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.
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Declarative concepts are a type of relational category, which is an umbrella term used to encompass various subtypes of concepts that involve the representation of relations between features or entities. Research on relational categories is relatively scant compared to the vast literature on feature-based categories (i.e., categories that are represented by a set of independent, perceptual attributes). Goldwater and Schalk (2016) note that relational categories are distinct from feature-based categories in content and representational form; relational categories also differ from feature-based categories in the extent to which they involve processes such as structural alignment, mapping, integration, and analogical reasoning. Thus, “without a specific focus on relational categories and knowledge, the degree to which cognitive theories [i.e., theories about feature-based categories] can inform education is ultimately limited” (p. 730). In contrast, research on the learning of other kinds of relational categories can be leveraged to inform investigation of declarative concept learning of interest here. To foreshadow, we lean heavily on prior research on the learning of rule-based concepts (another type of relational category) to motivate our design and predictions in the current research.
Concerning long-term learning, no parallels can be drawn because the rule-based concepts literature has almost always used immediate rather than delayed tests to assess learning (e.g., Paas and van Merrienboer 1994).
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Appendix A: Methods and Results for the Paraphrase Definition Group in Experiment 1
Appendix A: Methods and Results for the Paraphrase Definition Group in Experiment 1
Experiment 1 included a fourth group (a paraphrase definition group). We included this group to ensure that our measures were sensitive enough to detect group differences if our example-based learning groups did not differ on final tests. We also included this group to conceptually replicate the basic effects of provided examples versus definition restudy found in Rawson et al. (2015) and of generated examples versus definition restudy found in Rawson and Dunlosky (2016).
On the first trial for each concept during the practice phase, the paraphrase definition group received the concept definition that all participants received during initial study. For the second trial for each concept, they received a paraphrased version of the definition. We decided to include a paraphrased version of the definition because we were concerned that students would not spend time actively processing the trial if the definition was written exactly the same as the previous trial. The paraphrase definition group received four practice trials per concept (two with the original definition and the other two with the paraphrased version of the definition).
The following findings refer to performance on the example classification posttest. For examples that were studied by the provided examples group, all groups numerically outperformed the paraphrase definition group (M = 51%, SE = 4, ds = .12–.32). For examples that were studied by provided and combination groups, all groups numerically outperformed the paraphrase definition group (M = 47%, SE = 4, ds = .10–.47). For novel examples, all groups numerically outperformed the paraphrase definition group (M = 47%, SE = 4, ds = .07–.34).
For the cued recall posttest, the paraphrase definition group performed similarly to all other groups (M = 26%, SE = 3, ds = .01–.19). Finally, concerning efficiency, the paraphrase definition group outperformed all other groups (M = 5 min, SE < 1 min, ds = 1.39–3.18).
Relative to the example-based learning groups, the paraphrase definition group did better than expected, which in hindsight is perhaps not surprising. Balch (2005) and Glover and Corkill (1987) both found that studying paraphrased definitions is more beneficial than restudying verbatim definitions.
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Zamary, A., Rawson, K.A. Which Technique is most Effective for Learning Declarative Concepts—Provided Examples, Generated Examples, or Both?. Educ Psychol Rev 30, 275–301 (2018). https://doi.org/10.1007/s10648-016-9396-9
- Declarative concepts
- Example-based learning
- Provided examples
- Generated examples