Article Summary: Kurtz et al. (2001) Learning by Analogical Bootstrapping

Motivation: To explore the effect of different degrees of comparison on learning through analogical encoding.

Analogies in education: Analogical learning compares two similar concepts, such as water flow and electricity, to bootstrap learning. Bootstrapping means use existing resources – in this case, prior knowledge – to solve a problem. Therefore, analogies bootstrap learning by transferring knowledge about a well-known concept to a new concept (Pirolli & Anderson, 1985). For example, learning about electricity is difficult because electricity is invisible and all visible effects of electricity, such as a light turning on, do not demonstrate how electricity itself works. Instead of purely describing electricity through abstract terms, instructors can make an analogy to flowing water, which is something that learners have prior knowledge of and can visualize. This particular analogy is so prevalent that it is commonly used in college-level classes in engineering and physics.

Despite the potential benefits, analogical learning has many pitfalls. First, analogies are not perfect. Electricity is not exactly like flowing water, and the imperfections in the analogy can lead to pervasive and persistent misconceptions (Brown & Clement, 1989). Second, learners can struggle to recognize relevant similarities between concepts unless they are explicitly pointed out (Gick & Holyoak, 1980). Third, not discussed in Kurtz et al.’s article but highly relevant to issues of equity, making analogies relies upon finding a base concept that all learners understand well. If a psychology professor described how human process information by making an analogy to how computers process information, students who don’t know how computers process information would gain nothing and might feel inadequate for not knowing something that the professor is implying is common knowledge.

In response to these pitfalls, Kurtz et al. proposed a different way of teaching analogies – analogical encoding. Analogical encoding asks students to compare two analogous situations, both from the new concept that they are learning. Kurtz et al. argue that comparing similar situations with different surface features will allow learners to recognize structural information more readily and facilitate abstraction and transfer. In this research based on structure-mapping theory (Gentner, 1983), they induced different degrees of comparisons between analogous situations to explore the impact of depth of comparison on learning outcomes.

Experiment 1: Participants were given two analogous scenarios to compare to each other to learn about heat flow. The four conditions were

  1. no comparison task (control)
  2. participants view scenarios separately and are explicitly asked to compare scenarios and describe them together
  3. participants view scenarios at the same time and are explicitly asked to compare scenarios and describe them together
  4. same as condition 3 + a mapping task in which participants matched features in one scenario to their analog in the other scenario

The fourth condition, which had to map features between scenarios, performed better than the control group on assessments that asked participants to rate the differences and similarities between analogous heat flow scenarios, meaning that they were better able to separate the surface details of specific scenarios from the structure of the underlying concept of heat flow.

Experiment 2: Because the fourth condition in Exp. 1 combined making comparisons with a mapping task, it’s unclear whether the differences among groups was due solely to the mapping task or if the combination of both tasks would be necessary to achieve the same benefits. Thus, Experiment 2 teased these two features apart to determine the independent effect of the mapping task. Kurtz et al. found that the mapping task by itself was not sufficient to achieve the same level of performance that the combination of the two tasks did.

Why this is important: Analogical learning can be a powerful tool in learning, but it has many potential shortcomings and relies on all learners to have shared and well-understood knowledge of a previous concept. Analogical encoding, on the other hand, does not have the same limitations. It offers an opportunity to, so to speak, build something from nothing. By simultaneously comparing two scenarios through a description task and mapping task, learners can develop a depth of knowledge that does not rely upon prior knowledge. Furthermore, the explicit prompting from an external source, like an instructor or instructional materials, ensures that all students are leveraging this learning strategy, giving in consistent results. Therefore, analogical encoding is a way of bootstrapping learning without relying upon learners to come in with the same knowledge or tools.

Brown, D. E., & Clement, J. (1989). Overcoming misconceptions via analogical reasoning: Abstract transfer versus explanatory model construction. Instructional Science18(4), 237-261.

Gentner, D. (1983). Structure-mapping: A theoretical framework for analogy. Cognitive Science7(2), 155-170.

Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology12(3), 306-355.

Kurtz, K. J., Miao, C. H., & Gentner, D. (2001). Learning by analogical bootstrapping. The Journal of the Learning Sciences10(4), 417-446.

Pirolli, P. L., & Anderson, J. R. (1985). The role of learning from examples in the acquisition of recursive programming skills. Canadian Journal of Psychology39(2), 240.

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One thought on “Article Summary: Kurtz et al. (2001) Learning by Analogical Bootstrapping

  1. Pingback: Article Summary: Series Introduction | Lauren Margulieux

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