A Teachable Machine Application for Portuguese Language Psychogenesis Trough Children’s Handwriting

Ricardo Lucas Pires, Armando Paulo da Silva, Eduardo Filgueiras Damasceno

Abstract


Handwriting development involves fine motor control, perception, and visual-motor integration skills, crucially developed during the literacy phase. This text emphasizes psych-pedagogical professionals' role in monitoring written language's psychogenesis. Early childhood educators are pivotal in initiating literacy acquisition, necessitating continuous training to enhance their pedagogical practices. While the literacy environment significantly influences knowledge acquisition by fostering student interaction and curiosity, challenges such as school organization and imposed goals can divert educators' focus from identifying learning evolution or failure due to cognitive disorders. Recognizing the importance of literacy, educators must undergo ongoing training to create conducive literacy environments for comprehensive student development. The daunting task of teaching many students to read and write requires educators to consider variations in language writing, incorporating psychological elements and cultural backgrounds. This work introduces a tool leveraging artificial intelligence and machine learning algorithms to assist educators in monitoring and quickly identifying the stage of language writing formation in 6 to 7-year-old beginners. The prototype aids early childhood educators in categorizing and identifying each child's stage while detecting potential motor or cognitive deficits.

Keywords


Handwriting Development; Language Writing Formation; Machine Learning; Teachable Machine; User Experience

References


W. Park, G. Korres, S. Tahir, and M. Eid, “Evaluation of Handwriting Skills in Children with Learning Difficulties,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2019, pp. 150–159. doi: https://doi.org/10.1007/978-3-030-23563-5_13 .

Rowe, D. W., Davis, Z. G., & Piestrzynski, L. (2023). Adult Supports for Preschool Writers During Learning Centers. Reading Research Quarterly, 58(4), 539–560. https://doi.org/10.1002/rrq.506

J. E. Dockrell, C. R. Marshall, and D. Wyse, “Teachers’ reported practices for teaching writing in England,” Read Writ, vol. 29, no. 3, pp. 409–434, Mar. 2016, doi: https://doi.org/10.1007/s11145-015-9605-9 .

N. Bonneton-Botté, L. Miramand, R. Bailly, and C. Pons, “Teaching and Rehabilitation of Handwriting for Children in the Digital Age: Issues and Challenges,” Children, vol. 10, no. 7. Multidisciplinary Digital Publishing Institute (MDPI), Jul. 01, 2023. doi: https://doi.org/10.3390/children10071096 .

Barakina, E. Y., Popova, A. v., Gorokhova, S. S., & Voskovskaya, A. S. (2021). Digital Technologies and Artificial Intelligence Technologies in Education. European Journal of Contemporary Education, 10(2), 285–298. https://doi.org/10.13187/ejced.2021.2.285

Fellasufah, F., & Mustadi, A.. Cursive handwriting skills of primary school preservice teachers. Journal of Education and Learning (EduLearn), 13(4), 482–489, 2019. https://doi.org/10.11591/edulearn.v13i4.13504

D. S. Qi and S. Lapkin, “Exploring the role of noticing in a three-stage second language writing task,” J Second Lang Writ, vol. 10, no. 4, 2001, doi: https://doi.org/10.1016/S1060-3743(01)00046-7

M. González-López, “Teaching management to reading and writing in children of elementary school,” International Journal of Educational Administration, Management, and Leadership, 2021, doi: https://doi.org/10.51629/ijeamal.v2i1.17 .

A. M. Re, F. De Vita, C. Cornoldi, and S. Schmidt, “Copy Skills and Writing Abilities in Children with and without Specific Learning Disabilities,” J Learn Disabil, vol. 56, no. 5, 2023, doi: https://doi.org/10.1177/00222194231157089 .

Lavoie, N., Morin, M.-F., Coallier, M., & Alamargot, D. An explicit multicomponent alphabet writing instruction program in grade 1 to improve writing skills. European Journal of Psychology of Education, 35(2), 333–355. 2020. https://doi.org/10.1007/s10212-019-00428-6

Rodríguez, C., & Villarroel, R. Predicting Handwriting Difficulties Through Spelling Processes. Journal of Learning Disabilities, 50(5), 504–510. 2017 https://doi.org/10.1177/0022219416633863

Kandel, S., Soler, O., Valdois, S., & Gros, C. Graphemes as Motor Units in the Acquisition of Writing Skills. Reading and Writing, 19(3), 313–337. 2006 https://doi.org/10.1007/s11145-005-4321-5

J. Mombach, C. Ferreira, J. Felix, R. Salvini, and F. Soares, “Mirrored and Rotated Letters in Children Spellings: An Automatic Analysis Approach,” in 2020 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE), London, ON, Canada: IEEE, Aug. 2020, pp. 1–4. doi: https://doi.org/10.1109/CCECE47787.2020.9255765 .

Arcon, N., Klein, P. D., & Dombroski, J. D. Effects of Dictation, Speech to Text, and Handwriting on the Written Composition of Elementary School English Language Learners. Reading & Writing Quarterly, 33(6), 533–548. 2017 https://doi.org/10.1080/10573569.2016.1253513

M. Longcamp, M. T. Zerbato-Poudou, and J. L. Velay, “The influence of writing practice on letter recognition in preschool children: A comparison between handwriting and typing,” Acta Psychol (Amst), vol. 119, no. 1, 2005, doi: 10.1016/j.actpsy.2004.10.019.

C. Marquardt, M. Diaz Meyer, M. Schneider, and R. Hilgemann, “Learning handwriting at school – A teachers’ survey on actual problems and future options,” Trends in Neuroscience and Education, vol. 5, no. 3. 2016. doi: 1 https://doi.org/0.1016/j.tine.2016.07.001 .

J. Mombach and F. Soares, “Designing an App for Remotely Children’s Spelling Assessment,” in Learning and Collaboration Technologies: New Challenges and Learning Experiences, vol. 12784, P. Zaphiris and A. Ioannou, Eds., Cham: Springer International Publishing, 2021, pp. 92–107. doi: https://doi.org/10.1007/978-3-030-77889-7 .

J. Memon, M. Sami, R. A. Khan, and M. Uddin, “Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR),” IEEE Access, vol. 8. Institute of Electrical and Electronics Engineers Inc., pp. 142642–142668, 2020. doi: https://doi.org/10.1109/ACCESS.2020.3012542 .

Y. Weng and C. Xia, “A New Deep Learning-Based Handwritten Character Recognition System on Mobile Computing Devices,” Mobile Networks and Applications, vol. 25, no. 2, pp. 402–411, Apr. 2020, doi: https://doi.org/10.1007/s11036-019-01243-5.

B. Rajyagor and R. M. Rakholia, “Handwritten Character Recognition using Deep Learning,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 6, pp. 5815–5819, 2020, doi: https://doi.org/10.35940/ijrte.F8608.038620 .

Verulava, T., Darbaidze, M., Baramia, M., & Bouadze, C.-M. Online Learning of Students with Special Needs: Teachers’ Perspectives. International Journal of Early Childhood Special Education, 14(1), 01–07, 2022. https://doi.org/10.9756/INT-JECSE/V14I1.221001

J. Su and W. Yang, “Artificial intelligence in early childhood education: A scoping review,” Computers and Education: Artificial Intelligence, vol. 3. Elsevier B.V., Jan. 01, 2022. doi: https://doi.org/10.1016/j.caeai.2022.100049 .

P. Yogendra Prasad, D. Prasad, N. Malleswari, M. N. Shetty, and N. Gupta, “Implementation of Machine Learning Based Google Teachable Machine in Early Childhood Education,” Article in International Journal of Early Childhood Special Education, vol. 14, p. 2022, Available: https://www.researchgate.net/publication/360438764

C. Rodrigues, J. Afonso, and P. Tomé, “Mobile application webservice performance analysis: Restful services with JSON and XML,” in Communications in Computer and Information Science, 2021. doi: https://doi.org/10.1007/978-3-642-24355-4 .

K. Peffers, T. Tuunanen, M. A. Rothenberger, and S. Chatterjee, “A design science research methodology for information systems research,” Journal of Management Information Systems, vol. 24, no. 3, 2017, doi: https://doi.org/10.2753/MIS0742-1222240302 .

S. R. Al-Taai, N. M. Azize, Z. A. Thoeny, H. Imran, L. F. A. Bernardo, and Z. Al-Khafaji, “XGBoost Prediction Model Optimized with Bayesian for the Compressive Strength of Eco-Friendly Concrete Containing Ground Granulated Blast Furnace Slag and Recycled Coarse Aggregate,” Applied Sciences (Switzerland), vol. 13, no. 15, Aug. 2023, doi: https://doi.org/10.3390/app13158889 .

Damasceno, E. F., Fernandes, L. B., Silva, A. P. da, & Moreira, S. T. (2024). An Evaluation of Immersive Laboratory in Microbiology Teachings. Creative Education, 15(08), 1718–1732. https://doi.org/10.4236/ce.2024.158104

Yanikoglu, B., Gogus, A., & Inal, E. Use of handwriting recognition technologies in tablet-based learning modules for first grade education. Educational Technology Research and Development, 65(5), 1369–1388, 2017. https://doi.org/10.1007/s11423-017-9532-3

Richter, J., Lachner, A, Jacob, L., Bilgenroth, F., & Scheiter, K. Self‐concept but not prior knowledge moderates effects of different implementations of computer‐assisted inquiry learning activities on students’ learning. Journal of Computer Assisted Learning, 38(4), 1141–1159, 2022. https://doi.org/10.1111/jcal.12673

Koh, J. H. L., & Kan, R. Y. P. Students’ use of learning management systems and desired e-learning experiences: are they ready for next generation digital learning environments? Higher Education Research & Development, 40(5), 995–1010, 2021 https://doi.org/10.1080/07294360.2020.1799949




DOI: https://doi.org/10.11591/edulearn.v20i3.23688

Refbacks

  • There are currently no refbacks.


Copyright (c) 2026 Intelektual Pustaka Media Utama

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Journal of Education and Learning (EduLearn)
p-ISSN: 2089-9823; e-ISSN: 2302-9277
Published by Intelektual Pustaka Media Utama (IPMU) in collaboration with the Institute of Advanced Engineering and Science (IAES).

View EduLearn Stats