Intelligent Adaptive Testing Using Machine Learning Techniques
ilustraciones, gráficas
- Autores:
-
Cáliz Viñas, Arcesio Jose
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/82665
- Palabra clave:
- 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación
Devsis - evaluación
Evaluación curricular
Evaluación académica
Devsis - evaluation
Curriculum evaluation
Machine Learning
Reinforcement Learning
Neural Network
Adaptive Tests
Item Response Theory
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
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Intelligent Adaptive Testing Using Machine Learning Techniques |
| dc.title.translated.spa.fl_str_mv |
Test Adaptativos Inteligene Usando Técnicas de Aprendizae de Máquina |
| title |
Intelligent Adaptive Testing Using Machine Learning Techniques |
| spellingShingle |
Intelligent Adaptive Testing Using Machine Learning Techniques 000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Devsis - evaluación Evaluación curricular Evaluación académica Devsis - evaluation Curriculum evaluation Machine Learning Reinforcement Learning Neural Network Adaptive Tests Item Response Theory |
| title_short |
Intelligent Adaptive Testing Using Machine Learning Techniques |
| title_full |
Intelligent Adaptive Testing Using Machine Learning Techniques |
| title_fullStr |
Intelligent Adaptive Testing Using Machine Learning Techniques |
| title_full_unstemmed |
Intelligent Adaptive Testing Using Machine Learning Techniques |
| title_sort |
Intelligent Adaptive Testing Using Machine Learning Techniques |
| dc.creator.fl_str_mv |
Cáliz Viñas, Arcesio Jose |
| dc.contributor.advisor.none.fl_str_mv |
Montenegro Díaz, Álvaro Mauricio |
| dc.contributor.author.none.fl_str_mv |
Cáliz Viñas, Arcesio Jose |
| dc.subject.ddc.spa.fl_str_mv |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación |
| topic |
000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computación Devsis - evaluación Evaluación curricular Evaluación académica Devsis - evaluation Curriculum evaluation Machine Learning Reinforcement Learning Neural Network Adaptive Tests Item Response Theory |
| dc.subject.lemb.spa.fl_str_mv |
Devsis - evaluación Evaluación curricular Evaluación académica |
| dc.subject.lemb.eng.fl_str_mv |
Devsis - evaluation Curriculum evaluation |
| dc.subject.proposal.eng.fl_str_mv |
Machine Learning Reinforcement Learning Neural Network Adaptive Tests Item Response Theory |
| description |
ilustraciones, gráficas |
| publishDate |
2022 |
| dc.date.accessioned.none.fl_str_mv |
2022-11-08T19:05:10Z |
| dc.date.available.none.fl_str_mv |
2022-11-08T19:05:10Z |
| dc.date.issued.none.fl_str_mv |
2022-08-01 |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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Software |
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https://repositorio.unal.edu.co/handle/unal/82665 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/82665 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
| dc.language.iso.spa.fl_str_mv |
eng |
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eng |
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RedCol LaReferencia |
| dc.relation.references.spa.fl_str_mv |
Approximately Optimal Approximate Reinforcement Learning Spinning Up in Deep Reinforcement Learning Trust Region Policy Optimization A Global Information Approach to Computerized Adaptive Testing An Introduction to Multivariate Statistical Analysis Testlet-Based Multidimensional Adaptive Testing Computerized Adaptive Testing: Theory and Practice Generating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory Applications Theory of Statistical Estimation Practical Methods of Optimization Statistical Inference A model for testing with multidimensional items Deep Reinforcement Learning with Double Q-learning Human-level control through deep reinforcement learning Learning from Delayed Rewards Reinforcement Learning: State-of-the-Art Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Multidimensional adaptive testing On Information and Sufficiency Multidimensional adaptive testing with constraints on test content Reinforcement Learning: An Introduction Multidimensional Adaptive Testing with Optimal Design Criteria for~Item Selection Multidimensional Item Response Theory Deep Reinforcement Learning in Action AI and Machine Learning for Coders mirt: A Multidimensional Item Response Theory Package for the R Environment Some latent trait models and their use in inferring an examinee's ability Applications of Item Response Theory to Practical Testing Problems Loglinear multidimensional IRT models for polytomously scored items |
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Atribución-NoComercial 4.0 Internacional |
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info:eu-repo/semantics/openAccess |
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v, 42 páginas |
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application/pdf |
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Universidad Nacional De Colombia |
| dc.publisher.program.spa.fl_str_mv |
Bogotá - Ciencias - Doctorado en Ciencias - Estadística |
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Departamento de Estadística |
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Facultad de Ciencias |
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Bogotá, Colombia |
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Universidad Nacional de Colombia - Sede Bogotá |
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Universidad Nacional de Colombia |
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Atribución-NoComercial 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Montenegro Díaz, Álvaro Mauricio6f12c8a30723630b64edb57f3cf861a1Cáliz Viñas, Arcesio Josec142a0d36cfc0d9dca063c0093cc54462022-11-08T19:05:10Z2022-11-08T19:05:10Z2022-08-01https://repositorio.unal.edu.co/handle/unal/82665Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficasIntelligent adaptive tests allow to perform evaluations that reduce the number of questions, improve the estimation, and adapt to the person’s answers. A decision to make in the design of these tests is the method for choosing the next question. There is no method that proves to be the best from the perspective of the improvements implied by an adaptive test. In this work, we explore the use of reinforcement learning algorithms in conjunction with deep learning algorithms for question choice. The results show that under certain conditions and depending on the algorithm used, these new methods achieve competent results in terms of the number of questions asked and the accuracy of the estimation compared to traditional statistical methods. (Texto tomado de la fuente)Los test adaptativos inteligentes permiten realizar evaluaciones que reducen el número de preguntas, mejoran la estimación y se adaptan a las respuestas de la persona. Una decisión a tomar en el diseño de estas pruebas, es el método para escoger la siguiente pregunta. No existe un método que demuestre ser el mejor desde la perspectiva de las mejoras implicadas en un test adaptativo. En este trabajo exploramos el uso de algoritmos de aprendizaje por refuerzo en conjunto con algoritmos de aprendizaje profundo para la escogencia de las preguntas. Los resultados muestran que bajo ciertas condiciones y dependiendo del algoritmo utilizado, estos nuevos métodos logran resultados competentes en términos del número de preguntas hechas y la exactitud de la estimación comparándolos con los métodos estadísticos tradicionales.MaestríaMagíster en Ciencias - Estadísticav, 42 páginasapplication/pdfengUniversidad Nacional De ColombiaBogotá - Ciencias - Doctorado en Ciencias - EstadísticaDepartamento de EstadísticaFacultad de CienciasBogotá, ColombiaUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generales::005 - Programación, programas, datos de computaciónDevsis - evaluaciónEvaluación curricularEvaluación académicaDevsis - evaluationCurriculum evaluationMachine LearningReinforcement LearningNeural NetworkAdaptive TestsItem Response TheoryIntelligent Adaptive Testing Using Machine Learning TechniquesTest Adaptativos Inteligene Usando Técnicas de Aprendizae de MáquinaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionSoftwarehttp://purl.org/redcol/resource_type/TMRedColLaReferenciaApproximately Optimal Approximate Reinforcement LearningSpinning Up in Deep Reinforcement LearningTrust Region Policy OptimizationA Global Information Approach to Computerized Adaptive TestingAn Introduction to Multivariate Statistical AnalysisTestlet-Based Multidimensional Adaptive TestingComputerized Adaptive Testing: Theory and PracticeGenerating Adaptive and Non-Adaptive Test Interfaces for Multidimensional Item Response Theory ApplicationsTheory of Statistical EstimationPractical Methods of OptimizationStatistical InferenceA model for testing with multidimensional itemsDeep Reinforcement Learning with Double Q-learningHuman-level control through deep reinforcement learningLearning from Delayed RewardsReinforcement Learning: State-of-the-ArtBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftMultidimensional adaptive testingOn Information and SufficiencyMultidimensional adaptive testing with constraints on test contentReinforcement Learning: An IntroductionMultidimensional Adaptive Testing with Optimal Design Criteria for~Item SelectionMultidimensional Item Response TheoryDeep Reinforcement Learning in ActionAI and Machine Learning for Codersmirt: A Multidimensional Item Response Theory Package for the R EnvironmentSome latent trait models and their use in inferring an examinee's abilityApplications of Item Response Theory to Practical Testing ProblemsLoglinear multidimensional IRT models for polytomously scored itemsInvestigadoresLICENSElicense.txtlicense.txttext/plain; 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