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Effectiveness, efficiency, and appeal: pick any two? The influence of learning domains and learning outcomes on designer judgments of useful instructional methods

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Abstract

When choosing instructional methods, instructional designers trade-off or sacrifice an outcome, such as effectiveness, efficiency, or appeal. In instructional planning theory, this is referred to as values about priorities. When values about priorities are combined with conditions about content, we expect that a different instructional outcome is sacrificed. The purpose of this research is to expand the ideas presented in our previous paper on the topic of how instructional designers actually use instructional planning theory to judge the usefulness of instructional methods. We asked 56 instructional designers to rate the usefulness of 31 instructional methods within four content conditions (cognitive, affective, psychomotor, and interpersonal) and three outcome values (effectiveness, efficiency, and appeal). The results show that learning domain, learning outcome, and the interaction of domain and outcome have a statistically-significant influence on a designer’s judgments regarding the usefulness of an instructional method. Furthermore, useful methods for cognitive content sacrifice appeal, useful methods for affective and psychomotor content sacrifice efficiency, and useful methods for interpersonal content sacrifice effectiveness. Overall, the results provide evidence that supports the core principles of instructional planning theory, specifically associated with the interaction between conditions and values. The results also provide instructional designers further guidance in selecting the most useful instructional methods.

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References

  • Atkinson, R. (1999). Project management: Cost, time, and quality, two best guesses and a phenomenon, it’s time to accept other success criteria. International Journal of Project Management, 17(6), 337–342.

    Article  Google Scholar 

  • Baytak, A., & Land, S. M. (2011). An investigation of the artifacts and process of constructing computer games about environmental science in a fifth grade classroom. Educational Technology Research and Development, 59(1), 765–782. doi:10.1007/s11423-010-9184-z.

    Article  Google Scholar 

  • Beatty, B. J. (2009). Fostering integrated learning outcomes across domains. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models: Building a common knowledge base (Vol. III, pp. 275–299). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300.

    Google Scholar 

  • Clark, F. E., & Angert, J. F. (1980). Instructional design research and teacher education. Paper presented at the Annual Meeting of the Southwest Educational Research Association, San Antonio, TX, 8 Feb 1980. ERIC Document 183528.

  • Collins, A. (1993). Design issues for learning environments. Center for Technology in Education, Technical Report No. 27. ERIC Document 357733.

  • Cuellar, M. (2010). Assessing project success: moving beyond the triple constraint. International Research Workshop on IT Project Management, 2010, Paper 13. Retrieved from http://aisel.aisnet.org/irwitpm2010/13.

  • Frei, F. X. (2006). Breaking the trade-off between efficiency and service. Harvard Business Review, 84(11), 92–101.

    Google Scholar 

  • Fritz, C. O., Morris, P. E., & Richler, J. J. (2012). Effect size estimates: Current use, calculations, and interpretation. Journal of Experimental Psychology, 141(1), 2–18. doi:10.1037/a0024338.

    Article  Google Scholar 

  • Gibbons, A. S., McConkie, M., Seo, K. K., & Wiley, D. A. (2009). Simulation approach to instruction. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models: Building a common knowledge base (Vol. III, pp. 167–193). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Gibson, J. T. (2009). Discussion approach to instruction. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models: Building a common knowledge base (Vol. III, pp. 99–116). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Gropper, G. L., & Kress, G. C. (1965). Individualizing instruction through pacing procedures. AV Communications Review, 13(2), 165–182.

    Google Scholar 

  • Hannifin, M. J., & Rieber, L. P. (1989). Psychological foundations of instructional design for emerging computer-based instructional technologies, part II. Educational Technology Research and Development, 37(2), 102–114.

    Article  Google Scholar 

  • Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23–48.

    Article  Google Scholar 

  • Honebein, P. H., & Honebein, C. H. (2014). The influence of cognitive domain content levels and gender on design judgments regarding useful instructional methods. Educational Technology Research and Development, 62(1), 53–69. doi:10.1007/s11423-013-9322-5.

    Article  Google Scholar 

  • Lindsey, L., & Berger, N. (2009). Experiential approach to instruction. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models: Building a common knowledge base (Vol. III, pp. 117–142). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Morrison, G., Ross, S., Kalman, H. K., & Kemp, J. (2011). Designing effective instruction. Hoboken, NJ: Wiley.

    Google Scholar 

  • Otto, K. N., & Antonsson, E. K. (2001). Trade-off strategies in engineering design. In E. K. Antonsson (Ed.), Imprecision in engineering design. Pasadena: Engineering Design Research Laboratory, Division of Engineering and Applied Science, California Institute of Technology.

    Google Scholar 

  • Reigeluth, C. M. (1983). Instructional design: What is it and why is it? In C. M. Reigeluth (Ed.), Instructional-design theories and models: An overview of their current status (pp. 3–36). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Reigeluth, C. M. (1999). What is instructional-design theory and how is it changing? In C. M. Reigeluth (Ed.), Instructional-design theories and models: A new paradigm of instructional theory (Vol. II, pp. 5–29). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Reigeluth, C. M., & Carr-Chellman, A. (2009). Understanding instructional theory. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models: Building a common knowledge base (Vol. III, pp. 3–26). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Reigeluth, C. M., & Keller, J. B. (2009). Understanding instruction. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models: Building a common knowledge base (Vol. III, pp. 27–39). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Savery, J. R. (2009). Problem-based approach to instruction. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models: Building a common knowledge base (Vol. III, pp. 143–165). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Tosti, D. T., & Ball, J. R. (1969). A behavioral approach to instructional design and media selection. AV Communications Review, 17(1), 5–24.

    Google Scholar 

  • Weber, K., & Custer, R. (2005). Gender-based preferences toward technology education content, activities, and instructional methods. Journal of Technology Education, 16(2), 55–71.

    Google Scholar 

  • Weston, C., & Cranton, P. A. (1986). Selecting instructional strategies. The Journal of Higher Education, 57(3), 259–288.

    Article  Google Scholar 

  • Wilson, B. G. (2004). Designing E-learning environments for flexible activity and instruction. Educational Technology Research and Development, 52(4), 77–84.

    Article  Google Scholar 

  • Wiske, M. S., & Beatty, B. J. (2009). Fostering understanding outcomes. In C. M. Reigeluth & A. Carr-Chellman (Eds.), Instructional-design theories and models (Vol. III, pp. 225–247). Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

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Correspondence to Peter C. Honebein.

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Honebein, P.C., Honebein, C.H. Effectiveness, efficiency, and appeal: pick any two? The influence of learning domains and learning outcomes on designer judgments of useful instructional methods. Education Tech Research Dev 63, 937–955 (2015). https://doi.org/10.1007/s11423-015-9396-3

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