Algoritmos de Reconhecimento de Dígitos para Integração de Equações Manuscritas em Sistemas Tutores Inteligentes
Resumo
Sistemas Tutores Inteligente (STIs) têm sido amplamente utilizados para auxiliar no aprendizado de matemática. No entanto, a diferença na forma de inserção de soluções nos STIs, que requer o uso de um teclado, em comparação com a prática padrão de escrever à mão, pode levar a problemas de usabilidade e prejudicar a aprendizagem. Para superar essa limitação, pesquisas recentes têm explorado o reconhecimento de caracteres escritos à mão em papel como entrada para os STIs. Porém, existe uma lacuna de conhecimento em relação ao desempenho dos algoritmos de reconhecimento de dígitos avançados no contexto de operações matemáticas básicas. Este artigo compara quatro algoritmos de última geração para o reconhecimento de dígitos em problemas matemáticos de adição e subtração. Os resultados revelam que o algoritmo BTTR obteve o melhor desempenho em termos de acurácia, enquanto o algoritmo SAN apresentou um bom equilíbrio entre acurácia e velocidade de reconhecimento. Essas descobertas são relevantes para pesquisadores e desenvolvedores ao selecionar os algoritmos mais adequados para o desenvolvimento de STIs baseados em entrada escrita à mão.
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Rosa, D., Cordeiro, F. R., Carvalho, R., Souza, E., Chevtchenko, S., Rodrigues, L., Marinho, M., Vieira, T., and Macario, V. (2023). Recognizing handwritten mathematical expressions of vertical addition and subtraction. arXiv preprint arXiv:2308.05820.
Soofi, A. A. and Ahmed, M. U. (2019). A systematic review of domains, techniques, delivery modes and validation methods for intelligent tutoring systems. International Journal of Advanced Computer Science and Applications, 10(3).
Steenbergen-Hu, S. and Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on k–12 students’ mathematical learning. Journal of educational psychology, 105(4):970.
Vu, T.-M., Phan, K.-M., Ung, H.-Q., Nguyen, C.-T., and Nakagawa, M. (2021). Clustering of handwritten mathematical expressions for computer-assisted marking. IEICE TRANSACTIONS on Information and Systems, 104(2):275–284.
Wang, G., Bowditch, N., Zeleznik, R., Kwon, M., and LaViola, J. J. (2016). A tablet-based math tutor for beginning algebra. Revolutionizing Education with Digital Ink: The Impact of Pen and Touch Technology on Education, pages 91–102.
Yuan, Y., Liu, X., Dikubab, W., Liu, H., Ji, Z., Wu, Z., and Bai, X. (2022a). Syntax-aware network for handwritten mathematical expression recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4553–4562.
Yuan, Y., Liu, X., Dikubab, W., Liu, H., Ji, Z., Wu, Z., and Bai, X. (2022b). Syntax-aware network for handwritten mathematical expression recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4553–4562.
Zhao, W. and Gao, L. (2022). Comer: Modeling coverage for transformer-based handwritten mathematical expression recognition. In Avidan, S., Brostow, G., Cissé, M., Farinella, G. M., and Hassner, T., editors, Computer Vision – ECCV 2022, pages 392–408, Cham. Springer Nature Switzerland.
Zhao, W., Gao, L., Yan, Z., Peng, S., Du, L., and Zhang, Z. (2021). Handwritten mathematical expression recognition with bidirectionally trained transformer. In Lladós, J., Lopresti, D., and Uchida, S., editors, Document Analysis and Recognition – ICDAR 2021, pages 570–584, Cham. Springer International Publishing.
Anthony, L., Yang, J., and Koedinger, K. R. (2005). Evaluation of multimodal input for entering mathematical equations on the computer. In CHI’05 Extended Abstracts on Human Factors in Computing Systems, pages 1184–1187.
Anthony, L., Yang, J., and Koedinger, K. R. (2007). Benefits of handwritten input for students learning algebra equation solving. Frontiers in Artificial Intelligence and Applications, 158:521.
Anthony, L., Yang, J., and Koedinger, K. R. (2012). A paradigm for handwriting-based intelligent tutors. International Journal of Human-Computer Studies, 70(11):866–887.
Davis, S. R., DeCapito, C., Nelson, E., Sharma, K., and Hand, E. M. (2020). Homework helper: Providing valuable feedback on math mistakes. In Advances in Visual Computing: 15th International Symposium, ISVC 2020, San Diego, CA, USA, October 5–7, 2020, Proceedings, Part II 15, pages 533–544. Springer.
Hillmayr, D., Ziernwald, L., Reinhold, F., Hofer, S. I., and Reiss, K. M. (2020). The potential of digital tools to enhance mathematics and science learning in secondary schools: A context-specific meta-analysis. Computers & Education, 153:103897.
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Le, A. D. and Nakagawa, M. (2016). A system for recognizing online handwritten mathematical expressions by using improved structural analysis. International Journal on Document Analysis and Recognition (IJDAR), 19:305–319.
Li, B., Yuan, Y., Liang, D., Liu, X., Ji, Z., Bai, J., Liu, W., and Bai, X. (2022). When counting meets hmer: Counting-aware network for handwritten mathematical expression recognition. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXVIII, pages 197–214. Springer.
Morais, F. and Jaques, P. A. (2022). Does handwriting impact learning on math tutoring systems? Informatics in Education, 21(1):55–90.
Mouchère, H., Viard-Gaudin, C., Zanibbi, R., and Garain, U. (2014). Icfhr 2014 competition on recognition of on-line handwritten mathematical expressions (crohme 2014). In 2014 14th International Conference on Frontiers in Handwriting Recognition, pages 791–796.
Nkambou, R., Mizoguchi, R., and Bourdeau, J. (2010). Advances in intelligent tutoring systems, volume 308. Springer Science & Business Media.
Patel, N., Thakkar, M., Rabadiya, B., Patel, D., Malvi, S., Sharma, A., and Lomas, D. (2022). Equitable access to intelligent tutoring systems through paper-digital integration. In Intelligent Tutoring Systems: 18th International Conference, ITS 2022, Bucharest, Romania, June 29–July 1, 2022, Proceedings, pages 255–263. Springer.
Rosa, D., Cordeiro, F. R., Carvalho, R., Souza, E., Chevtchenko, S., Rodrigues, L., Marinho, M., Vieira, T., and Macario, V. (2023). Recognizing handwritten mathematical expressions of vertical addition and subtraction. arXiv preprint arXiv:2308.05820.
Soofi, A. A. and Ahmed, M. U. (2019). A systematic review of domains, techniques, delivery modes and validation methods for intelligent tutoring systems. International Journal of Advanced Computer Science and Applications, 10(3).
Steenbergen-Hu, S. and Cooper, H. (2013). A meta-analysis of the effectiveness of intelligent tutoring systems on k–12 students’ mathematical learning. Journal of educational psychology, 105(4):970.
Vu, T.-M., Phan, K.-M., Ung, H.-Q., Nguyen, C.-T., and Nakagawa, M. (2021). Clustering of handwritten mathematical expressions for computer-assisted marking. IEICE TRANSACTIONS on Information and Systems, 104(2):275–284.
Wang, G., Bowditch, N., Zeleznik, R., Kwon, M., and LaViola, J. J. (2016). A tablet-based math tutor for beginning algebra. Revolutionizing Education with Digital Ink: The Impact of Pen and Touch Technology on Education, pages 91–102.
Yuan, Y., Liu, X., Dikubab, W., Liu, H., Ji, Z., Wu, Z., and Bai, X. (2022a). Syntax-aware network for handwritten mathematical expression recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4553–4562.
Yuan, Y., Liu, X., Dikubab, W., Liu, H., Ji, Z., Wu, Z., and Bai, X. (2022b). Syntax-aware network for handwritten mathematical expression recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4553–4562.
Zhao, W. and Gao, L. (2022). Comer: Modeling coverage for transformer-based handwritten mathematical expression recognition. In Avidan, S., Brostow, G., Cissé, M., Farinella, G. M., and Hassner, T., editors, Computer Vision – ECCV 2022, pages 392–408, Cham. Springer Nature Switzerland.
Zhao, W., Gao, L., Yan, Z., Peng, S., Du, L., and Zhang, Z. (2021). Handwritten mathematical expression recognition with bidirectionally trained transformer. In Lladós, J., Lopresti, D., and Uchida, S., editors, Document Analysis and Recognition – ICDAR 2021, pages 570–584, Cham. Springer International Publishing.
Publicado
06/11/2023
Como Citar
CHEVTCHENKO, Sergio et al.
Algoritmos de Reconhecimento de Dígitos para Integração de Equações Manuscritas em Sistemas Tutores Inteligentes. In: SIMPÓSIO BRASILEIRO DE INFORMÁTICA NA EDUCAÇÃO (SBIE), 34. , 2023, Passo Fundo/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 1442-1453.
DOI: https://doi.org/10.5753/sbie.2023.235237.
