You are here:

The Relationship of a Motivational Instructional Design to Learning Effort and Outcomes in an Asynchronous Computer-Based Learning Program

, , Purdue University, United States

AACE Award

EdMedia + Innovate Learning, in Honolulu, HI, USA ISBN 978-1-880094-73-0 Publisher: Association for the Advancement of Computing in Education (AACE), Waynesville, NC


Technology has long been embraced by nursing faculty who seek to create authentic learning experiences prior to actual hands-on experience in a clinical setting. The aim of this study was to explore effect of a CBL program designed with embedded motivational strategies based on Keller’s Attention, Relevance, Confidence, and Satisfaction (ARCS) model of motivational design on learning effort and learning outcomes. Motivational state and motivational appeal of the materials was also measured. Motivational appeal of the program had a positive correlation with total time (effort) spent in the program. Time spent in the program was the strongest predictor variable for learner outcomes. The 65 participants were highly positive about using a CBL format with over 90% indicating they would like to learn other basic nursing skills this way.


Kirkpatrick, J. & Lehman, J. (2009). The Relationship of a Motivational Instructional Design to Learning Effort and Outcomes in an Asynchronous Computer-Based Learning Program. In G. Siemens & C. Fulford (Eds.), Proceedings of ED-MEDIA 2009--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 762-769). Honolulu, HI, USA: Association for the Advancement of Computing in Education (AACE). Retrieved January 18, 2019 from .


View References & Citations Map


  1. Billings, D. (2002). Conversations in E-Learning. Pensacola, FL: Pohl Publishing.
  2. Cook, D. (2005). The research we still are not doing: An agenda for the study of computer-based learning. Academic Medicine, 80(6), 541-548.
  3. Cook, D., Thompson, W., Thomas, K., Thomas, M., & Pankratz, S. (2006). Impact of self-assessment questions and learning styles in web-based learning: A randomized, controlled, crossover trial. Academic Medicine, 81 (3), 231 – 238.
  4. Driscoll, M. (2005). Psychology of learning for instruction (3rd ed.). Boston: Allyn and Bacon.
  5. Keller, J.M. (2004). A Predictive Model of Motivation, Volition, and Multimedia Learning. Proceedings of the
  6. Kirkpatrick, J. (2006). Comprehensive newborn assessment: An interactive learning package. Gestational age
  7. Means, T.B., Jonassen, D.H., & Dwyer, F.M. (1997). Enhancing relevance: Embedded ARCS strategies vs. Purpose. Educational Technology Research and Development, 45(1), 5-17.
  8. Neuman, L.H. (2006). Creating new futures in nursing education: envisioning the evolution of e-nursing education. Nursing Education Perspectives, 27(1), 12-15.
  9. Paas, F., Tuovinen, J., van Merriënboer, J., Darabi, A. (2005). A motivational perspective on the relation between mental effort and performance: Optimizing learner involvement in instruction. Educational Technology Research and Development, 53(3), 25-34.
  10. Pintrich, P.R., Smith, D.A.F., Garcia, T. & McKeachie, W.J. (1991).A manual for the use of the Motivated Strategies for Learning Questionnaire (MSLQ). Ann Arbor: University of Michigan, National Center for Research to Improve Postsecondary Teaching and Learning.
  11. Rodgers, D., & Withrow-Thorton, B. (2005). The effect of instructional media on learner motivation. International Journal of Instructional Media, 32(4), 333-342.
  12. Song, S.H., & Keller, J.M. (2001). Effectiveness of motivationally adaptive computer-assisted instruction on the dynamic aspects of motivation. Educational Technology Research and Development ,49(2), 5-22.

These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact