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

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

Abstract

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.

Keywords

  • Self-explanation
  • Rubric
  • Automatic summarization
  • NLP

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References

  1. Rittle-Johnson, B.: Promoting transfer: effects of self-explanation and direct instruction. Child Dev. 77(1), 1–15 (2006)

    CrossRef  Google Scholar 

  2. Bisra, K., Liu, Q., Nesbit, J.C., Salimi, F., Winne, P.H.: Inducing self-explanation: a meta-analysis. Educ. Psychol. Rev. 30(3), 703–725 (2018). https://doi.org/10.1007/s10648-018-9434-x

    CrossRef  Google Scholar 

  3. McNamara, D.S., Levinstein, I.B., Boonthum, C.: iSTART: interactive strategy training for active reading and thinking. Beh. Res. Methods, Inst. Comput. 36(2), 222–233 (2004)

    CrossRef  Google Scholar 

  4. Nakamoto, R., Flanagan, B., Takam K., Dai Y., Ogata, H.: Identifying students’ stuck points using self-explanations and pen stroke data in a mathematics quiz. In: ICCE 2021, 2021.11.22–26 (2021)

    Google Scholar 

  5. Flanagan, B., Ogata, H.: Learning analytics platform in higher education in Japan. Knowl. Manage. E-Learn. (KM&EL) 10(4), 469–484 (2018)

    Google Scholar 

  6. Reimers, N., Gurevych, I.: Sentence-BERT: sentence embeddings using Siamese BERT-Networks, arXiv preprint arXiv:1908.10084 (2019)

  7. Suzuki, M.: Pretrained Japanese BERT models, GitHub repository. https://github.com/cl-tohoku/bert-japanese. Accessed 10 Aug 2020

  8. Erkan, G., Radev, D.: LexRank: graph-based lexical centrality as salience in text summarization. arXiv:1109.2128 (2004)

  9. Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 378–382 (1971)

    CrossRef  Google Scholar 

  10. Zhang, T., Kishore, V., Wu, F., Weinberger, K.Q., Artzi, Y.: Bertscore: evaluating text generation with bert. arXiv preprint arXiv:1904.09675 (2019)

  11. Papineni, K., Roukos, S., Ward, T. & Zhu, W.: BLEU: a method for automatic evaluation of machine translation. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics (ACL 2002). Association for Computational Linguistics, USA, 311–318. https://doi.org/10.3115/1073083.1073135(2002)

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Acknowledgments

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. https://doi.org/10.1007/978-3-031-11647-6_46

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  • DOI: https://doi.org/10.1007/978-3-031-11647-6_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-11646-9

  • Online ISBN: 978-3-031-11647-6

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