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How Effective is Example Generation for Learning Declarative Concepts?

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Abstract

Declarative concepts (i.e., key terms and corresponding definitions for abstract concepts) represent foundational knowledge that students learn in many content domains. Thus, investigating techniques to enhance concept learning is of critical importance. Various theoretical accounts support the expectation that example generation will serve this purpose, but few studies have examined the efficacy of this technique. We conducted three experiments involving 487 undergraduates to investigate the effects of example generation on concept learning and examined factors that may moderate its effectiveness. Students read a short text that introduced eight concepts. Some students were then prompted to generate concrete examples of each concept followed by definition restudy, whereas others only restudied definitions for the same amount of time. Two days later, students completed final tests involving example generation and definition cued recall. Meta-analytic outcomes indicated that example generation yields moderate improvements in learning of declarative concepts, relative to restudy only. Each experiment also included additional groups to investigate potential moderators. Example generation tended to be more effective with spaced versus massed restudy. Despite strong correlations between the quality of examples generated during practice and final test performance, experimental manipulations that improved example quality did not improve learning. In sum, the current work establishes that example generation enhances concept learning and provides an important foundation for further investigating factors that moderate its benefits to learning.

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Notes

  1. We selected definition restudy as the activity for the comparison group (as opposed to no practice or some other encoding strategy) because it arguably provides the most appropriate business-as-usual comparison, given that restudy is the technique that students often report using most frequently during self-regulated learning (e.g., Hartwig and Dunlosky 2012; Karpicke et al. 2009; Susser and McCabe 2013). To foreshadow, experiments 2 and 3 also afforded comparison of example generation to another learning technique (retrieval practice).

  2. On average, participants spent 2.7 min (SE = 0.1) studying the text and 13.3 s (SE = 0.7) per concept definition; similar outcomes were observed in experiments 2 and 3 [text M = 2.8 min (SE = 0.1) for the text and M = 12.7 s (SE = 0.5) per concept definition in experiment 2; M = 2.7 min (SE = 0.1) for the text and M = 11.6 s (SE = 0.5) per concept definition in experiment 3]. Neither text study time nor concept study time differed significantly as a function of group in any experiment, and including these variables as covariates in analyses of final test performance did not qualitatively change any statistical conclusions.

  3. Given that timing of restudy did not significantly moderate the benefit of example generation over restudy-only in experiment 1, we dropped the timing manipulation from experiment 2 to keep the design from becoming too unwieldy with the addition of other extension groups. Given the advantage of example generation involving spaced versus massed restudy, we used spaced restudy for all groups in experiments 2 and 3.

  4. Cognitive overload from completing both tasks was unlikely, given that learners were prompted to complete the two tasks sequentially, spent a similar amount of time for each component task as learners in the single-task groups (example generation only or recall only; see Table 5), and produced responses of similar quality as learners in the single-task groups (see Tables 3 and 4).

  5. Simmons et al. (2011) particularly recommend that the field “should be more tolerant of imperfections in results … Underpowered studies with perfect results are the ones that should invite extra scrutiny.”

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Acknowledgments

The research reported here was supported by a James S. McDonnell Foundation 21st Century Science Initiative in Bridging Brain, Mind and Behavior Collaborative Award.

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Authors and Affiliations

  1. Department of Psychological Sciences, Kent State University, P.O. Box 5190, Kent, OH, 44242-0001, USA

    Katherine A. Rawson & John Dunlosky

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  1. Katherine A. Rawson
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Correspondence to Katherine A. Rawson.

Appendix: Excerpt from Text and Concept Definitions Used in Experiments 1–3

Appendix: Excerpt from Text and Concept Definitions Used in Experiments 1–3

Although attribution often involves the logical kind of reasoning just described, this is not always the case. In fact, it is subject to several kinds of biases. One of the most important is known as the correspondence bias, which is the tendency to attribute other people’s behavior to internal causes to a greater extent than is actually justified while underestimating the effect of the situation. The correspondence bias can lead us to false conclusions about others. Another bias in our attributions concerns our own behavior. The self-serving bias is the tendency to attribute positive outcomes to our own traits or characteristics (internal causes) but negative outcomes to factor beyond our control (external causes). Finally, the just-world hypothesis refers to the strong desire or need people have to believe that the world is an orderly, predictable, and just place, where people get what they deserve. This influences our attributions because when we encounter evidence suggesting that the world is not just, we sometimes persuade ourselves that no injustice has occurred.

Correspondence bias

The tendency to attribute other people’s behavior to internal causes to a greater extent than is actually justified while underestimating the effect of the situation

Self-serving bias

The tendency to attribute positive outcomes to our own traits or characteristics (internal causes) but negative outcomes to factor beyond our control (external causes)

Just-world hypothesis

The strong desire or need people have to believe that the world is an orderly, predictable, and just place, where people get what they deserve

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Rawson, K.A., Dunlosky, J. How Effective is Example Generation for Learning Declarative Concepts?. Educ Psychol Rev 28, 649–672 (2016). https://doi.org/10.1007/s10648-016-9377-z

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