Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments
A methodology to classify and predict users in virtual education environments, studying the interaction of students with the platform and their performance in exams is proposed. For this, the machine learning tools, main components, clustering, fuzzy and the algorithm of the K nearest neighbor were...
- Autores:
- Tipo de recurso:
- Fecha de publicación:
- 2019
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/8754
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/8754
- Palabra clave:
- Cluster
Education
KNN
Machine learning
VLE
Cluster analysis
Clustering algorithms
E-learning
Education
Forecasting
Learning systems
Machine components
Machine learning
Motion compensation
Nearest neighbor search
Pattern recognition
Students
Cluster
Fuzzy k-means
K nearest neighbor algorithm
K-nearest neighbors
Three categories
Transition zones
Virtual education
Learning algorithms
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments |
| dc.title.alternative.none.fl_str_mv |
Metodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educación |
| title |
Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments |
| spellingShingle |
Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments Cluster Education KNN Machine learning VLE Cluster analysis Clustering algorithms E-learning Education Forecasting Learning systems Machine components Machine learning Motion compensation Nearest neighbor search Pattern recognition Students Cluster Fuzzy k-means K nearest neighbor algorithm K-nearest neighbors Three categories Transition zones Virtual education Learning algorithms |
| title_short |
Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments |
| title_full |
Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments |
| title_fullStr |
Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments |
| title_full_unstemmed |
Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments |
| title_sort |
Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments |
| dc.subject.keywords.none.fl_str_mv |
Cluster Education KNN Machine learning VLE Cluster analysis Clustering algorithms E-learning Education Forecasting Learning systems Machine components Machine learning Motion compensation Nearest neighbor search Pattern recognition Students Cluster Fuzzy k-means K nearest neighbor algorithm K-nearest neighbors Three categories Transition zones Virtual education Learning algorithms |
| topic |
Cluster Education KNN Machine learning VLE Cluster analysis Clustering algorithms E-learning Education Forecasting Learning systems Machine components Machine learning Motion compensation Nearest neighbor search Pattern recognition Students Cluster Fuzzy k-means K nearest neighbor algorithm K-nearest neighbors Three categories Transition zones Virtual education Learning algorithms |
| description |
A methodology to classify and predict users in virtual education environments, studying the interaction of students with the platform and their performance in exams is proposed. For this, the machine learning tools, main components, clustering, fuzzy and the algorithm of the K nearest neighbor were used. The methodology first relates the users according to the study variables, to then implement a cluster analysis that identifies the formation of groups. Finally uses a machine learning algorithm to classify the users according to their level of knowledge. The results show how the time a student stays in the platform is not related to belonging to the high knowledge group. Three categories of users were identified, applying the Fuzzy K-means methodology to determine transition zones between levels of knowledge. The k nearest neighbor algorithm presents the best prediction results with 91%. © 2019 Centro de Informacion Tecnologica. All Rights Reserved. |
| publishDate |
2019 |
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2019-11-06T19:05:18Z |
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2019-11-06T19:05:18Z |
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2019 |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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info:eu-repo/semantics/article |
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info:eu-repo/semantics/publishedVersion |
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Artículo |
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publishedVersion |
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Informacion Tecnologica; Vol. 30, Núm. 1; pp. 247-254 |
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0716-8756 |
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https://hdl.handle.net/20.500.12585/8754 |
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10.4067/S0718-07642019000100247 |
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Universidad Tecnológica de Bolívar |
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Repositorio UTB |
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Informacion Tecnologica; Vol. 30, Núm. 1; pp. 247-254 0716-8756 10.4067/S0718-07642019000100247 Universidad Tecnológica de Bolívar Repositorio UTB |
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https://hdl.handle.net/20.500.12585/8754 |
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eng |
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eng |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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info:eu-repo/semantics/openAccess |
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Atribución-NoComercial 4.0 Internacional |
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openAccess |
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Recurso electrónico |
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application/pdf |
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Centro de Informacion Tecnologica |
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Centro de Informacion Tecnologica |
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2019-11-06T19:05:18Z2019-11-06T19:05:18Z2019Informacion Tecnologica; Vol. 30, Núm. 1; pp. 247-2540716-8756https://hdl.handle.net/20.500.12585/875410.4067/S0718-07642019000100247Universidad Tecnológica de BolívarRepositorio UTBA methodology to classify and predict users in virtual education environments, studying the interaction of students with the platform and their performance in exams is proposed. For this, the machine learning tools, main components, clustering, fuzzy and the algorithm of the K nearest neighbor were used. The methodology first relates the users according to the study variables, to then implement a cluster analysis that identifies the formation of groups. Finally uses a machine learning algorithm to classify the users according to their level of knowledge. The results show how the time a student stays in the platform is not related to belonging to the high knowledge group. Three categories of users were identified, applying the Fuzzy K-means methodology to determine transition zones between levels of knowledge. The k nearest neighbor algorithm presents the best prediction results with 91%. © 2019 Centro de Informacion Tecnologica. All Rights Reserved.Recurso electrónicoapplication/pdfengCentro de Informacion Tecnologicahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2https://www2.scopus.com/inward/record.uri?eid=2-s2.0-85062369611&doi=10.4067%2fS0718-07642019000100247&partnerID=40&md5=b41fc73c06182541a20fd032f7cfe6b1Scopus 26031339600Scopus 57070183000Scopus 57200633636Methodology of Machine Learning for the classification and Prediction of users in Virtual Education EnvironmentsMetodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educacióninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArtículohttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1ClusterEducationKNNMachine learningVLECluster analysisClustering algorithmsE-learningEducationForecastingLearning systemsMachine componentsMachine learningMotion compensationNearest neighbor searchPattern recognitionStudentsClusterFuzzy k-meansK nearest neighbor algorithmK-nearest neighborsThree categoriesTransition zonesVirtual educationLearning algorithmsDe la Hoz Domínguez, Enrique JoséFontalvo Herrera, Tomás JoséArista-Jalife, A., Calderón-Auza, G., Fierro-Radilla, A., Nakano, M., Clasificación de Imágenes Urbanas Aéreas: Comparación entre Descriptores de Bajo Nivel y Aprendizaje Profundo (2017) Información Tecnológica, 28 (3), pp. 209-224Bayne, S., Higher education as a visual practice: Seeing through the virtual learning environment (2008) Teaching in Higher Education, 13 (4), pp. 395-410Britton, B.K., Tesser, A., Effects of time-management practices on college grades (1991) Journal of Educational Psychology, 83 (3), p. 405Carpaneto, E., Chicco, G., Napoli, R., Scutariu, M., Electricity customer classification using frequency–domain load pattern data (2006) International Journal of Electrical Power & Energy Systems, 28 (1), pp. 13-20Christensen, R., Effects of technology integration education on the attitudes of teachers and students (2002) Journal of Research on Technology in Education, 34 (4), pp. 411-433Clavero, A., Salicrú, M., Turbón, D., Sex prediction from the femur and hip bone using a sample of CT images from a Spanish population (2015) International Journal of Legal Medicine, 129 (2), pp. 373-383De La Hoz, E., Mendoza, A., Ojeda, H., Clasificación de perfiles de lectores de un periódico digital (2017) Revista U.D.C.A., Actualidad Y Divulgación Científica, 20 (2), pp. 469-478De La Hoz, E., Polo, L., Aplicación de Técnicas de Análisis de Conglomerados y Redes Neuronales Artificiales en la Evaluación del Potencial Exportador de una Empresa (2017) Información Tecnológica, 28 (4), pp. 67-74Fontalvo, T.J., De La Hoz, E., Diseño e Implementación de un Sistema de Gestión de la Calidad ISO 9001:2015 en una Universidad Colombiana (2018) Formación Universitaria, 11 (1), pp. 35-44Fontalvo, T., De La Hoz-Domínguez, E., Mendoza- Mendoza, A., Aplicación de Minería de Datos para la Clasificación de programas universitarios de Ingeniería Industrial Acreditados en alta calidad en Colombia (2018) Información Tecnológica, 29 (3)Galili, T., Dendextend: An R package for visualizing, adjusting and comparing trees of hierarchical clustering (2015) Bioinformatics, 31 (22), pp. 3718-3720. , doi.orgKataria, A., Singh, M.D., A review of data classification using k-nearest neighbour algorithm (2013) International Journal of Emerging Technology and Advanced Engineering, 3 (6), pp. 354-360Klašnja-Milićević, A., Ivanović, M., Nanopoulos, A., Recommender systems in e-learning environments: A survey of the state-of-the-art and possible extensions (2015) Artificial Intelligence Review, 44 (4), pp. 571-604. , y doi.orgKnijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C., Explaining the user experience of recommender systems (2012) User Modeling and User-Adapted Interaction, 22 (4-5), pp. 441-504Kobsa, A., Generic user modeling systems (2001) User Modeling and User-Adapted Interaction, pp. 49-63. , 1-2Liao, S.-H., Hsian, P.Y., Wu, G.L., Mining user knowledge for investigating the facebook business model: The case of Taiwan users (2014) Journal Applied Artificial Intelligence, 28 (7), pp. 712-736Melo-Solarte, D.S., Díaz, P., El Aprendizaje Afectivo y la Gamificación en Escenarios de Educación Virtual (2018) Información Tecnológica, 29 (3), pp. 237-248Morsi, R., Ibrahim, W., Williams, F., Concept maps: Development and validation of engineering curricula (2007) Frontiers in Education Conference-Global Engineering, pp. T3H–18. , y IEEENonis, S.A., Hudson, G.I., Academic performance of college students: Influence of time spent studying and working (2006) Journal of Education for Business, 81 (3), pp. 151-159Pardos, Z.A., Gowda, S.M., Baker, R.S., Heffernan, N.T., The sum is greater than the parts: Ensembling models of student knowledge in educational software (2012) SIGKDD Explor. Newsl., 13 (2), pp. 37-44Plant, E.A., Ericsson, K.A., Hill, L., Asberg, K., Why study time does not predict grade point average across college students: Implications of deliberate practice for academic performance (2005) Contemporary Educational Psychology, 30 (1), pp. 96-116Punj, G., Stewart, D.W., Cluster analysis in marketing research: Review and suggestions for application (1983) Journal of Marketing Research, 20 (2), pp. 134-148Rau, W., Durand, A., The academic ethic and college grades: Does hard work help students to "make the grade"? (2000) Sociology of Education, 73 (1), pp. 19-38Salamonson, Y., Andrew, S., Academic performance in nursing students: Influence of part-time employment, age and ethnicity (2006) Journal of Advanced Nursing, 55 (3), pp. 342-349Siirtola, H., Raiha, K.J., Surakka, V., Interactive curriculum visualization (2013) Information Visualisation (IV), 2013 17 th International Conference, pp. 108-117. , y IEEETeam, R.C., (2013) R: A Language and Environment for Statistical ComputingYe, Q., Zhang, Z., Law, R., Sentiment classification of online reviews to travel destinations by supervised machine learning approaches (2009) Expert Systems with Applications, 36 (3), pp. 6527-6535http://purl.org/coar/resource_type/c_6501ORIGINALDOI10_4067S0718-07642019000100247.pdfapplication/pdf511019https://repositorio.utb.edu.co/bitstream/20.500.12585/8754/1/DOI10_4067S0718-07642019000100247.pdff5e979ed0229ef9bf9605354b55c5cdbMD51TEXTDOI10_4067S0718-07642019000100247.pdf.txtDOI10_4067S0718-07642019000100247.pdf.txtExtracted texttext/plain34759https://repositorio.utb.edu.co/bitstream/20.500.12585/8754/4/DOI10_4067S0718-07642019000100247.pdf.txt245e389bb4956ea7a8e766c8d192a6a3MD54THUMBNAILDOI10_4067S0718-07642019000100247.pdf.jpgDOI10_4067S0718-07642019000100247.pdf.jpgGenerated Thumbnailimage/jpeg86987https://repositorio.utb.edu.co/bitstream/20.500.12585/8754/5/DOI10_4067S0718-07642019000100247.pdf.jpg82cafb3cd677cc54289e13d5be8fbeeeMD5520.500.12585/8754oai:repositorio.utb.edu.co:20.500.12585/87542023-05-26 09:36:57.193Repositorio Institucional UTBrepositorioutb@utb.edu.co |