Ir para o menu de navegação principal Ir para o conteúdo principal Ir para o rodapé

v. 20 n. 2 (2020)
Julio - Diciembre

ARTICULOS DE INVESTIGACION

FINDING TOPICS IN CREATIVE WRITING ON ENVIRONMENTAL PRESERVATION FOR BETTER TEACHING STRATEGIES: A CASE OF STUDY IN AN ELEMENTARY SCHOOL FROM COLOMBIA

https://doi.org/10.19053/1900771X.v20.n2.2020.13391

Publicado 2021-09-02

  • Camilo Arturo Suárez Ballesteros
    • ,
  • María Claudia Esperanza Bernal Camargo
    • ,
  • Nidia Yaneth Torres Merchán

Camilo Arturo Suárez Ballesteros
Universidad Pedagógica y Tecnológica de Colombia, Facultad de Ciencias, Tunja

María Claudia Esperanza Bernal Camargo
I.E. San Jerónimo Emiliani

Nidia Yaneth Torres Merchán
Universidad Pedagógica y Tecnológica de Colombia, Sede Tunja

Resumo

En esta investigación, se evaluaron ensayos sobre la preservación de árboles de estudiantes de cuarto grado (escuela primaria de Colombia) con Latent Dirichlet Allocation (LDA). El objetivo fue extraer los temas fundamentales, para comprender el comportamiento y la conciencia de los estudiantes hacia el medio ambiente a partir de la escritura creativa. Los resultados computacionales sugieren que las reflexiones del estudiante sobre la preservación del medio ambiente se centran en cinco temas principales en: Enseñar-Aprender a cuidar el medio ambiente, Explorar-descubrir el medio ambiente, Bienestar del medio ambiente, Preocupación por el medio ambiente y Restauración y conservación del entorno. Este análisis de texto por LDA puede complementar el análisis manual de los docentes, evitando el sesgo de veracidad y permitiendo potenciar las estrategias de enseñanza.

Palavras-chave

escritura reflexiva, modelado de temas, modelo LDA, alumnos de primaria, aplicación de tecnología

PDF (Español)

Referências

  1. Aletras, N., & Stevenson, M. (2013). Evaluating topic coherence using distributional semantics. In Proceedings of the 10th International Conference on Computational Semantics, IWCS 2013 - Long Papers.
  2. Anandarajan, M., Hill, C., & Nolan, T. (2019). Cluster Analysis: Modeling Groups in Text. https://doi.org/10.1007/978-3-319-95663-3_7
  3. Balyan, R., McCarthy, K. S., & McNamara, D. S. (2020). Applying Natural Language Processing and Hierarchical Machine Learning Approaches to Text Difficulty Classification. International Journal of Artificial Intelligence in Education, 30(3), 337–370. https://doi.org/10.1007/s40593-020-00201-7
  4. Bhardwaj, A., Reddy, M., Setlur, S., Govindaraju, V., & Ramachandrula, S. (2010). Latent Dirichlet Allocation based Writer Identification in Offline handwriting. ACM International Conference Proceeding Series, 357–362. https://doi.org/10.1145/1815330.1815376
  5. Blei, D. M., Ng, A. Y., & Edu, J. B. (2003). Latent Dirichlet Allocation Michael I. Jordan. In Journal of Machine Learning Research (Vol. 3).
  6. Blei, D. M., Ng, A. Y., & Jordan, M. T. (2002). Latent dirichlet allocation. In Advances in Neural Information Processing Systems.
  7. Chang, J., Boyd-Graber, J., Gerrish, S., Wang, C., & Blei, D. M. (2009). Reading tea leaves: How humans interpret topic models. In Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. http://rexa.info
  8. Chen, B., Chen, X., & Xing, W. (2015). “Twitter Archeology” of Learning Analytics and Knowledge Conferences. https://doi.org/10.1145/2723576.2723584
  9. Chen, Y., Yu, B., Zhang, X., & Yu, Y. (2016). Topic modeling for evaluating students’ reflective writing: A case study of pre-Service teachers’ journals. ACM International Conference Proceeding Series, 25-29-Apri, 1–5. https://doi.org/10.1145/2883851.2883951
  10. Chuang, J., Gupta, S., Manning, C. D., & Heer, J. (2013). Topic model diagnostics: Assessing domain relevance via topical alignment. In 30th International Conference on Machine Learning, ICML 2013 (Issue PART 2). http://vis.stanford.edu/topic-diagnostics
  11. Erkens, M., Bodemer, D., & Hoppe, H. U. (2016). Improving collaborative learning in the classroom: Text mining based grouping and representing. International Journal of Computer-Supported Collaborative Learning, 11(4), 387–415. https://doi.org/10.1007/s11412-016-9243-5
  12. Evaluation of Topic Modeling: Topic Coherence | DataScience+. (n.d.). Retrieved January 19, 2021, from https://datascienceplus.com/evaluation-of-topic-modeling-topiccoherence/
  13. Ezen-Can, A., & Boyer, K. E. (n.d.). Unsupervised Classification of Student Dialogue Acts With Query-Likelihood Clustering.
  14. Faustmann, G. (2018). Improved learning of academic writing reducing complexity by modeling academic texts. CSEDU 2018 - Proceedings of the 10th International Conference on Computer Supported Education, 1, 447–453. https://doi.org/10.5220/0006792204470453
  15. Fischer, C., Pardos, Z. A., Baker, R. S., Williams, J. J., Smyth, P., Yu, R., Slater, S., Baker, R., & Warschauer, M. (2020). Mining Big Data in Education: Affordances and Challenges. Review of Research in Education, 44(1), 130–160. https://doi.org/10.3102/0091732X20903304
  16. Greene, D., O’Callaghan, D., & Cunningham, P. (2014). How many topics? Stability analysis for topic models. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8724 LNAI(PART 1), 498–513. https://doi.org/10.1007/978-3-662-44848-9_32
  17. Gui, L., Leng, J., Pergola, G., Zhou, Y., Xu, R., & He, Y. (n.d.). Neural Topic Model with Reinforcement Learning. Retrieved January 25, 2021, from http://mallet.cs.umass.edu/import-stoplist.php
  18. Jelodar, H., Wang, Y., Yuan, C., Feng, X., Jiang, X., Li, Y., & Zhao, L. (2019). Latent Dirichlet allocation (LDA) and topic modeling: models, applications, a survey. Multimedia Tools and Applications, 78(11), 15169–15211. https://doi.org/10.1007/s11042-018-6894-4
  19. Kakkonen, T., Myller, N., & Sutinen, E. (2006). Applying latent Dirichlet allocation to automatic essay grading. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4139 LNAI, 110–120. https://doi.org/10.1007/11816508_13
  20. Kherwa, P., & Bansal, P. (2018). Latent Semantic Analysis: An Approach to Understand Semantic of Text. International Conference on Current Trends in Computer, Electrical, Electronics and Communication, CTCEEC 2017, 870–874. https://doi.org/10.1109/CTCEEC.2017.8455018
  21. Landauer, T. K., McNamara, D., Dennis, S., & Kintsch, W. (2011). The Handbook of Latent Semantic Analysis.
  22. Lei, L., Deng, Y., & Liu, D. (2020). Examining research topics with a dependency-based noun phrase extraction method: a case in accounting. Library Hi Tech. https://doi.org/10.1108/LHT-12-2019-0247
  23. Lenhart, J., Lenhard, W., Vaahtoranta, E., & Suggate, S. (2020). More than words: Narrator engagement during storytelling increases children’s word learning, story comprehension, and on-task behavior. Early Childhood Research Quarterly, 51, 338–351. https://doi.org/10.1016/j.ecresq.2019.12.009
  24. Letsche, T. A., & Berry, M. W. (1997). Large-scale information retrieval with latent semantic indexing. Information Sciences, 100(1–4), 105–137. https://doi.org/10.1016/S0020-0255(97)00044-3
  25. Liu, S., Peng, X., Cheng, H. N. H., Liu, Z., Sun, J., & Yang, C. (2019). Unfolding Sentimental and Behavioral Tendencies of Learners’ Concerned Topics From Course Reviews in a MOOC. Journal of Educational Computing Research, 57(3), 670–696. https://doi.org/10.1177/0735633118757181
  26. Liu, Z. (2013). High Performance Latent Dirichlet Allocation for Text Mining.
  27. Louvigne, S., Kato, Y., Rubens, N., & Ueno, M. (2014). Goal-based messages recommendation utilizing latent dirichlet allocation. Proceedings - IEEE 14th International Conference on Advanced Learning Technologies, ICALT 2014, 464–468. https://doi.org/10.1109/ICALT.2014.138
  28. Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & Mccallum, A. (n.d.). Optimizing Semantic Coherence in Topic Models.
  29. Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011). Optimizing semantic coherence in topic models. In EMNLP 2011 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference. Association for Computational Linguistics.
  30. Ming, N. C. & Ming, V. L. (2012). Automated Predictive Assessment from Unstructured Student Writing. In Automated predcitive assessment from unstructured student writing (Issue c).
  31. Murillo, L. (2013). Cultura ambiental: un estudio desde las dimensiones de valor, creencias, actitudes y comportamientos ambientales. Producción + Limpia, 8(2), 94–105. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=95426468&lang=es&site=ehost-live
  32. O’Callaghan, D., Greene, D., Carthy, J., & Cunningham, P. (2015). An analysis of the coherence of descriptors in topic modeling. Expert Systems with Applications, 42(13), 5645–5657. https://doi.org/10.1016/j.eswa.2015.02.055
  33. Prabhakaran, S. (2018). Gensim Topic Modeling - A Guide to Building Best LDA models. Machine Learning Plus. https://www.machinelearningplus.com/nlp/topic-modelinggensim-python/
  34. Ramadhan, S., Sukma, E., & Indriyani, V. (2019). Environmental education and disaster mitigation through language learning. IOP Conference Series: Earth and Environmental Science, 314(1), 012054. https://doi.org/10.1088/1755-1315/314/1/012054
  35. Röder, M., Both, A., & Hinneburg, A. (2015). Exploring the space of topic coherence measures. WSDM 2015 - Proceedings of the 8th ACM International Conference on Web Search and Data Mining, 399–408. https://doi.org/10.1145/2684822.2685324
  36. Sarkar, D. (2019). Text Analytics with Python. In Text Analytics with Python. https://doi.org/10.1007/978-1-4842-4354-1
  37. Seufert, S., Guggemos, J., & Sonderegger, S. (2019). Learning analytics in higher education using peer-feedback and self-assessment: Use case of an academic writing course. CSEDU 2019 - Proceedings of the 11th International Conference on Computer Supported
  38. Education, 2, 315–322. https://doi.org/10.5220/0007714603150322
  39. Song, Y., Pan, S., Liu, S., Zhou, M. X., & Qian, W. (2009). Topic and keyword re-ranking for LDA-based topic modeling. International Conference on Information and Knowledge Management, Proceedings, 1757–1760. https://doi.org/10.1145/1645953.1646223
  40. Stevens, K., Kegelmeyer, P., Andrzejewski, D., & Buttler, D. (2012). Exploring topic coherence over many models and many topics. In EMNLP-CoNLL 2012 - 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language
  41. Learning, Proceedings of the Conference. Association for Computational Linguistics. http://mallet.cs.umass.edu/
  42. Topic Model Evaluation - High Demand Skills. (n.d.). Retrieved January 19, 2021, from https://highdemandskills.com/topic-model-evaluation/
  43. Topic modeling | Computing for the Social Sciences. (n.d.). Retrieved January 19, 2021, from https://cfss.uchicago.edu/notes/topic-modeling/
  44. Tran, B. X., Nghiem, S., Sahin, O., Vu, T. M., Ha, G. H., Vu, G. T., Pham, H. Q., Do, H. T., Latkin, C. A., Tam, W., Ho, C. S. H., & Ho, R. C. M. (2019). Modeling research topics for artificial intelligence applications in medicine: Latent dirichlet allocation application study.
  45. Journal of Medical Internet Research, 21(11), e15511. https://doi.org/10.2196/15511
  46. Valdez, D., Pickett, A. C., & Goodson, P. (2018). Topic Modeling: Latent Semantic Analysis for the Social Sciences. Social Science Quarterly, 99(5), 1665–1679. https://doi.org/10.1111/ssqu.12528
  47. Wang, T., & Liu, C. Y. (2017). JSEA: A Program Comprehension Tool Adopting LDA-based Topic Modeling. In IJACSA) International Journal of Advanced Computer Science and Applications (Vol. 8, Issue 3). https://github.com/jseaTool/JSEA
  48. Xing, W., Lee, H. S., & Shibani, A. (2020). Identifying patterns in students’ scientific argumentation: content analysis through text mining using Latent Dirichlet Allocation. Educational Technology Research and Development, 68(5), 2185–2214. https://doi.org/10.1007/s11423-020-09761-w
  49. Xun, G., Gopalakrishnan, V., Li, F. M. Y., Gao, J., & Zhang, A. (2017). Topic discovery for short texts using word embeddings. Proceedings - IEEE International Conference on Data Mining, ICDM, 1299–1304. https://doi.org/10.1109/ICDM.2016.33
  50. Yoshida, T., Hisano, R., & Ohnishi, T. (2020). Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back. ArXiv. http://arxiv.org/abs/2002.10855
  51. Zheng, A. X., Cong, Z. X., Wang, J. R., Li, J., Yang, H. H., & Chen, G. N. (2013). Highlyefficient peroxidase-like catalytic activity of graphene dots for biosensing. Biosensors and Bioelectronics, 49, 519–524. https://doi.org/10.1016/j.bios.2013.05.038
  52. Zhu, G., Xing, W., Costa, S., Scardamalia, M., & Pei, B. (2019). Exploring emotional and cognitive dynamics of Knowledge Building in grades 1 and 2. User Modeling and UserAdapted Interaction, 29(4), 789–820. https://doi.org/10.1007/s11257-019-09241-8

Downloads

Não há dados estatísticos.