FINDING TOPICS IN CREATIVE WRITING ON ENVIRONMENTAL PRESERVATION FOR BETTER TEACHING STRATEGIES: A CASE OF STUDY IN AN ELEMENTARY SCHOOL FROM COLOMBIA
DOI:
https://doi.org/10.19053/1900771X.v20.n2.2020.13391Palabras clave:
escritura reflexiva, modelado de temas, modelo LDA, alumnos de primaria, aplicación de tecnologíaResumen
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.
Descargas
Referencias
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.
Anandarajan, M., Hill, C., & Nolan, T. (2019). Cluster Analysis: Modeling Groups in Text. https://doi.org/10.1007/978-3-319-95663-3_7
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
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
Blei, D. M., Ng, A. Y., & Edu, J. B. (2003). Latent Dirichlet Allocation Michael I. Jordan. In Journal of Machine Learning Research (Vol. 3).
Blei, D. M., Ng, A. Y., & Jordan, M. T. (2002). Latent dirichlet allocation. In Advances in Neural Information Processing Systems.
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
Chen, B., Chen, X., & Xing, W. (2015). “Twitter Archeology” of Learning Analytics and Knowledge Conferences. https://doi.org/10.1145/2723576.2723584
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
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
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
Evaluation of Topic Modeling: Topic Coherence | DataScience+. (n.d.). Retrieved January 19, 2021, from https://datascienceplus.com/evaluation-of-topic-modeling-topiccoherence/
Ezen-Can, A., & Boyer, K. E. (n.d.). Unsupervised Classification of Student Dialogue Acts With Query-Likelihood Clustering.
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
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
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
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
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
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
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
Landauer, T. K., McNamara, D., Dennis, S., & Kintsch, W. (2011). The Handbook of Latent Semantic Analysis.
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
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
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
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
Liu, Z. (2013). High Performance Latent Dirichlet Allocation for Text Mining.
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
Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & Mccallum, A. (n.d.). Optimizing Semantic Coherence in Topic Models.
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.
Ming, N. C. & Ming, V. L. (2012). Automated Predictive Assessment from Unstructured Student Writing. In Automated predcitive assessment from unstructured student writing (Issue c).
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
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
Prabhakaran, S. (2018). Gensim Topic Modeling - A Guide to Building Best LDA models. Machine Learning Plus. https://www.machinelearningplus.com/nlp/topic-modelinggensim-python/
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
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
Sarkar, D. (2019). Text Analytics with Python. In Text Analytics with Python. https://doi.org/10.1007/978-1-4842-4354-1
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
Education, 2, 315–322. https://doi.org/10.5220/0007714603150322
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
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
Learning, Proceedings of the Conference. Association for Computational Linguistics. http://mallet.cs.umass.edu/
Topic Model Evaluation - High Demand Skills. (n.d.). Retrieved January 19, 2021, from https://highdemandskills.com/topic-model-evaluation/
Topic modeling | Computing for the Social Sciences. (n.d.). Retrieved January 19, 2021, from https://cfss.uchicago.edu/notes/topic-modeling/
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.
Journal of Medical Internet Research, 21(11), e15511. https://doi.org/10.2196/15511
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
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
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
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
Yoshida, T., Hisano, R., & Ohnishi, T. (2020). Gaussian Hierarchical Latent Dirichlet Allocation: Bringing Polysemy Back. ArXiv. http://arxiv.org/abs/2002.10855
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
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
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Los autores deben firmar y enviar la Autorización de evaluación y publicación del artículo suministrada por la Revista, en la cual se consignan todos los aspectos involucrados a los Derechos de Autor.
Todos los artículos de la Revista Ingeniería Investigación y Desarrollo son difundidos bajo la licencia Creative Commons de Atribución (BY).
Declaración de privacidad
Los nombres y direcciones de correo electrónico introducidos en esta revista se usarán exclusivamente para los fines establecidos en ella y no se proporcionarán a terceros o para su uso con otros fines.