Which Technique is most Effective for Learning Declarative Concepts—Provided Examples, Generated Examples, or Both?

Replication

Abstract

Students in many courses are commonly expected to learn declarative concepts, which are abstract concepts denoted by key terms with short definitions that can be applied to a variety of scenarios as reported by Rawson et al. (Educational Psychology Review 27:483–504, 2015). Given that declarative concepts are common and foundational in many courses, an important question arises: What are the most effective techniques for learning declarative concepts? The current research competitively evaluated the effectiveness of various example-based learning techniques for learning declarative concepts, with respect to both long-term learning and efficiency during study. In experiment 1, students at a large, Midwestern university were asked to learn 10 declarative concepts in social psychology by studying provided examples (instances of concepts that are provided to students illustrate how the concept can be applied), generating examples (instances of concepts that the student generates on his or her own to practice applying the concept), or by receiving a combination of alternating provided examples and generated examples. Two days later, students completed final tests (an example classification test and a definition cued recall test). Experiment 2 replicated and extended findings from experiment 1. The extension group was a variation of the combination group, in which participants were simultaneously presented with a provided example while generating an example. In both experiments, long-term learning and study efficiency were greater following the study of provided examples relative to the other example-based learning techniques.

Keywords

Declarative concepts Example-based learning Provided examples Generated examples 

References

  1. Anderson, L. W., Krathwohl, D. R., & Bloom, B. S. (2001). A taxonomy for learning, teaching and assessing: a revision of Bloom’s taxonomy of educational objectives. Boston, MA: Allyn & Bacon.Google Scholar
  2. Balch, W. R. (2005). Elaborations of introductory psychology terms: effects on test performance and subjective ratings. Teaching of Psychology, 32, 29–34.CrossRefGoogle Scholar
  3. Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn?: a taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637.CrossRefGoogle Scholar
  4. Blaisman, R. N., Dunlosky, J., & Rawson, K. A. (in press). The what, how much, and when of study strategies: Comparing intended versus actual study behavior. Memory.Google Scholar
  5. Braver, S. L., Thoemmes, F. J., & Rosenthal, R. (2014). Continuously cumulating meta-analysis and replicability. Perspectives on Psychological Science, 9(3), 333–342.CrossRefGoogle Scholar
  6. Cantrambone, R., & Holyoak, K. J. (1989). Overcoming contextual limitations on problem-solving transfer. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(6), 1147–1156.Google Scholar
  7. Carroll, W. M. (1994). Using worked examples as an instructional support in the algebra classroom. Journal of Educational Psychology, 86(3), 360–367.CrossRefGoogle Scholar
  8. Cepeda, N. J., Pashler, H., Vul, E., Wixted, J. T., & Rohrer, D. (2006). Distributed practice in verbal recall tasks: a review and quantitative synthesis. Psychological Bulletin, 132(3), 354–380.CrossRefGoogle Scholar
  9. Cortina, J. M., & Nouri, H. (2000). Effect size for ANOVA designs. Thousand Oaks: CA: Sage.CrossRefGoogle Scholar
  10. Dornisch, M., Sperling, R. A., & Zeruth, J. A. (2011). The effects of level of elaboration on learners’ strategic processing of text. Instructional Science, 39, 1–26.CrossRefGoogle Scholar
  11. Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students’ learning with effective learning techniques: promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4–58.CrossRefGoogle Scholar
  12. Ebbinghaus, H. (1885). Memory: a contribution to experimental psychology. New York: NY: Columbia University Press.Google Scholar
  13. Faul, F., Erdfelder, E., Buchner, A., & Lang, A. G. (2009). Statistical power analyses using G*power 3.1: tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160.CrossRefGoogle Scholar
  14. Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 1–38.CrossRefGoogle Scholar
  15. Glover, J. A., & Corkill, A. J. (1987). Influence of paraphrased repetitions on the spacing effect. Journal of Educational Psychology, 79, 198–199.CrossRefGoogle Scholar
  16. Goldwater, M. B., & Schalk, L. (2016). Relational categories as a bridge between cognitive and educational research. Psychological Bulletin, 142(7), 729–757.CrossRefGoogle Scholar
  17. Gorrell, J., Tricou, C., & Graham, A. (1991). Children’s short and long-term retention of science concepts via self-generated examples. Journal of Research in Childhood Education, 5(2), 100–108.CrossRefGoogle Scholar
  18. Gurung, R. A. R. (2005). How do students really study (and does it matter)? Teaching of Psychology, 31, 164–166.CrossRefGoogle Scholar
  19. Gurung, R. A. R., Weidert, J., & Jeske, A. (2010). Focusing on how students study. Journal of the Scholarship of Teaching and Learning, 10(1), 28–35.Google Scholar
  20. Hamilton, R. (1989). The effects of learner-generated elaborations on concept learning from prose. The Journal of Experimental Education, 57(3), 205–217.CrossRefGoogle Scholar
  21. Hamilton, R. (1990). The effect of elaboration on the acquisition of conceptual problem-solving skills from prose. The Journal of Experimental Education, 59(1), 5–17.CrossRefGoogle Scholar
  22. Hamilton, R. (1997). Effects of three types of elaboration on learning concepts from text. Contemporary Educational Psychology, 22, 299–318.CrossRefGoogle Scholar
  23. Hamilton, R. (1999). The role of elaboration within a text processing and text adjunct context. British Journal of Educational Psychology, 69, 363–376.CrossRefGoogle Scholar
  24. Hamilton, R. (2004). Material appropriate processing and elaboration: the impact of balanced and complementary types of processing on learning concepts from text. British Journal of Educational Psychology, 74, 221–237.CrossRefGoogle Scholar
  25. Holyoak, K. J. (2012). Analogy and relational reasoning. In K. J. Holyoak & R. G. Morrison (Eds.), The Oxford handbook of thinking and reasoning (234–259). New York: Oxford University Press.CrossRefGoogle Scholar
  26. Howe, M. J. A., & Singer, L. (1975). Presentation variables and students’ activities in meaningful learning. British Journal of Educational Psychology, 45, 52–61.CrossRefGoogle Scholar
  27. Judd, C. M., & McClelland, G. H. (1989). Data analysis: a model-comparison approach. San Diego, CA: Harcourt Brace Jovanovich.Google Scholar
  28. Kalyuga, S., Rikers, R., & Paas, F. (2012). Educational implications of expertise reversal effects in learning and performance of complex cognitive and sensorimotor skills. Educational Psychology Review, 24(2), 313–337.CrossRefGoogle Scholar
  29. Kornell, N. (2009). Optimising learning using flashcards: spacing is more effective than cramming. Applied Cognitive Psychology, 23, 1297–1317.CrossRefGoogle Scholar
  30. Leahy, W., Hanham, J., & Sweller, J. (2015). High element interactivity information during problem solving may lead to failure to obtain the testing effect. Educational Psychology Review, 27(2), 291–304.CrossRefGoogle Scholar
  31. Lishner, D. A. (2015). A concise set of core recommendations to promote the dependability of psychological research. Review of General Psychology, 19, 52–68.CrossRefGoogle Scholar
  32. Maner, J. K. (2014). Let’s put our money where our mouth is. If authors are to change their ways, reviewers (and editors) must change with them. Perspectives in Psychological Science, 9, 343–351.CrossRefGoogle Scholar
  33. Murphy, G. (2004). The big book of concepts. Cambridge: MIT press.Google Scholar
  34. Nievelstein, F., van Gog, T., van Dijck, G., & Boshuizen, H. P. A. (2013). The worked example and expertise reversal effect in less structured tasks: learning to reason about legal cases. Contemporary Educational Psychology, 38(2), 118–125.CrossRefGoogle Scholar
  35. Paas, F., & van Merrienboer, J. J. G. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: a cognitive load approach. Journal of Educational Psychology, 86, 122–133.CrossRefGoogle Scholar
  36. Rawson, K. A., & Dunlosky, J. (2011). Optimizing schedules of retrieval practice for durable and efficient learning: how much is enough? Journal of Experimental Psychology: General, 140(3), 283–302.CrossRefGoogle Scholar
  37. Rawson, K. A., & Dunlosky, J. (2013). Relearning attenuates the benefits and costs of spacing. Journal of Experimental Psychology: General, 142, 1113–1129.CrossRefGoogle Scholar
  38. Rawson, K. A., & Dunlosky, J. (2016). How effective is example generation for learning declarative concepts? Educational Psychology Review, 28(3), 649–672.CrossRefGoogle Scholar
  39. Rawson, K. A., Thomas, R. C., & Jacoby, L. L. (2015). The power of examples: illustrative examples enhance conceptual learning of declarative concepts. Educational Psychology Review, 27, 483–504.CrossRefGoogle Scholar
  40. Renkl, A., Atkinson, R. K., Maier, U. H., & Staley, R. (2002). From example study to problem solving: smooth transitions help learning. Journal of Experimental Education, 70(4), 293–315.CrossRefGoogle Scholar
  41. Roediger, H. L., & Butler, A. C. (2011). The critical role of retrieval practice in long-term retention. Trends in Cognitive Sciences, 15(1), 20–27.CrossRefGoogle Scholar
  42. Rosenthal, R., & Rosnow, R. L. (1985). Contrast analysis: focused comparisons in the analysis of variance. New York: Cambridge University Press.Google Scholar
  43. Rowland, C. A. (2014). The effect of testing versus restudy on retention: a meta-analytic review of the testing effect. Psychological Bulletin, 140(6), 1432–1463.CrossRefGoogle Scholar
  44. Salden, R. J. C. M., Koedinger, K. R., Renkl, A., Aleven, V., & McLaren, B. M. (2010). Accounting for beneficial effects of worked examples in tutored problem solving. Educational Psychology Review, 22, 379–392.CrossRefGoogle Scholar
  45. Schmidt, S. (2009). Shall we really do it again? The powerful concept of replication is neglected in the social sciences. Review of General Psychology, 13, 90–100.CrossRefGoogle Scholar
  46. Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: undisclosed flexibility in data collections and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.CrossRefGoogle Scholar
  47. Simons, D. J. (2014). The value of direct replication. Perspectives on Psychological Science, 9, 76–80.CrossRefGoogle Scholar
  48. Tabachnick, B. G. & Fidell, L. S. (2001). Using multivariate statistics. Boston, MA : Allyn & Bacon.Google Scholar
  49. Taraban, R., Maki, W. S., & Rynearson, K. (1999). Measuring study time distributions: implications for designing computer-based courses. Behavior Research Methods, Instruments, & Computers, 31(2), 263–269.CrossRefGoogle Scholar
  50. van Gog, T., & Kester, L. (2012). A test of the testing effect: acquiring problem-solving skills from worked examples. Cognitive Science, 36(8), 1532–1541.CrossRefGoogle Scholar
  51. van Gog, T., Kester, L., Dirkx, K., Hoogerheide, V., Boerboom, J., & Verkoeijen, P. P. J. L. (2015). Testing after worked example study does not enhance delayed problem-solving performance compared to restudy. Educational Psychology Review, 27(2), 265–289.CrossRefGoogle Scholar
  52. van Gog, T., Paas, F., & van Merrienboer, J. J. G. (2006). Effects of process-oriented worked examples on troubleshooting transfer performance. Learning and Instruction, 16(2), 154–164.CrossRefGoogle Scholar
  53. Vaughn, K. E., Dunlosky, J., & Rawson, K. A. (2016). Effects of successive relearning on recall: does relearning override the effects of initial learning criterion? Memory and Cognition, 44, 897–909.CrossRefGoogle Scholar
  54. Weinstein, Y. Lawrence, J. S., Tran, N., Frye, A. A. (2013). How and how much do students really study? Tracking study habits with the diary method. Poster presented at the 54th Annual Meeting of the Psychonomic Society; Nov. 14–17; Toronto, ON, Canada.Google Scholar
  55. Wilkinson, L., & Task Force on Statistical Inference, American Psychological Association, Science Directorate. (1999). Statistical methods in psychology journals: guidelines and explanations. American Psychologist, 54, 594–604.CrossRefGoogle Scholar
  56. Woloshyn, V. E., Paivio, A., & Pressley, M. (1994). Use of elaborative interrogation to help students acquire information consistent with prior knowledge and information inconsistent with prior knowledge. Journal of Educational Psychology, 86(1), 79–89.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Psychological SciencesKent State UniversityKentUSA

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