Abstract
A systematic study of the implementation of simulation hardware (TIMS) replacing software (MATLAB) was undertaken for advanced undergraduate and early graduate courses in electrical engineering. One outcome of the qualitative component of the study was remarkable: most students interviewed (4/4 and 6/9) perceived the software simulations as “fake”. Professionals, on the other hand, find such simulations as essentially perfectly replacing data from “real” systems. The implications of this large difference in perception between advanced undergraduate/early graduate students and professionals are discussed. At present, suitable theoretical frameworks related to motivation do not afford satisfactory explanation for this observation.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andrade J. (2001). The working memory model: Consensus, controvery, and future directions. In: Andrade J. (eds) Working memory in perspective. Psychology Press, Ltd., East Sussex, pp. 281–310
Baddeley A. D. (1986). Working memory. Oxford University Press, Oxford
Baddeley A. D., Hitch G. J. (1974). Working memory. In Bower G. (Eds.), The psychology of learning and motivation. Vol. VIII Academic Press, New York, pp. 47–90
Bandura A. (1997). Self-efficacy: The exercise of control. W. H. Freeman, New York
Clark R. C., Mayer R. E. (2003). E-learning and the science of instruction. San Francisco, CA: Jossey-Bass/Pfeiffer
Colom R., Rebollo I., Palacios A., Juan-Espinosa M., Kyllonen P. C. (2004). Working memory is (almost) perfectly predicted by g. Intelligence 32: 277–296
Deci E. L., Ryan R. M. (1985). Intrinsic motivation and self-determination in human behavior. Plenaum Press, New York
Dick W., Carey L., Carey J. O. (2001). The systematic design of instruction. (5th ed.) Longman, New York
Engle R. W., Kane M. J., Tuholski S. W. (1999). Individual differences in working memory capacity and what they tell us about controlled attention, general fluid intelligence, and functions of the prefrontal cortex. In: Miyake A., Shah P. (Eds.), Models of working memory. Cambridge University Press, Cambridge
Ericsson K. A., Delaney P. F. (1999). Long-term working memory as an alternative to capacity models of working memory in everyday skilled performance. In: Miyake A., Shah P. (eds) Models of working memory. Cambridge University Press, Cambridge, pp. 257–297
Hidi S., Renninger A., Krapp A. (2004). Interest, a motivational variable that combines affective and cognitive functioning. In: Dai D. Y., Sternberg R. J. (eds) Motivation, emotion, and cognition. Lawrence Erlbaum, Mahwah, NJ, pp. 89–115
Huang D. W., Diefes-Dux H., Imbrie P. K., Daku B., Kallimani J. G. (2004). Learning motivation evaluation for a computer-based instructional tutorial using arcs model of motivational design. Savannah, GA
Jensen A. R. (1998). The g factor. Praeger Publishers, Westpost, CT
Just M. A., Carpenter P. A. (1992). A capacity theory of comprehension: Individual differences in working memor. Psychological Review 99:122–149
Kalyuga S., Ayres P., Chandler P., Sweller J. (2003). The expertise reversal effect. Educational Psychologist 38(1):23–31
Keller J. M. (1987). The systematic process of motivational design. Performance and Instruction 26(9):1–8
Linebarger D. L., Kosanic A. Z., Greenwood C. R., Doku N. S. (2004). Effects of viewing the television program between the lions on the emergent literacy skills of young children. Journal of Educational Psychology 92(2):297–308
Malone T. W. (1981). Toward a theory of intrinsically motivating instruction. Cognition and Science 4:333–369
MathWorks T. (2005). Matlab® — the language of technical computing. Retrieved February 7, 2005, from http://www.mathworks.com/products/matlab/
Mayer R. (1998). Cognitive, metacognitive, and motivational aspects of problem solving. Cognition and Science 26:49–63
Pintrich P. R., Schunk D. H. (1996). Motivation in education: Theory, research, and applications. Prentice Hall, Englewood Cliffs, NJ
Renkl A. (2002). Learning from worked-out examples: Instructional explanations supplement self-explanations. Learning & instruction 12:529–556
Schraw , G., Brooks, D. W., Crippen, K. J. (2005). Improving chemistry instruction using an interactive, compensatory model of learning. Journal of Chemical Education 81, in press
Shapiro A. (2004). How including prior knowledge as a subject variable may change outcomes of learning research. American Educational Research Journal 41(1):159–189
Srinivasan, S. (2004). Implementation of an integral signals and systems laboratory in electrical engineering courses: A study. Unpublished MA, University of Nebraska, Lincoln
Srinivasan, S., Pérez, L. C., Palmer, R. D. and Anderson, M. F. (2003a, November 5–7, 2003). Assessing laboratory effectiveness in electrical engineering courses. Paper presented at the Proceedings of the 2003 ASEE/IEEE Frontiers in Education (FIE) Conference, Boulder, CO
Srinivasan, S., Pérez, L. C., Palmer, R. D., Anderson, M. F., and Boye, A. J. (2003b, June 22–25, 2003a). An integrated signals and systems laboratory at the university of nebraska: Laboratory philosophy and study design. Paper presented at the 2003 ASEE Annual Conference and Exposition, Nashville, TN
Sweller J. (1999). Instructional design in technical areas. ACER Press, Camberwell, Victoria, Australia
Sweller J., Cooper G. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction 2:59–89
TIMS. (2005). Tims telecommunication: University, modulation, transmission, wireless. Retrieved February 7, 2005, from http://www.qpsk.com/html/qpsk.html
Trainin, G., Wilson, K., Wickless, M., and Brooks, D. (2005). Extraordinary animals and expository writing: Zoo in the classroom. Journal of Science Education and Technology 14(3): 299–304
Tuovinen J., Sweller J. (1999). A comparison of cognitive load assoicated with discovery learning and worked examples. Journal of Educational Psychology 91(2):334–341
Wade S. E., Buxton W. M., Kelly M. (1999). Using think-alouds to examine reader-text interest. Reading Research Quarterly 34:194–216
Acknowledgements
This material is based upon work supported by the National Science Foundation under Grant No. DUE - 0126733. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Author information
Authors and Affiliations
Corresponding author
Additional information
Work done at the Department of Electrical Engineering, University of Nebraska-Lincoln
Rights and permissions
About this article
Cite this article
Srinivasan, S., Pérez, L.C., Palmer, R.D. et al. Reality versus Simulation. J Sci Educ Technol 15, 137–141 (2006). https://doi.org/10.1007/s10956-006-9007-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10956-006-9007-5