Instructional Science

, Volume 42, Issue 6, pp 839–859 | Cite as

Enhancing learning outcomes in computer-based training via self-generated elaboration

  • Haydee M. Cuevas
  • Stephen M. Fiore


The present study investigated the utility of an instructional strategy known as the query method for enhancing learning outcomes in computer-based training. The query method involves an embedded guided, sentence generation task requiring elaboration of key concepts in the training material that encourages learners to ‘stop and think’ about the information already presented before proceeding to new concepts. This study also investigated the effect of varying the level of elaboration (low or high) prompted by the queries. Fifty-one undergraduate students from the general psychology department subject pool at a major university in the southeastern United States received instruction on the basic principles of flight via one of three versions of a computer-based tutorial (no query, low-level elaboration query, or high-level elaboration query). Participants had no prior knowledge or previous experience with the aviation domain. A one-way between-groups design was employed, with the query method serving as the independent variable and a sample size of 17 per condition. Dependent variables included knowledge organization, knowledge acquisition, and instructional efficiency. Overall, results showed that incorporating low-level elaboration queries into the training resulted in improved organization, integration, and application of task-relevant knowledge and higher instructional efficiency. High-level elaboration queries consistently failed to produce significantly better post-training outcomes, possibly due to the increased cognitive load imposed on learners during training. The discussion centers on theoretical and practical implications for promoting and assessing learning outcomes in computer-based training.


Cognitive load Instructional efficiency Knowledge acquisition Knowledge organization Self-generated elaboration 



The views herein are those of the authors and do not necessarily reflect those of the organizations with which the authors are affiliated. The research reported in this paper is based upon the doctoral dissertation of Haydee M. Cuevas, University of Central Florida. Portions of this paper were presented at the Human Factors and Ergonomics Society 50th Annual Meeting. This research was partially supported by funding through Grant Number F49620-01-1-0214 from the Air Force Office of Scientific Research to Eduardo Salas, Stephen M. Fiore, and Clint A. Bowers.


  1. Brünken, R., Plass, J. L., & Leutner, D. (2004). Assessment of cognitive load in multimedia learning with dual-task methodology: Auditory load and modality effects. Instructional Science, 32, 115–132.CrossRefGoogle Scholar
  2. Catrambone, R., & Yuasa, M. (2006). Acquisition of procedures: The effects of example elaborations and active learning exercises. Learning and Instruction, 16(2), 139–153.CrossRefGoogle Scholar
  3. Chipman, S. F., Segal, J. W., & Glaser, R. (Eds.) (2013). Thinking and learning skills: Vol. 2: Research and open questions. New York, NY: Routledge–Taylor and Francis Group.Google Scholar
  4. Clark, R. C., Nguyen, F., & Sweller, J. (2006). Efficiency in learning: Evidence-based guidelines to manage cognitive load. San Francisco: Jossey-Bass.Google Scholar
  5. Cuevas, H. M., Fiore, S. M., Bowers, C. A., & Salas, E. (2004a). Fostering constructive cognitive and metacognitive activity in computer-based complex task training environments. Computers in Human Behavior, 20, 225–241.CrossRefGoogle Scholar
  6. Cuevas, H. M., Fiore, S. M., Bowers, C. A., & Salas, E. (2004b). Using guided learner-generated instructional strategies to transform learning into a constructive cognitive and metacognitive activity. Proceedings of the 48th Annual Meeting of the Human Factors and Ergonomics Society, (pp. 1049–1053). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
  7. Cuevas, H. M., Fiore, S. M., & Oser, R. L. (2002). Scaffolding cognitive and metacognitive processes in low verbal ability learners: Use of diagrams in computer-based training environments. Instructional Science, 30, 433–464.CrossRefGoogle Scholar
  8. de Bruin, A., Rikers, R., & Schmidt, H. (2007). The effect of self-explanation and prediction on the development of principled understanding of chess in novices. Contemporary Educational Psychology, 32(2), 188–205.CrossRefGoogle Scholar
  9. Fiore, S. M., Cuevas, H. M., & Oser, R. L. (2003). A picture is worth a thousand connections: The facilitative effects of diagrams on task performance and mental model development. Computers in Human Behavior, 19, 185–199.CrossRefGoogle Scholar
  10. Fiore, S. M., Cuevas, H. M., Scielzo, S., & Salas, E. (2002). Training individuals for distributed teams: Problem solving assessment for distributed mission research. Computers in Human Behavior, 18, 729–744.CrossRefGoogle Scholar
  11. Fiore, S. M., Hoffman, R. R., & Salas, E. (2008). Learning and performance across disciplines: An epilogue for moving multidisciplinary research towards an interdisciplinary science of expertise. Military Psychology, 20(S1), S155–S170.CrossRefGoogle Scholar
  12. Fiore, S. M., & Salas, E. (Eds.). (2007). Toward a science of distributed learning. Washington, DC: American Psychological Association.Google Scholar
  13. Fiorella, L., Vogel-Walcutt, J. J., & Fiore, S. (2012). Differential impact of two types of metacognitive prompting provided during simulation-based training. Computers in Human Behavior, 28(2), 696–702.CrossRefGoogle Scholar
  14. Fonseca, B., & Chi, M. T. H. (2011). The self-explanation effect: A constructive learning activity. In R. E. Mayer & P. A. Alexander (Eds.), The handbook of research on learning and instruction (pp. 296–321). New York: Routledge—Taylor and Frances Group.Google Scholar
  15. Guilford, J. P., & Zimmerman, W. S. (1981). Manual of instructions and interpretations for the Guilford-Zimmerman Aptitude Survey (revised ed.). Palo Alto, CA: Consulting Psychological Press.Google Scholar
  16. Gully, S., & Chen, G. (2010). Individual differences, attribute-treatment interactions, and training outcomes. In S. W. J. Kozlowski & E. Salas (Eds.), Learning, training, and development in organizations (pp. 3–64). New York: Routledge–Taylor and Francis Group.Google Scholar
  17. Harper, M. E., Jentsch, F., Berry, D., Lau, H. C., Bowers, C., & Salas, E. (2003). TPL-KATS—Card Sort: A tool for assessing structural knowledge. Behavior Research Methods, Instruments, and Computers, 35(4), 577–584.CrossRefGoogle Scholar
  18. Hasler, B. S., Kersten, B., & Sweller, J. (2007). Learner control, cognitive load and instructional animation. Applied Cognitive Psychology, 21, 713–729.CrossRefGoogle Scholar
  19. Hermanson, D. R., Hermanson, H. M., & Tompkins, J. G, I. V. (1997). The impact of self-generated elaboration on students’ recall of finance concepts. Journal of Financial Education (Fall), 23, 27–34.Google Scholar
  20. Jeppesen Sanderson Training Systems. (1996a). Jeppesen Sanderson Private Pilot Exercises Book. Englewood, CO: Jeppesen Sanderson Inc.Google Scholar
  21. Jeppesen Sanderson Training Systems. (1996b). Jeppesen Sanderson Private Pilot Maneuvers Manual (6th ed.). Englewood, CO: Jeppesen Sanderson Inc.Google Scholar
  22. Jeppesen Sanderson Training Systems. (1996c). Jeppesen Sanderson Private Pilot Manual (15th ed.). Englewood, CO: Jeppesen Sanderson Inc.Google Scholar
  23. Kalyuga, S., Chandler, P., & Sweller, P. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13, 351–371.CrossRefGoogle Scholar
  24. Kalyuga, S., Chandler, P., & Sweller, J. (2004). When redundant on-screen text in multimedia technical instruction can interfere with learning. Human Factors, 46, 567–581.CrossRefGoogle Scholar
  25. King, A. (1992). Facilitating elaborative learning through guided student-generated questioning. Educational Psychologist, 27, 111–126.CrossRefGoogle Scholar
  26. Kirschner, P. A. (2002). Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and Instruction, 12, 1–10.CrossRefGoogle Scholar
  27. Kirwan, B., Evans, A., Donohoe, L., Kilner, A., Lamoureux, Atkinson, T. & MacKendrick, H. (1997). Human factors in the ATM system design life cycle. In Paper presented at the FAA/Eurocontrol ATM R&D Seminar, Paris, France, 16–20 June 1997. Retrieved from Accessed 19 Jan 2012.
  28. Marcus, N., Cooper, M., & Sweller, J. (1996). Understanding instructions. Journal of Educational Psychology, 88, 49–63.CrossRefGoogle Scholar
  29. Mayer, R. E. (2001). Multimedia learning. Cambridge, England: Cambridge University Press.CrossRefGoogle Scholar
  30. Mayer, R. E., Hegarty, M., Mayer, S., & Campbell, J. (2005). When static media promote active learning: Annotated illustrations versus narrated animations in multimedia instruction. Journal of Experimental Psychology: Applied, 11(4), 256–265.Google Scholar
  31. Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93, 187–198.CrossRefGoogle Scholar
  32. Moreno, R., & Mayer, R. E. (2002). Verbal redundancy in multimedia learning: When reading helps listening. Journal of Educational Psychology, 94(1), 156–163.CrossRefGoogle Scholar
  33. O’Reilly, T., Symons, S., & MacLatchy-Gaudet, H. (1998). A comparison of self-explanation and elaborative interrogation. Contemporary Educational Psychology, 23(4), 434–445.CrossRefGoogle Scholar
  34. Osman, M. E., & Hannafin, M. J. (1992). Metacognition research and theory: Analysis and implications for instructional design. Educational Technology Research and Development, 40, 83–99.CrossRefGoogle Scholar
  35. Paas, F. G. W. C., & van Merrienboer, J. J. G. (1993). The efficiency of instructional conditions: An approach to combine mental effort and performance measures. Human Factors, 35, 737–743.Google Scholar
  36. Paas, F. G. W. C., van Merrienboer, J. J. G., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and Motor Skills, 79, 419–430.CrossRefGoogle Scholar
  37. Rosenshine, B., Meister, C., & Chapman, S. (1996). Teaching students to generate questions: A review of the intervention studies. Review of Educational Research, 66, 181–221.CrossRefGoogle Scholar
  38. Salas, E., & Rosen, M. A. (2010). Experts at work: Principles for developing expertise in organizations. In S. W. J. Kozlowski & E. Salas (Eds.), Learning, training, and development in organizations (pp. 99–134). New York: Routledge–Taylor and Francis Group.Google Scholar
  39. Scielzo, S., Cuevas, H. M, & Fiore, S. M. (2005). Investigating individual differences and instructional efficiency in computer-based training environments. Proceedings of the Human Factors and Ergonomics Society 49th Annual Meeting (pp. 1251–1255). Santa Monica, CA: Human Factors and Ergonomics Society.Google Scholar
  40. Scielzo, S., Fiore, S. M., Cuevas, H. M., & Salas, E. (2004). Diagnosticity of mental models in cognitive and metacognitive processes: Implications for synthetic task environment training. In S. G. Schiflett, L. R. Elliott, E. Salas, & M. D. Coovert (Eds.), Scaled worlds: Development, validation, and applications (pp. 181–199). Aldershot, UK: Ashgate.Google Scholar
  41. Sweller, J. (1994). Cognitive load theory, learning difficulty and instructional design. Learning and Instruction, 4, 295–312.CrossRefGoogle Scholar
  42. Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22, 123–138.CrossRefGoogle Scholar
  43. Sweller, J., Ayers, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.CrossRefGoogle Scholar
  44. Sweller, J., van Merrienboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.CrossRefGoogle Scholar
  45. van Merrienboer, J. J. G., Schuurman, J. G., de Croock, M. B. M., & Paas, F. G. W. C. (2002). Redirecting learners’ attention during training: Effects on cognitive load, transfer test performance and training efficiency. Learning and Instruction, 12, 11–37.CrossRefGoogle Scholar
  46. Wong, R. M. F., Lawson, M. J., & Keeves, J. (2002). The effects of self-explanation training on students’ problem solving in high-school mathematics. Learning and Instruction, 12, 233–262.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.Department of Doctoral Studies, College of Aviation – Room 137EEmbry-Riddle Aeronautical UniversityDaytona BeachUSA
  2. 2.Cognitive Sciences – Department of Philosophy, College of Arts and HumanitiesUniversity of Central FloridaOrlandoUSA

Personalised recommendations