  67. Pick, H. L. (1992). Eleanor J. Gibson: Learning to perceive and perceiving to learn. Developmental Psychology, 28(5), 787–794.CrossRefGoogle Scholar
  68. Renkl, A. (2002). Learning from worked-out examples: Instructional explanations supplement self-explanations. Learning & Instruction, 12, 529–556.CrossRefGoogle Scholar
  69. Riskowski, J. L., Todd, C. D., Wee, B., Dark, M., & Harbor, J. (2009). Exploring the effectiveness of an interdisciplinary water resources engineering module in an Either Grade Science Course. International Journal of Engineering Education, 25(1), 181–195.Google Scholar
  70. Roll, I., Aleven, V., & Koedinger, K. (2011, January). Outcomes and mechanisms of transfer in invention activities. In Proceedings of the Annual Meeting of the Cognitive Science Society, 33(33), 2824–2829.Google Scholar
  71. Roth, W. M., Tobin, K., & Ritchie, S. (2001). Re/constructing elementary science. New York: Peter Lang Publishing.Google Scholar
  72. Rothkopf, E. Z., & Billington, M. J. (1979). Goal-guided learning from text: Inferring a descriptive processing model from inspection times and eye movements. Journal of Educational Psychology, 71(3), 310–327.CrossRefGoogle Scholar
  73. Ryan, A. M., & Pintrich, P. R. (1997). “ Should I ask for help?” The role of motivation and attitudes in adolescents’ help seeking in math class. Journal of Educational Psychology, 89(2), 329–341.CrossRefGoogle Scholar
  74. Schauble, L., Klopfer, L. E., & Raghavan, K. (1991). Students’ transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 28(9), 859–882.CrossRefGoogle Scholar
  75. Schnittka, C., & Bell, R. (2011). Engineering design and conceptual change in science: Addressing thermal energy and heat transfer in eighth grade. International Journal of Science Education, 33(13), 1861–1887.CrossRefGoogle Scholar
  76. Schunk, D. H., & Swartz, C. W. (1993). Goals and progress feedback: Effects on self-efficacy and writing achievement. Contemporary Educational Psychology, 18(3), 337–354.CrossRefGoogle Scholar
  77. Schunn, C. (2011). Design principles for high school engineering design challenges: Experiences from high school science classrooms. National Center for Engineering and Technology Education. Retrieved July 18, 2019 from
  78. Schwartz, D. L., & Arena, D. (2013). Measuring what matters most: Choice-based assessments for the digital age. Cambridge: MIT Press.CrossRefGoogle Scholar
  79. Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition and Instruction, 16(4), 475–5223.CrossRefGoogle Scholar
  80. 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), 759–775. Scholar
  81. 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.CrossRefGoogle Scholar
  82. Shemwell, J. T., Chase, C. C., & Schwartz, D. L. (2015). Seeking the general explanation: A test of inductive activities for learning and transfer. Journal of Research in Science Teaching, 52(1), 58–83. Scholar
  83. Shute, V. J., & Gluck, K. A. (1996). Individual differences in patterns of spontaneous online tool use. Journal of the Learning Sciences, 5, 329–355.CrossRefGoogle Scholar
  84. Silk, E. M., & Schunn, C. D. (2008, January). Utilizing contrasting cases to target science reasoning and content in a design-for-science unit. In Annual Meeting of the National Association for Research in Science Teaching (NARST), Baltimore, MD.Google Scholar
  85. Silk, E. M., Schunn, C. D., & Cary, M. S. (2009). The impact of an engineering design curriculum on science reasoning in an urban setting. Journal of Science Education and Technology, 18(3), 209–223.CrossRefGoogle Scholar
  86. Svarovsky, G. N., & Shaffer, D. W. (2007). Soda constructing knowledge through exploratoids. Journal of Research in Science Teaching, 44(1), 133–153.CrossRefGoogle Scholar
  87. Tavakol, M., & Dennik, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. Scholar
  88. University of Bristol (n.d.). Sample sizes for multilevel modeling. Retrieved January 5, 2019 from
  89. Vattam, S., & Kolodner, J. L. (2008). On foundations of technological support for addressing challenges facing design-based science learning. Pragmatics & Cognition, 16(2), 406–437.CrossRefGoogle Scholar
  90. Wood, D. (2001). Scaffolding, contingent tutoring, and computer-supported learning. International Journal of Artificial Intelligence in Education, 12, 280–292.Google Scholar
  91. Worsley, M., & Blikstein, P. (2014). Assessing the Makers: The Impact of Principle-Based Reasoning on Hands-on, Project-Based Learning. In J. L. Polman, E. A. Kyza, D. K. O’Neill, I. Tabak, W. R. Penuel, A. S. Jurow, … L. D’Amico (Eds.) Proceedings of the 2014 International Conference of the Learning Sciences (pp. 1147–1151). Boulder, CO.Google Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Human Development, Teachers CollegeColumbia UniversityNew YorkUSA

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