Modeling expertise through Decision-based Learning: Theory, practice, and technology applications
Abstract
In higher education, faculty are generally hired for their expertise in the field. They have received extensive training in the discipline but have received limited training in teaching. Thus, they struggle in two ways to teach and develop expertise in novices: First, they are often blind to how their own intuitive expertise functions, and second, they lack a pedagogical strategy to teach their intuitive expert decision-making to students. In this paper, we synthesize the literature on these difficulties for experts. We then discuss how DBL uses cognitive task analysis to help experts make their knowledge explicit and how DBL may be an appropriate pedagogical solution for many university professors. Finally, we provide case studies of DBL in action and discuss how educational technology can support the theory and practice of Decision-based Learning.
Downloads
References
Alamäki, A. (2018). A conceptual model for knowledge dimensions and processes in design and technology projects. International journal of technology and design education, 28(3), 667-683.
Amolloh, O. P., Lilian, G. K., & Wanjiru, K. G. (2018). Experiential learning, conditional knowledge and professional development at University of Nairobi, Kenya—Focusing on preparedness for teaching practice. International Education Studies, 11(7), 125-135. doi:10.5539/ies.v11n7p125
Anderson, L. W., Krathwohl, D. R., Airasian, P. W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., . Wittrock, M. C. (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom’s taxonomy of educational objectives, abridged edition. White Plains, NY: Longman.
Barrotta, P., & Montuschi, E. (2018). Expertise, Relevance and Types of Knowledge. Social Epistemology, 32(6), 387-396. doi:10.1080/02691728.2018.1546345
Bol, L., & Garner, J. K. (2011). Challenges in supporting self-regulation in distance education environments. Journal of Computing in Higher Education, 23(2-3), 104-123.
Bransford, J., Brown, A., Cocking, R., & Center, E. R. I. (2000). How People Learn: Brain, Mind, Experience, and School. (2nd ed.). Washington, D.C.: National Academy Press.
Catrambone, R. (2011). Task analysis by problem solving (TAPS): Uncovering expert knowledge to develop high-quality instructional materials and training. Paper presented at the Learning and Technology Symposium, Columbus, GA.
Chang, T. S., Lin, H. H., & Song, M. M. (2011). University faculty members’ perceptions of their teaching efficacy. Innovations in Education and Teaching International, 48(1), 49-60. doi:10.1080/14703297.2010.543770
Collins, A. (2006). Cognitive Apprenticeship. In R. K. Sawyer (Ed.), Cambridge Handbook of the Learning Sciences (First ed., pp. 47-60). New York: Cambridge University Press.
Dath, D., & Iobst, W. (2010). The importance of faculty development in the transition to competency-based medical education. Medical Teacher, 32(8), 683-686. doi:10.3109/0142159x.2010.500710
Dreyfus, H. L., & Dreyfus, S. E. (2005). Peripheral Vision:Expertise in Real World Contexts. Organization Studies, 26(5), 779-792. doi:10.1177/0170840605053102
Dreyfus, H. L., & Dreyfus, S. E. (2008). Beyond Expertise: Some preliminary thoughts on mastery. In K. Nielsen (Ed.), A Qualitative Stance; Essays in Honor of Steiner Kvale (pp. 113-124): Arhus University Press.
Elvira, Q., Imants, J., Dankbaar, B., & Segers, M. (2017). Designing Education for Professional Expertise Development. Scandinavian Journal of Educational Research, 61(2), 187-204. doi:10.1080/00313831.2015.1119729
Endsley, M. R. (2018). Expertise and Situation Awareness. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.), The Cambridge handbook of expertise and expert performance (2nd ed., pp. 633-651). Cambridge, UK: Cambridge University Press.
Ericsson, K. A. (2006). The influence of experience and deliberate practice on the development of superior expert performance. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman (Eds.), The Cambridge handbook of expertise and expert performance (pp. 685-705). Cambridge, UK: Cambridge University Press.
Ericsson, K. A. (2018a). The Differential Influence of Experience, Practice, and Deliberate Practice on the Development of Superior Individual Performance of Experts. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.), The Cambridge handbook of expertise and expert performance (2nd ed., pp. 745-769).
Ericsson, K. A. (2018b). Superior Working Memory in Experts. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.), The Cambridge handbook of expertise and expert performance (2nd ed., pp. 745-769).
Fadde, P. J. (2009). Instructional design for advanced learners: training recognition skills to hasten expertise. Educational Technology Research and Development, 57(3), 359-376. doi:10.1007/s11423-007-9046-5
Feldon, D. F. (2010). Do psychology researchers tell it like it is? A microgenetic analysis of research strategies and self-report accuracy along a continuum of expertise. Instructional Science, 38(4), 395-415. doi:10.1007/s11251-008-9085-2
Feldon, D. F., Timmerman, B. C., Stowe, K. A., & Showman, R. (2010). Translating expertise into effective instruction: The impacts of cognitive task analysis (CTA) on lab report quality and student retention in the biological sciences. Journal of Research in Science Teaching, 47(10), 1165-1185. doi:10.1002/tea.20382
Foley, C. E., & Donnellan, N. M. (2019). Overcoming Expert Blind Spot when Teaching the Novice Surgeon. Journal of Minimally Invasive Gynecology, 26(7, Supplement), S20. doi:https://doi.org/10.1016/j.jmig.2019.09.509
Garikano, X., Garmendia, M., Manso, A. P., & Solaberrieta, E. (2019). Strategic knowledge-based approach for CAD modelling learning. International journal of technology and design education, 29(4), 947-959. doi:10.1007/s10798-018-9472-1
Garrison, D. R. (2007). Online community of inquiry review: social, cognitive, and teaching presence issues. Journal of Asynchronous Learning Networks, 11, 61+. Retrieved from https://link.gale.com/apps/doc/A284325498/AONE?u=byuprovo&sid=AONE&xid=1c8141df
Gilmore, J., Maher, M. A., Feldon, D. F., & Timmerman, B. (2014). Exploration of factors related to the development of science, technology, engineering, and mathematics graduate teaching assistants' teaching orientations. Studies in Higher Education, 39(10), 1910-1928. doi:10.1080/03075079.2013.806459
Gobet, F. (2005). Chunking models of expertise: Implications for education. Applied Cognitive Psychology, 19(2), 183-204. Retrieved from https://onlinelibrary.wiley.com/doi/pdf/10.1002/acp.1110
Gobet, F., & Charness, N. (2018). Expertise in Chess. In K. A. Ericsson, R. R. Hoffman, A. Kozbelt, & A. M. Williams (Eds.), The Cambridge handbook of expertise and expert performance (2nd ed., pp. 597-615).
Goertz, P. W. (2013). Seeing past the expert blind spot : developing a training module for in-service teachers. (MA). University of Texas-Austin, Austin, TX. Retrieved from https://repositories.lib.utexas.edu/handle/2152/23989
Gordon, M., & Guo, P. J. (2015). Codepourri: Creating visual coding tutorials using a volunteer crowd of learners. Paper presented at the 2015 IEEE symposium on visual languages and human-centric computing (VL/HCC).
Greca, I. M., & Moreira, M. A. (2000). Mental models, conceptual models, and modelling. International Journal of Science Education, 22(1), 1-11. doi:10.1080/095006900289976
Hoffman, R. R. (1998). How can expertise be defined? Implications of research from cognitive psychology. In R. Williams, W. Faulkner, & J. Fleck (Eds.), Exploring Expertise (pp. 81-100). London: Palgrave Macmillan.
Hoffman, R. R. (2016). How can expertise be defined? Implications of research from cognitive psychology. In J. Fleck, W. Faulkner, & R. Williams (Eds.), Exploring Expertise: Issues and Perspectives (pp. 81-99). London: Macmillan Press.
Hovious, A. (2016). Reality Check Revisited: Sage on the Stage vs. Guide on the Side. Retrieved from https://designerlibrarian.wordpress.com/tag/conditional-knowledge/
Huang, E. (2018). Rearview mirrors for the “expert blind spot”. In A. Bakker (Ed.), Design Research in Education: A Practical Guide for Early Career Researchers (pp. 16). New York, NY: Routledge.
Ivarsson, J. (2017). Visual Expertise as Embodied Practice. Frontline Learning Research, 5(3), 123-138. doi:10.14786/flr.v5i3.253
Johnson, K. (2005). The ‘general’ study of expertise. In K. Johnson (Ed.), Expertise in second language learning and teaching (pp. 11-33). London: Palgrave Macmillan.
Koh, D., Koedinger, K. R., Rosé, C. P., & Feldon, D. (2015). Expertise in Cognitive Task Analysis Interviews. Paper presented at the 37th Annual Meeting of the Cognitive Science Society, Pasadena, CA.
Le Maistre, C. (1998). What is an expert instructional designer? Evidence of expert performance during formative evaluation. Educational Technology Research and Development, 46(3), 21-36.
Lorch, R. F., Lorch, E. P., & Klusewitz, M. A. (1993). College students' conditional knowledge about reading. Journal of educational psychology, 85(2), 239.
Meyer, H. (2018). Teachers’ Thoughts on Student Decision Making During Engineering Design Lessons. Education Sciences, 8(1), 9. doi:10.3390/educsci8010009
Nathan, M. J., Koedinger, K. R., & Alibali, M. W. (2001). Expert blind spot: When content knowledge eclipses pedagogical content knowledge. Paper presented at the Third International Conference on Cognitive Science, Beijing, China.
Nathan, M. J., & Petrosino, A. J. (2003). Expert blind spot among preservice teachers. American Educational Research Journal, 40(4), 905-928.
Oluwatayo, A. A., Ezema, I., & Opoko, A. (2017). Development of Design Expertise by Architecture Students. Journal of Learning Design, 10(2), 35-56.
Ostermann, A., Leuders, T., & Nückles, M. (2018). Improving the judgment of task difficulties: prospective teachers’ diagnostic competence in the area of functions and graphs. Journal of Mathematics Teacher Education, 21(6), 579-605. doi:10.1007/s10857-017-9369-z
Pentimonti, J. M., Justice, L. M., Yeomans-Maldonado, G., McGinty, A. S., Slocum, L., & O’Connell, A. (2017). Teachers’ Use of High- and Low-Support Scaffolding Strategies to Differentiate Language Instruction in High-Risk/Economically Disadvantaged Settings. Journal of Early Intervention, 39(2), 125-146. doi:10.1177/1053815117700865
Persellin, D. C., & Goodrick, T. (2010). Faculty development in higher education: Long-term impact of a summer teaching and learning workshop. Journal of the Scholarship of Teaching and Learning, 10(1), 1.
Petrosino, A., & Shekhar, P. (2018). Expert blind spot among pre-service and in-service teachers: Beliefs about algebraic reasoning and potential impact on engineering education. The International journal of engineering education, 34(1), 97-105.
Plummer, K., Swan, R. H., & Lush, N. (2017). Introduction to Decision-Based Learning. Paper presented at the 11th International Technology, Education and Development Conference, Valencia, Spain.
Plummer, K., Taeger, S., & Burton, M. (2020). Decision‐based learning in religious education. Teaching Theology & Religion, 23(2), 110-125. doi:10.1111/teth.12538
Prince, M. (2004). Does Active Learning Work? A Review of the Research. Journal of Engineering Education, 93(3), 223-231.
Prince, M., & Felder, R. (2006). Inductive teaching and learning methods: Definitions, comparisons, and research bases. Journal of Engineering Education, 95(2), 123-138.
Prince, M., & Felder, R. (2007). The Many Faces of Inductive Teaching and Learning. Journal of College Science Teaching, 36(5), 14-20.
Raymond, K. M. (2019). First-year secondary mathematics teachers’ metacognitive knowledge of communication activities. Investigations in Mathematics Learning, 11(3), 167-179.
Renkl, A., & Mandl, H. (1996). Inert knowledge: Analyses and remedies. Educational Psychologist, 31(2), 115. Retrieved from https://www.lib.byu.edu/cgi-bin/remoteauth.pl?url=http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=9612021865&site=ehost-live&scope=site
Sansom, R. L., Suh, E., & Plummer, K. J. (2019). Decision-Based Learning: ″If I Just Knew Which Equation To Use, I Know I Could Solve This Problem!″. Journal of Chemical Education, 96(3), 445-454. doi:10.1021/acs.jchemed.8b00754
Schmidmaier, R., Eiber, S., Ebersbach, R., Schiller, M., Hege, I., Holzer, M., & Fischer, M. R. (2013). Learning the facts in medical school is not enough: which factors predict successful application of procedural knowledge in a laboratory setting? BMC medical education, 13(1), 28.
Shulman, L. S. (1987). Knowledge and Teaching: Foundations of the New Reform. Harvard Educational Review, 57(1), 1-23. doi:10.17763/haer.57.1.j463w79r56455411
Shulman, L. S. (2015). PCK: Its genesis and exodus. In A. Berry, P. Friedrichsen, & J. Loughran (Eds.), Re-examining pedagogical content knowledge in science education (pp. 13-23). New York, NY: Routledge.
Singer, S., & Smith, K. A. (2013). Discipline-Based Education Research: Understanding and Improving Learning in Undergraduate Science and Engineering. Journal of Engineering Education, 102(4), 468-471. doi:10.1002/jee.20030
Sugiharto, B., Corebima, A. D., Susilo, H., & Ibrohim. (2018). A comparison of types of knowledge of cognition of preservice biology teachers. Paper presented at the Asia-Pacific Forum on Science Learning & Teaching.
Swan, R. H. (2008). Deriving Operational Principles for the Design of Engaging Learning Experiences. (Doctoral Dissertation). Brigham Young University, Provo. Retrieved from https://scholarsarchive.byu.edu/etd/1829/
Swan, R. H., Plummer, K. J., & West, R. E. (2020). Toward functional expertise through formal education: Identifying an opportunity for higher education. Educational Technology Research & Development. doi:10.1007/s11423-020-09778-1
Sweller, J. (2011). Cognitive load theory. In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation (Vol. 55, pp. 37-76). Amsterdam: Elsevier.
Tofel-Grehl, C., & Feldon, D. F. (2013). Cognitive Task Analysis–Based Training. Journal of Cognitive Engineering and Decision Making, 7(3), 293-304. doi:10.1177/1555343412474821
Van De Kamp, M.-T., Admiraal, W., & Rijlaarsdam, G. (2016). Becoming original: effects of strategy instruction. Instructional Science, 44(6), 543-566. doi:10.1007/s11251-016-9384-y
Van de Wiel, M. W. (2017). Examining Expertise Using Interviews and Verbal Protocols. Frontline Learning Research, 5(3), 112-140.
Vasyukova, E. E. (2012). The Nature of Chess Expertise: Knowledge or Search? Psychology in Russia: State of Art, 5(1), 511. doi:10.11621/pir.2012.0032
Walsh, A., & Kotzee, B. (2010). Reconciling ‘graduateness’ and work-based learning. Learning and Teaching in Higher Education(4-1), 36-50.
West, R. E., & Leary, H. (2019). Scaffolding new qualitative researchers through decision-based learning [Conference Presentation]. Paper presented at the Association for Educational Communications Technology Annual Conference, Las Vegas, NV.
Whitehead, A. N. (1929). The Aims of Education and Other Essays. New York, NY: The Free Press.
Wiggins, G. P., & McTighe, J. (2005). Understanding by design: Ascd.
Yuan, B., Wang, M., Kushniruk, A. W., & Peng, J. (2017). Deep Learning towards Expertise Development in a Visualization-based Learning Environment. Journal of Educational Technology & Society, 20(4), 233-246. Retrieved from www.jstor.org/stable/26229220
Las obras que se publican en esta revista están sujetas a los siguientes términos:
1. El Servicio de Publicaciones de la Universidad de Murcia (la editorial) conserva los derechos patrimoniales (copyright) de las obras publicadas, y favorece y permite la reutilización de las mismas bajo la licencia de uso indicada en el punto 2.
2. Las obras se publican en la edición electrónica de la revista bajo una licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 3.0 España (texto legal). Se pueden copiar, usar, difundir, transmitir y exponer públicamente, siempre que: i) se cite la autoría y la fuente original de su publicación (revista, editorial y URL de la obra); ii) no se usen para fines comerciales; iii) se mencione la existencia y especificaciones de esta licencia de uso.
3. Condiciones de auto-archivo. Se permite y se anima a los autores a difundir electrónicamente las versiones pre-print (versión antes de ser evaluada) y/o post-print (versión evaluada y aceptada para su publicación) de sus obras antes de su publicación, ya que favorece su circulación y difusión más temprana y con ello un posible aumento en su citación y alcance entre la comunidad académica. Color RoMEO: verde.
