Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato

ilustraciones, diagramas

Autores:: Bofill Barrera, Joan Gabriel

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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dc.title.spa.fl_str_mv	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato
dc.title.translated.eng.fl_str_mv	Machine learning based method for scoring short english essays from a high school student sample
title	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato
spellingShingle	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 370 - Educación::373 - Educación secundaria PLN Aprendizaje supervisado Transformers Ensamble de modelos SVR Calificación automática de ensayos NLP Automatic essay grading Supervised learning SVR Kaggle contest Ensamble of models Método de evaluación Procesamiento de datos Evaluation methods Data processing aprendizaje supervisado supervised learning
title_short	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato
title_full	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato
title_fullStr	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato
title_full_unstemmed	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato
title_sort	Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato
dc.creator.fl_str_mv	Bofill Barrera, Joan Gabriel
dc.contributor.advisor.spa.fl_str_mv	Niño Vásquez, Luis Fernando
dc.contributor.author.spa.fl_str_mv	Bofill Barrera, Joan Gabriel
dc.contributor.referee.spa.fl_str_mv	León Guzmán, Elizabeth
dc.contributor.researchgroup.spa.fl_str_mv	laboratorio de Investigación en Sistemas Inteligentes Lisi
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 370 - Educación::373 - Educación secundaria
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 370 - Educación::373 - Educación secundaria PLN Aprendizaje supervisado Transformers Ensamble de modelos SVR Calificación automática de ensayos NLP Automatic essay grading Supervised learning SVR Kaggle contest Ensamble of models Método de evaluación Procesamiento de datos Evaluation methods Data processing aprendizaje supervisado supervised learning
dc.subject.proposal.spa.fl_str_mv	PLN Aprendizaje supervisado Transformers Ensamble de modelos SVR Calificación automática de ensayos
dc.subject.proposal.eng.fl_str_mv	NLP Automatic essay grading Supervised learning SVR Kaggle contest Ensamble of models
dc.subject.unesco.spa.fl_str_mv	Método de evaluación Procesamiento de datos
dc.subject.unesco.eng.fl_str_mv	Evaluation methods Data processing
dc.subject.wikidata.spa.fl_str_mv	aprendizaje supervisado
dc.subject.wikidata.eng.fl_str_mv	supervised learning
description	ilustraciones, diagramas
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-05-28T22:15:47Z
dc.date.available.none.fl_str_mv	2024-05-28T22:15:47Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
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dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86173 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
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Alex Franklin, Natalie Rambis, Maggie Meg Benner, Perpetual Baffour, Ryan Holbrook, and u. Scott Crossley, “Feedback Prize - English Language Learning. Kaggle. ,” 2022. S. A. Crossley, K. Kyle, and D. S. Mcnamara, “To Aggregate or Not? Linguistic Features in Automatic Essay Scoring and Feedback Systems,” Grantee Submission, vol. 8, no. 1, 2015. C. Ramineni and D. M. Williamson, “Automated essay scoring: Psychometric guidelines and practices,” Assessing Writing, pp. 25–39, 2013. S. P. Balfour, “Assessing Writing in MOOCs: Automated Essay Scoring and Calibrated PeerReview™,” Research & Practice in Assessment, pp. 40–48, 2013. S. Cushing Weigle, “Validation of automated scores of TOEFL iBT tasks against non-test indicators of writing ability,” Language Testing, vol. 27, no. 3, pp. 335–353, 2010. K. Taghipour, Robust trait-specific essay scoring using neural networks and density es-timators. PhD thesis, National University of Singapore, Singapore, 2017. H. Shi and V. Aryadoust, “Correction to: A systematic review of automated writing evaluation systems,” Education and Information Technologies, vol. 28, pp. 6189–6190, 5 2023. P. C. Jackson, Toward human-level artificial intelligence: Representation and computation of meaning in natural language. Dover Publications, 11 2019. E. Mayfield and C. P. Rosé, “LightSIDE,” in Handbook of Automated Essay Evaluation, Routledge, 1 ed., 2013. M. Shermis and J. Burstein, Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge, 1 ed., 2013. S. Burrows, I. Gurevych, and B. Stein, “The Eras and Trends of Automatic Short Answer Grading,” Int J Artif Intell Educ, vol. 25, pp. 60–117, 2015. K. Zupanc and Z. Bosnic, “Automated essay evaluation with semantic analysis,” El-sevier, vol. 120, pp. 118–132, 2017. D. Yan, A. A. Rupp, and P. W. Foltz, Handbook of Automated Scoring; Theory into Practice. Chapman and Hall/CRC., 1 ed., 2020. B. Kitchenham, O. Pearl Brereton, D. Budgen, M. 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Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, 2019. E. Mayfield and A. W. Black, “Should You Fine-Tune BERT for Automated Essay Scoring?,” Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics, pp. 151–162, 7 2020. D. Ramesh and S. K. Sanampudi, “An automated essay scoring systems: a systematic literature review,” Artificial Intelligence Review, vol. 55, pp. 2495–2527, 3 2022. H. Yannakoudakis and R. Cummins, “Evaluating the performance of automated text scoring systems,” Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Educational Applications, pp. 213–223, 2015. R. Bhatt, M. Patel, G. Srivastava, and V. Mago, “A Graph Based Approach to Automate Essay Evaluation,” in Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, vol. 2020-Octob, pp. 4379–4385, 2020. Z. Ke and V. Ng, “Automated essay scoring: A survey of the state of the art,” in IJCAI International Joint Conference on Artificial Intelligence, vol. 2019-Augus, pp. 6300–6308, 2019. J. Devlin, M.-W. Chang, K. Lee, K. T. Google, and A. I. Language, “BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding,” arXiv preprint arXiv:1810.04805, 2018. D. Castro-Castro, R. Lannes-Losada, M. Maritxalar, I. Niebla, C. Pérez-Marqués, N. Álamo-Suárez, and A. Pons-Porrata, “A multilingual application for automated essay scoring,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 5290 LNAI, pp. 243–251, 2008. P. U. Rodriguez, A. Jafari, and C. M. Ormerod, “Language models and Automated Essay Scoring,” ArXiv, 9 2019. S. Ghannay, B. Favre, Y. Estève, and N. 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Microsoft, “GitHub - Microsoft/LightGBM:Light Gradient Boosting Machine.” G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.Y. Liu, “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” 31st Conference on Neural Information Processing Systems (NIPS 2017), pp. 3149–3157, 2017. T. Chen and C. Guestrin, “XGBoost: A scalable tree boosting system,” in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, vol. 13-17-August-2016, pp. 785–794, Association for Computing Machinery, 8 2016. J.J.Espinosa-Zúñiga, “Aplicación de algoritmos Random Forest y XGBoost en una base de solicitudes de tarjetas de crédito,” Ingeniería Investigación y Tecnología, vol. 21, no. 3, pp. 1–16, 2020. C. Cortes, V. Vapnik, and L. Saitta, “Support-Vector Networks,” Machine Leaming, vol. 20, pp. 273–297, 1995. M. Awad, R. Khanna, M. Awad, and R. Khanna, Support vector regression. Efficient learning machines: Theories, concepts, and applications for engineers and system designers. Apress, 2015. M.-C. Popescu, V. E. Balas, L. Perescu-Popescu, and N. Mastorakis, “Multilayer Perceptron and Neural Networks,” WSEAS Transactions on Circuits and Systems, vol. 8, no. 7, pp. 579–588, 2009. T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language Models are Few-Shot Learners,” ArXiv, 5 2020. T. Wolf, L. Debut, V. Sanh, J. Chaumond, C. Delangue, A. Moi, P. Cistac, T. Rault, R. Louf, M. Funtowicz, J. Davison, S. Shleifer, P. Von Platen, C. Ma, Y. Jernite, J. Plu, C. Xu, T. Le Scao, S. Gugger, M. Drame, Q. Lhoest, and A. M. Rush, “Transformers: State-of-the-Art Natural Language Processing,” Association for Computational Linguistics, pp. 38–45, 2020. P. He, X. Liu, J. Gao, W. Chen, and M. Dynamics, “Deberta: Decoding-enhanced bert with disentangled attention,” Conference paper at ICLR, 2021. P. Zhang, “Longformer-based Automated Writing Assessment for English Language Learners Stanford CS224N Custom Project,” tech. rep., Standford, 2023. K. K. Y. Chan, T. Bond, and Z. Yan, “Application of an Automated Essay Scoring engine to English writing assessment using Many-Facet Rasch Measurement,” Language Testing, vol. 40, pp. 61–85, 1 2023. A. Mizumoto and M. Eguchi, “Exploring the potential of using an AI language model for automated essay scoring,” Research Methods in Applied Linguistics, vol. 2, 8 2023. V. Mohan, M. J. Ilamathi, and M. Nithya, “Preprocessing Techniques for Text Mining-An Overview,” International Journal of Computer Science & Communication Networks, vol. 5, no. 1, pp. 7–16, 2015. M. Siino, I. 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Inoue, “Multi-Sample Dropout for Accelerated Training and Better Generalization,” ArXiv, 5 2019. A. Stanciu, I. Cristescu, E. M. Ciuperca, and C. E. Cˆırnu, “Using an ensemble of transformer-based models for automated writing evaluation of essays,” in 14th International Conference on Education and New Learning Technologies, (Palma, Spain), pp. 5276–5282, IATED, 7 2022. H. Zhang, Y. Gong, Y. Shen, W. Li, J. Lv, N. Duan, and W. Chen, “Poolingformer: Long Document Modeling with Pooling Attention,” ArXiv, 2021. A. Aziz, M. Akram Hossain, and A. Nowshed Chy, “CSECU-DSG at SemEval-2023 Task 4: Fine-tuning DeBERTa Transformer Model with Cross-fold Training and Multisample Dropout for Human Values Identification,” tech. rep., Department of Computer Science and Engineering University of Chittagong, Chattogram, Bangladesh, 2023. E. Mayfield and A. W. 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dc.format.extent.spa.fl_str_mv	vii, 61 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Bogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ingeniería
dc.publisher.place.spa.fl_str_mv	Bogotá, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Bogotá
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vásquez, Luis Fernandobc784b82735e16fe53653c3f5c8f3bbeBofill Barrera, Joan Gabriel6124025a95a25c44620bb2a9a8957b68León Guzmán, Elizabethlaboratorio de Investigación en Sistemas Inteligentes Lisi2024-05-28T22:15:47Z2024-05-28T22:15:47Z2024https://repositorio.unal.edu.co/handle/unal/86173Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, diagramasEste trabajo aborda el desafío de la calificación automática de ensayos argumentativos en inglés escritos por estudiantes de bachillerato que están aprendiendo el inglés como segunda lengua. El objetivo general es implementar un método automético basado en aprendizaje supervisado que permita resolver esta tarea para 6 indicadores en simultáneo: Cohesión, Sintaxis, Vocabulario, Gramática, Fraseología y Convenciones en escala de 1 a 5. Para lograrlo, se realiza un análisis descriptivo de los datos, se aplican procedimientos de preprocesamiento y se extraen características relevantes; se exploran diferentes estrategias, técnicas de representación y modelos desde algunos clásicos hasta aquellos con mejor desempeño en la actualidad, evaluando en cada iteración su rendimiento, contrastándola con las calificaciones humanas. Luego, se presenta el modelo con menor error que está basado principalmente en DeBERTa al cual se le aplican distintas técnicas para mejorar su desempeño y se combina con un modelo SVR que toma como características los embeddings de los textos concatenados en 10 modelos preentrenados sin fine-tuning. Con esta estrategia, el resultado se acerca bastante a las calificaciones humanas, presentando un RMSE de 0.45 sobre todos los indicadores. (Texto tomado de la fuente).This work addresses the challenge of automatically grading argumentative essays in English written by high school students that learn English as a second language. The general objective is to implement an automatic method based on supervised learning that allows solving this task for 6 indicators simultaneously: Cohesion, Syntax, Vocabulary, Grammar, Phraseology and Conventions rated on a scale from 1 to 5. To achieve this, a descriptive analysis of the data is conducted, preprocessing procedures are applied and relevant features are extracted; different strategies, representation techniques and models are explored, from some classic ones to the currently best performing models. Their performance is evaluated in each iteration, contrasting it with human ratings with a chosen measure. Then, the method with the best performance is presented, it is based mainly on DeBERTa V3 Large, where different techniques are applied to improve its performance. Finally, and is combined with a regressor model SVR that takes as features the concatenated embeddings of the texts in 10 different pretrained models. With this strategy, the result is quite close to human ratings, presenting a root mean square error of 0.45 over all indicators.MaestríaMagíster en Ingeniería - Ingeniería de Sistemas y ComputaciónSistemas inteligentesvii, 61 páginasapplication/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y ComputaciónFacultad de IngenieríaBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores370 - Educación::373 - Educación secundariaPLNAprendizaje supervisadoTransformersEnsamble de modelosSVRCalificación automática de ensayosNLPAutomatic essay gradingSupervised learningSVRKaggle contestEnsamble of modelsMétodo de evaluaciónProcesamiento de datosEvaluation methodsData processingaprendizaje supervisadosupervised learningMétodo basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachilleratoMachine learning based method for scoring short english essays from a high school student sampleTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMP. Kline, The New Psychometrics: Science, Psychology and Measurement. Routledge, 1 ed., 1999.T. N. Fitria, “Artificial intelligence (AI) technology in OpenAI ChatGPT application: A review of ChatGPT in writing English essay.,” ELT Forum: Journal of English Language Teaching, vol. 12, no. 1, pp. 44–58, 2023.E. B. Page, “Grading Essays by Computer: Progress Report. Proceedings of the 1966 Invitational Conference on Testing Problems.,” Princeton, N.J. Educational Testing Service, pp. 87–100, 1967.E. Page, “The use of the computer in analyzing student essays,” Int Rev Educ, pp. 210–225, 1968.K. L. Gwet, “Handbook of inter-rater reliability: The definitive guide to measuring the extent of agreement among raters,” Advanced Analytics, LLC, 2014.Mark D. S., “Contrasting State-of-the-Art in the Machine Scoring of Short-Form Cons-tructed Responses,” Educational Assessment, vol. 20, no. 1, pp. 46–65, 2015.Alex Franklin, Natalie Rambis, Maggie Meg Benner, Perpetual Baffour, Ryan Holbrook, and u. Scott Crossley, “Feedback Prize - English Language Learning. Kaggle. ,” 2022.S. A. Crossley, K. Kyle, and D. S. Mcnamara, “To Aggregate or Not? Linguistic Features in Automatic Essay Scoring and Feedback Systems,” Grantee Submission, vol. 8, no. 1, 2015.C. Ramineni and D. M. Williamson, “Automated essay scoring: Psychometric guidelines and practices,” Assessing Writing, pp. 25–39, 2013.S. P. Balfour, “Assessing Writing in MOOCs: Automated Essay Scoring and Calibrated PeerReview™,” Research & Practice in Assessment, pp. 40–48, 2013.S. Cushing Weigle, “Validation of automated scores of TOEFL iBT tasks against non-test indicators of writing ability,” Language Testing, vol. 27, no. 3, pp. 335–353, 2010.K. Taghipour, Robust trait-specific essay scoring using neural networks and density es-timators. PhD thesis, National University of Singapore, Singapore, 2017.H. Shi and V. Aryadoust, “Correction to: A systematic review of automated writing evaluation systems,” Education and Information Technologies, vol. 28, pp. 6189–6190, 5 2023.P. C. Jackson, Toward human-level artificial intelligence: Representation and computation of meaning in natural language. Dover Publications, 11 2019.E. Mayfield and C. P. Rosé, “LightSIDE,” in Handbook of Automated Essay Evaluation, Routledge, 1 ed., 2013.M. Shermis and J. Burstein, Handbook of Automated Essay Evaluation: Current Applications and New Directions. Routledge, 1 ed., 2013.S. Burrows, I. Gurevych, and B. Stein, “The Eras and Trends of Automatic Short Answer Grading,” Int J Artif Intell Educ, vol. 25, pp. 60–117, 2015.K. Zupanc and Z. Bosnic, “Automated essay evaluation with semantic analysis,” El-sevier, vol. 120, pp. 118–132, 2017.D. Yan, A. A. Rupp, and P. W. Foltz, Handbook of Automated Scoring; Theory into Practice. Chapman and Hall/CRC., 1 ed., 2020.B. Kitchenham, O. Pearl Brereton, D. Budgen, M. Turner, J. 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Método basado en aprendizaje automático para la calificación de ensayos cortos en inglés de una muestra de estudiantes de bachillerato

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