Abstract
Optimizing technical systems depends on scientifically grounded models of system performance. Similarly, the development of engineering and technology education systems fruitfully builds upon relevant learning theories. Engineering and technology involve complex skills and concepts embedded in rich contexts. We review learning theories particularly appropriate for supporting learning of such complex concepts in rich contexts, drawing heavily on information processing, distributed cognition and cognitive apprenticeship.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
REFERENCES
Anderson, J. R., Bothell, D., Byrne, M., & LeBiere, C. (2004). An integrated theory of the mind. Psychological Review, 111(4), 1036–1060.
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.
Anderson, J. R., Fincham, J. M., & Douglass, S. (1997). The role of examples and rules in the acquisition of a cognitive skill. Journal of Experimental Psychology-Learning Memory and Cognition, 23(4), 932–945.
Anderson, J. R., & Schunn, C. D. (2000). Implications of the ACT-R learning theory: No magic bullets. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 5, pp. 1–33). Mahwah, NJ: Erlbaum.
Baddeley, A. (2003). Working memory: Looking back and looking forward. Nature Reviews Neuroscience, 4(10), 829–839.
Biederman, I., & Shiffrar, M. M. (1987). Sexing day-old chicks: A case study and expert systems analysis of a difficult perceptual-learning task. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13(4), 640–645.
Borrego, M. (2007). Conceptual difficulties experienced by trained engineers learning educational research methods. Journal of Engineering Education, 96(2), 91–102.
Carpenter, P. A., Just, M. A., & Shell, P. (1990). What one intelligence test measures: A theoretical account of the processing in the Raven Progressive Matrices Test. Psychological Review, 97, 404–431.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 15, 145–182.
Clement, J. (1982). Students’ preconceptions in introductory mechanics. The American Journal of Physics, 50(1), 66–71.
Collins, A., Brown, J. S., & Holum, A. (1991). Cognitive apprenticeship: Making thinking visible. American Educator, 15(3), 6–11, 38–46.
Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (2nd ed.). Cambridge, MA: MIT Press.
Gainsburg, J. (2006). The mathematical modelling of structural engineers. Mathematical Thinking and Learning, 8(1), 3–36.
Geary, D. C., Boykin, A. W., Embretson, S., Reyna, V., Siegler, R., Berch, D. B., et al. (2008). Report of the task group on learning processes. Washington, DC: U.S. Department of Education.
Hamilton, E., Lesh, R., Lester, F., & Brilleslyper, M. (2008). Model-eliciting activities (MEAs) as a bridge between engineering education research and mathematics education research. Advances in Engineering Education, 1(2), 1–25.
Hammer, D., & Elby, A. (2003). Tapping epistemological resources for learning physics. Journal of the Learning Sciences, 12(1), 53–90.
Hutchins, E. (1995). How a cockpit remembers its speeds. Cognitive Science, 19(3), 265–288.
Kaplan, C. A., & Simon, H. A. (1990). In search of insight. Cognitive Psychology, 22, 374–419.
Kellman, P. J., Massey, C. M., & Son, J. Y. (2010). Perceptual learning modules in mathematics: Enhancing students’ pattern recognition, structure extraction, and fluency. Topics in Cognitive Science, 2(2), 285–305.
Kilpatrick, J., Swafford, J., & Findell, B. (Eds.). (2001). Adding it up: Helping children learn mathematics. Washington, DC: National Academies Press.
Kim, E., & Pak, S. J. (2002). Students do not overcome conceptual difficulties after solving 1000 traditional problems. American Journal of Physics, 70, 759–765.
Klahr, D., & Carver, S. M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362–404.
Knowlton, B. J., Mangels, J. A., & Squire, L. R. (1996). A neostriatal habit learning system in humans. Science, 273, 1399–1402.
Lave, J. (1988). Cognition in practice: Mind, mathematics and culture in everyday life. Cambridge, MA: Cambridge University Press.
Lesh, R., & Doerr, H. (2003). Beyond constructivism: A models & modeling perspective on mathematics teaching, learning, and problems solving. Mahwah, NJ: Lawrence Erlbaum.
Lesh, R., & Harel, G. (2003). Problem solving, modeling, and local conceptual development. Mathematical Thinking and Learning, 5(2&3), 157–189.
Lesh, R., Hoover, M., Hole, B., Kelly, A., & Post, T. (2000). Principles for developing thought-revealing activities for students and teachers. In A. Kelly & R. Lesh (Eds.), Handbook of research design in mathematics and science education (pp. 591–646). Mahwah, NJ: Lawrence Erlbaum Associates.
Lesh, R., & Lehrer, R. (2003). Models and modeling perspectives on the development of students and teachers. Mathematical Thinking and Learning, 5(2&3), 109–129.
Mehalik, M. M., Doppelt, Y., & Schunn, C. D. (2008). Middle-school science through design-based learning versus scripted inquiry: Better overall science concept learning and equity gap reduction. Journal of Engineering Education, 97(1), 71–85.
Moore, T. J., & Diefes-Dux, H. A. (2004). Developing Model-Eliciting Activities for undergraduate students based on advanced engineering context. Proceedings of the Thirty-Fourth ASEE/IEEE Frontiers in Education conference. Savannah, GA. Retrieved fromhttp://fie-conference.org/fie2004/index.htm
Moss, J., Kotovsky, K., & Cagan, J. (2006). The role of functionality in the mental representations of engineering students: Some differences in the early stages of expertise. Cognitive Science, 30(1), 65–93.
John, D., Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (1999). How people learn: Brain, mind, experience, and school. Washington, DC: National Academies Press.
Duschl, R. A., Schweingruber, H. A., & Shouse, A. W. (Eds.). (2007). Taking science to school: Learning and teaching science in grades K-8. Washington, DC: The National Academies Press.
Palincsar, A. S., & Brown, A. L. (1984). Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1(2), 117–175.
Pavlik, P. I., & Anderson, J. R. (2005). Practice and forgetting effects on vocabulary memory: An activationbased model of the spacing effect. Cognitive Science, 29(4), 559–586.
Prince, M. (2004). Does active learning work? A review of the research. Journal of Engineering Education, 93(3), 223–231.
Reif, F., & Scott, L. A. (1999). Teaching scientific thinking skills: Students and computers coaching each other. American Journal of Physics, 67(9), 819–831.
Reisslein, J., Sullivan, H., & Reisslein, M. (2007). Learning achievement and attitudes under different paces of transitioning to independent problem solving. Journal of Engineering Education, 96(1), 45–55.
Renkl, A., Atkinson, R. K., & Grosse, C. S. (2004). How fading worked solution steps works - a cognitive load perspective. Instructional Science, 32(1–2), 59–82.
Schoenfeld, A. H. (1987). Cognitive science and mathematics education. Hillsdale, NJ: Erlbaum.
Silk, E. M., Higashi, R., Shoop, R., & Schunn, C. D. (2010). Designing technology activities that teach mathematics. The Technology Teacher, 69(4), 21–27.
Singley, M. K., & Anderson, J. R. (1989). The transfer of cognitive skill. Cambridge, MA: Harvard Press.
Smith, C., Maclin, D., Grosslight, L., & Davis, H. (1997). Teaching for understanding: A study of students’ pre-instruction theories of matter and a comparison of the effectiveness of two approaches to teaching about matter and density. Cognition and Instruction, 15(3), 317–393.
Suchman, L. A. (1987). Plans and situated action: The problem of human-machine communication. New York: Cambridge University Press.
Thorndike, E. L. (1913). Educational psychology: The psychology of learning (Vol. 2). New York: Teacher’s College.
Van Merrienboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: recent developments and future directions. Educational Psychology Review, 17(2), 147–177.
Vygotsky, L. S. (1978). Mind in society. In M. Cole, V. John-Steiner, S. Scribner, & E. Souberman (Eds.), Interaction between learning and development (pp. 79–91). Cambridge, MA: Harvard University Press.
Wickens, C. D. (2008). Multiple Resources and Mental Workload. Human Factors. The Journal of the Human Factors and Ergonomics Society, 50(3), 449–455.
Wickens, C. D., & McCarley, J. S. (2008). Applied attention theory. Boca Raton, FL: Taylor & Francis.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Sense Publishers
About this chapter
Cite this chapter
Schunn, C.D., Silk, E.M. (2011). Learning Theories For Engineering and Technology Education. In: Barak, M., Hacker, M. (eds) Fostering Human Development Through Engineering and Technology Education. International Technology Education Studies, vol 6. SensePublishers. https://doi.org/10.1007/978-94-6091-549-9_1
Download citation
DOI: https://doi.org/10.1007/978-94-6091-549-9_1
Publisher Name: SensePublishers
Online ISBN: 978-94-6091-549-9
eBook Packages: Humanities, Social Sciences and LawEducation (R0)