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Application of the Recomposition Method to Mind Map and Experimental Verification of Learning Effect

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Human Interface and the Management of Information (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14016))

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Abstract

The Mind Map (MM) is a thinking tool that represents understanding diagrammatically, and its creation and use by oneself or by sharing with others are accepted as useful activities. Understanding the map creator’s understanding represented in MM is an essential activity in such situations. In this study, the promotion of the understanding of MM has been investigated. As a method of promotion, a recomposition method successfully used for the concept map is adopted. If the conventional recomposition method is applied to MM, a complete decomposition of MM is performed (Full Recomposition method: FR). In addition to FR, we have proposed a method in which only the leaves of MM are recomposed by the students (Leaves Recomposition method: LR). LR reduces the cognitive load in the recomposition but it is expected to keep the learning effect because recomposing a leaf requires an understanding of the path from the root to the leaf. In this paper, we conducted comparative experiments between FR and LR. The results of pre-tests and post-tests showed that both methods significantly increased the test scores and there was no significant difference between FR and LR. Then, the growth rates of map scores for LR were much higher than those of FR, and the results of the questionnaire suggested that LR was an easier way to recompose MM. These results suggest that (1) the recomposition of MM has a learning effect, and (2) LR can reduce the cognitive load of recomposition while keeping the learning effect.

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Notes

  1. 1.

    Unlike CM, multiple nodes with the same name are allowed to exist in MM. This and the path dependence of meaning taken together lead that nodes with the same name that can have different meanings depending on the path.

  2. 2.

    Note that this ratio was calculated based on the questionnaire conducted in these experiments, but since only 69 out of 74 students responded to the questionnaire, it does not take into account the experience of the 5 students who did not respond to the questionnaire.

  3. 3.

    Since there were two students who did not have TOEIC scores, they were excluded from the average score calculation.

  4. 4.

    Note that, as already mentioned in the previous section, two of the students did not have TOEIC scores, so we assigned them to be in a different group.

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Acknowledgments

This work was partially supported by JSPS KAKENHI Grant Numbers 21H03565 and 19KK0052.

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Correspondence to Kodai Watanabe .

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Watanabe, K., Pinandito, A., Nurmaya, Hayashi, Y., Hirashima, T. (2023). Application of the Recomposition Method to Mind Map and Experimental Verification of Learning Effect. In: Mori, H., Asahi, Y. (eds) Human Interface and the Management of Information. HCII 2023. Lecture Notes in Computer Science, vol 14016. Springer, Cham. https://doi.org/10.1007/978-3-031-35129-7_28

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  • DOI: https://doi.org/10.1007/978-3-031-35129-7_28

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