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Explanation generation, not explanation expectancy, improves metacomprehension accuracy

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Abstract

The ability to monitor the status of one’s own understanding is important to accomplish academic tasks proficiently. Previous studies have shown that comprehension monitoring (metacomprehension accuracy) is generally poor, but improves when readers engage in activities that access valid cues reflecting their situation model (activities such as concept mapping or self-explaining). However, the question still remains as to which process, encoding or retrieving, causes the improvement of metacomprehension accuracy, and the findings of previous research on this matter have been inconsistent. This study examined whether college students’ metacomprehension accuracy improves when they expect, at the time of reading, that they will explain the content later (active encoding) or when they actually generate an explanation (encoding plus active retrieving). In the experiments, college students read five texts. During reading, some students expected that they would generate explanations but did not actually generate them. In contrast, some students actually generated an explanation of the text after reading. All students then rated their comprehension of each text. Finally, they completed tests on the materials. Results of both studies revealed that metacomprehension accuracy, operationalized as the association between comprehension ratings and test performance, was greater for the group that actually generated explanations than for the expectancy or control groups.

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Notes

  1. In both studies, graduate students were included as participants, but they were equally distributed between the three groups.

  2. One may argue that it is hard for participants to distinguish between what they knew before the experiment and what they learned in the experiment, and prior knowledge should be assessed at the start of the experiment. The study by Rozenblit and Keil (2002), however, demonstrated that college students could accurately evaluate the initial states of their knowledge even after reading the expert’s explanation. Rozenblit and Keil (2002) also showed that before reading experts’ explanations or answering test questions, participants overestimated their understanding of the device mechanism. This is why prior knowledge was assessed at the end of the experiment in our study.

  3. Some research argued that prior knowledge does not contribute to relative metacomprehension accuracy (Griffin et al. 2009). Note, however, that this does not mean that participants do not profit from prior knowledge of one or several texts to varying degrees when making judgments of their comprehension. For example, knowledge of baseball does not help readers improve relative metacomprehension accuracy when reading baseball texts. If, however, a set of materials consists of texts not only about baseball, but also about music, prior knowledge about baseball does contribute to accurate discrimination between well-learned materials (that is, baseball texts) and less well-learned materials (music texts) as suggested in Glenberg and Epstein (1987). In this context, we assume that prior knowledge might have positive impact when participants read texts.

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Acknowledgments

Appreciation is expressed to Shin-ichi Ichikawa for his helpful comments on an earlier version of this article. I also thank Toshihiko Endo, Mikiko Seo, Takanori Fukushi, Soichiro Iwata, Mika Morozumi, Hitoshi Nakajima, Yoshikazu Suzuki, Yuji Watanabe, Yuka Masamune, Arisa Morizawa, and Satoru Sugimoto for data collection, Tetsuya Kawamoto and Aiko Komoto for data analysis, and Emmanuel Manalo and Stephanie Coop for English proofreading. This research was partly supported in part by grant from Research Fellow of the Japan Society for the Promotion of Science (23-9955).

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  1. Research Center for Advanced Science and Technology, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8904, Japan

    Tatsushi Fukaya

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Correspondence to Tatsushi Fukaya.

Appendices

Appendix A. Example of text

Title: Toilet tank (Experiment 2)

When you press a toilet handle, you push water into a siphon pipe. Once the water flows through the pipe, air pressure causes all of the water in the tank flow rapidly through the tube into the bowl. The float drops with the water level, and water then flows into the tank. This makes the float rise, stopping the water flow.

figure a

Appendix B. Example of inference questions

Title: Toilet tank

(1) If you press a toilet handle, how does the disk move?

The disk goes up into the funnel. (Correct answer)

(2) Is there any problem if the disk is too small?

With a small disk, you can’t push enough water into the funnel, so the water never flows through a siphon pipe. (Correct answer)

(3) When the float drops with the water level, in what way does water flow into the tank?

The drop of the float opens the valve to the water inlet. (Correct answer)

(4) Why is there a float in a tank?

With the float, you can open the valve when the tank is empty, and close the valve when the tank is full. (Correct answer)

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Fukaya, T. Explanation generation, not explanation expectancy, improves metacomprehension accuracy. Metacognition Learning 8, 1–18 (2013). https://doi.org/10.1007/s11409-012-9093-0

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