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An Automatic Self-explanation Sample Answer Generation with Knowledge Components in a Math Quiz

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13356)


Little research has addressed how systems can use the learning process of self-explanation to provide scaffolding or feedback. Here, we propose a model automatically generating sample self-explanations with knowledge components required to solve a math quiz. The proposed model contains three steps: vectorization, clustering, and extraction. In an experiment using 1434 self-explanation answers from 25 quizzes, we found 72% of the quizzes generated sample answers with all necessary knowledge components. The similarity between human-created and machine-generated sentences was 0.719, with a significant correlation of R = 0.48 for the best performing generation model by BERTScore. These results suggest that our model can generate sample answers with the necessary key knowledge components and be further improved by using the BERTScore.


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This work was partly supported by JSPS Grant-in-Aid for Scientific Research 20H01722, 21K19824, and NEDO JPNP20006, JPNP18013.

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Correspondence to Ryosuke Nakamoto .

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Nakamoto, R., Flanagan, B., Dai, Y., Takami, K., Ogata, H. (2022). An Automatic Self-explanation Sample Answer Generation with Knowledge Components in a Math Quiz. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds) Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham.

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  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

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