Item request has been placed! ×
Item request cannot be made. ×
loading  Processing Request

Modelo de dominio específico para análisis y minería de datos educativos ; Specific domain model for educational data mining and analysis

Item request has been placed! ×
Item request cannot be made. ×
loading   Processing Request
  • معلومة اضافية
    • Contributors:
      Duque Méndez, Néstor Darío; Gaia Grupo de Ambientes Inteligentes Adaptativos; Hernández Leal, Emilcy Juliana 0000-0002-5865-9604; Hernández Leal, Emilcy Juliana 0001402728; Hernández Leal, Emilcy Juliana Emilcy Juliana Hernández-Leal
    • بيانات النشر:
      Universidad Nacional de Colombia
      Manizales - Ingeniería y Arquitectura - Doctorado en Ingeniería - Industria y Organizaciones
      Facultad de Ingeniería y Arquitectura
      Manizales, Colombia
      Universidad Nacional de Colombia - Sede Manizales
    • الموضوع:
      2024
    • نبذة مختصرة :
      graficas, tablas ; El uso de técnicas de análisis de datos para el apoyo de procesos educativos, al igual que en otros dominios de datos, busca potencializar la toma de decisiones y la planeación de estos. Las tecnologías de información y comunicación contribuyen a dichos procesos de análisis. En particular, desde la minería de datos se tiene una opción para dar atención a las necesidades presentes en cuanto a gestión de datos académicos, datos que se producen desde el proceso de enseñanza-aprendizaje como tal, así como también desde procesos de carácter administrativo que están asociados. Dependiendo del nivel educativo, para el caso de Colombia estos niveles se distribuyen en educación pre-escolar, básica, media y superior, los sistemas de información donde son almacenados los datos educativos varían, influyendo también el carácter de la institución (pública o privada). Para el caso de la educación superior, estos sistemas de información o fuentes de datos suelen estar bastante estructurados, facilitando el acceso a los datos y por tanto la extracción de información y conocimiento. No obstante, a nivel de educación básica y media, las fuentes de datos resultan más difíciles de acceder y el tratamiento que requieren los datos antes de ser analizados puede ser considerable. En este sentido, esta tesis doctoral propone un modelo conceptual con enfoque de dominio específico para minería de datos educativos, que ofrece mecanismos de solución a los problemas particulares de cada etapa del proceso de minería de datos educativos y en general de los modelos de dominio genérico, además, de atender la problemática asociada a los datos que provienen de múltiples fuentes y escalas para una aplicación puntual con datos de educación básica y media en Colombia, acotado también a técnicas de aprendizaje supervisado. De la mano del modelo conceptual, se presenta una estrategia de validación y aplicación de este. El modelo propuesto puede ser aplicado a diferentes contextos educativos y para diferentes fuentes de datos, contando ...
    • File Description:
      187 páginas; application/pdf
    • Relation:
      Acevedo-Díaz, J. A., García-Carmona, A., Aragón-Méndez, M. del M., & Oliva-Martínez, J. M. (2017). Modelos científicos: significado y papel en la práctica científica- Scientific models: meaning and role in scientific practice. Revista Científica, 3(30), 155. https://doi.org/10.14483/23448350.12288; Agrawal, A. (2003). Metamodel based model transformation language to facilitate Domain Specific Model Driven Architecture. Proceedings of the Conference on Object-Oriented Programming Systems, Languages, and Applications, OOPSLA, 118–119. https://doi.org/10.1145/949344.949379; Agt-Rickauer, H., Kutsche, R. D., & Sack, H. (2019). Automated recommendation of related model elements for domain models. Communications in Computer and Information Science, 991, 134–158. https://doi.org/10.1007/978-3-030-11030-7_7/COVER; Arslan, S., & Kardas, G. (2020). DSML4DT: A domain-specific modeling language for device tree software. Computers in Industry, 115, 103179. https://doi.org/10.1016/J.COMPIND.2019.103179; Ayala Franco, E., López Martínez, R. E., & Menéndez Domínguez, V. H. (2021). Modelos predictivos de riesgo académico en carreras de computación con minería de datos educativos. Revista de Educación a Distancia (RED), 21(66), 1–36. https://doi.org/10.6018/RED.463561; Baker, R. S., & Inventado, P. S. (2014). Educational Data Mining and Learning Analytics. In Learning Analytics (pp. 61–75). Springer New York. https://doi.org/10.1007/978-1-4614-3305-7_4; Bhardwaj, B. K., & Pal, S. (2012). Data Mining: A prediction for performance improvement using classification. International Journal of Computer Science and Information Security. http://arxiv.org/abs/1201.3418; Begum, S. H. (2013). Data Mining Tools and Trends – An Overview. International Journal of Emerging Research in Management &Technology, 6–12; Bellifemine, F., Fortino, G., Giannantonio, R., Gravina, R., Guerrieri, A., & Sgroi, M. (2011). SPINE: a domain-specific framework for rapid prototyping of WBSN applications. Software: Practice and Experience, 41(3), 237–265. https://doi.org/10.1002/spe.998; Bermanis, A., Averbuh, A., & Coifman, R. (2013). Multiscale data sampling and function extension. Applied and Computational Harmonic Analysis, 34(1), 15–29. https://doi.org/10.1016/J.ACHA.2012.03.002; Bettini, L. (2016). Implementing domain-specific languages with Xtext and Xtend : learn how to implement a DSL with Xtext and Xtend using easy-to-understand examples and best practices (2nd ed.). Packt Publishing.; Brownlee, J. (2020). Data preparation for machine learning: data cleaning, feature selection, and data transforms in Python. Machine Learning Mastery.; Candia Oviedo, D. I. (2019). Predicción del rendimiento académico de los estudiantes de la UNSAAC a partir de sus datos de ingreso utilizando algoritmos de aprendizaje automático.; Cao, L. (2010). Domain-Driven Data Mining: Challenges and Prospects. IEEE Transactions on Knowledge and Data Engineering, 22(6), 755–769. https://doi.org/10.1109/TKDE.2010.32; Cao, L., & Zhang, C. (2007). The Evolution of KDD: Towards Domain-Driven Data Mining. International Journal of Pattern Recognition and Artificial Intelligence, 21(04), 677–692. https://doi.org/10.1142/S0218001407005612; Castellanos, C., Varela, C. A., & Correal, D. (2021). ACCORDANT: A domain specific-model and DevOps approach for big data analytics architectures. Journal of Systems and Software, 172, 110869. https://doi.org/10.1016/J.JSS.2020.110869; Castro, M., & Lizasoain, L. (2012). Las técnicas de modelización estadística en la investigación educativa: minería de datos, modelos de ecuaciones estructurales y modelos jerárquicos lineales. Revista Española de Pedagogía, 70(251), 131–148.; Cechinel, C., Ochoa, X., Lemos dos Santos, H., Carvalho Nunes, J. B., Rodés, V., & Marques Queiroga, E. (2020). Mapping Learning Analytics initiatives in Latin America. British Journal of Educational Technology, 1–23. https://doi.org/10.1111/bjet.12941; Chan, J. Y. Le, Bea, K. T., Leow, S. M. H., Phoong, S. W., & Cheng, W. K. (2023). State of the art: a review of sentiment analysis based on sequential transfer learning. Artificial Intelligence Review, 56(1), 749–780. https://doi.org/10.1007/S10462-022-10183-8/TABLES/7; Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. (1999). The CRISP-DM User Guide.; Che, D., Safran, M., & Peng, Z. (2013). From Big Data to Big Data Mining: Challenges, Issues, and Opportunities. In B. Hong, X. Meng, L. Chen, W. Winiwarter, & W. Song (Eds.), Database Systems for Advanced Applications: 18th International Conference, DASFAA 2013, International Workshops: BDMA, SNSM, SeCoP, Wuhan, China, April 22-25, 2013. Proceedings (pp. 1–15). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-40270-8_1; Chiok, M., & Higinio, C. (2017). Predicción del rendimiento académico aplicando técnicas de minería de datos - Dialnet. Anales Científicos, 78(1), 26–33.; Chen, G., Iwen, M., Chin, S., & Maggioni, M. (2012). A fast multiscale framework for data in high-dimensions: Measure estimation, anomaly detection, and compressive measurements. Visual Communications and Image Processing, 1–6. https://doi.org/10.1109/VCIP.2012.6410789; Cios, K. J., Pedrycz, W., Swiniarski, R., & Kurgan, L. (2007). Data mining : a knowledge discovery approach. Springer.; Clark, T., Van Den Brand, M., Combemale, B., & Rumpe, B. (2015). Conceptual model of the globalization for domain-specific languages. In Globalizing Domain-Specific Languages (Vol. 9400, pp. 7–20). Springer Verlag. https://doi.org/10.1007/978-3-319-26172-0_2; Csikszentmihalyi, M., & Wolfe, R. (2014). New Conceptions and Research Approaches to Creativity: Implications of a Systems Perspective for Creativity in Education. In The Systems Model of Creativity (pp. 161–184). Springer Netherlands. https://doi.org/10.1007/978-94-017-9085-7_10; Contreras, L. E., Fuentes, H. J., & Rodríguez, J. I. (2020). Predicción del rendimiento académico como indicador de éxito/fracaso de los estudiantes de ingeniería, mediante aprendizaje automático. Formación Universitaria, 13(5), 233–246. https://doi.org/10.4067/S0718-50062020000500233; ANE. (2018). Boletín Técnico Educación Formal (EDUC) 2018.; De Almeida Neto, F. A., & Castro, A. (2017). A reference architecture for educational data mining. Proceedings – Frontiers in Education Conference, FIE, 2017-October, 1–8. https://doi.org/10.1109/FIE.2017.8190728; De Lara, J., & Guerra, E. (2012). Domain-specific textual meta-modelling languages for model driven engineering. ECMFA’12: Proceedings of the 8th European Conference on Modelling Foundations and Applications, 7349 LNCS, 259–274. https://doi.org/10.1007/978-3-642-31491-9_20; Desolda, G., Ardito, C., & Matera, M. (2017). Empowering end users to customize their smart environments: Model, composition paradigms, and domain-specific tools. ACM Transactions on Computer-Human Interaction, 24(2), 1–52. https://doi.org/10.1145/3057859; Díaz-Landa, B., Meleán-Romero, R., & Marín-Rodriguez, W. (2021). Rendimiento académico de estudiantes en Educación Superior: predicciones de factores influyentes a partir de árboles de decisión. Revista de Estudios Interdisciplinarios En Ciencias Sociales, 23(3), 616–639.; Dsilva, C. J., Talmon, R., Gear, C. W., Coifman, R. R., & Kevrekidis, I. G. (2015). Data-Driven Reduction for Multiscale Stochastic Dynamical Systems. Applied Dynamical Systems. http://arxiv.org/abs/1501.05195; Edwards, G., & Medvidovic, N. (2008). A methodology and framework for creating domain-specific development infrastructures. ASE 2008 – 23rd IEEE/ACM International Conference on Automated Software Engineering, Proceedings, 168–177. https://doi.org/10.1109/ASE.2008.27; El Ghosh, M., Naja, H., Abdulrab, H., & Khalil, M. (2017). Ontology learning process as a bottom-up strategy for building domain-specific ontology from legal texts. ICAART 2017 – Proceedings of the 9th International Conference on Agents and Artificial Intelligence, 2, 473–480. https://doi.org/10.5220/0006188004730480; Evans, E. (2004). Domain-driven design: tackling complexity in the heart of software. Addison-Wesley Professional. http://books.google.com/books?hl=en&lr=&id=7dlaMs0SECsC&pgis=1; Fayyad, U. M., Piatetsky-Shapiro, G., & Smyth, P. (1996). Advances in knowledge discovery and data mining. In Advances in knowledge discovery and data mining. AAAI Press. https://dl.acm.org/citation.cfm?id=257942; Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From Data Mining to Knowledge Discovery in Databases. AI Magazine, 17, 37–54. https://doi.org/10.1609/aimag.v17i3.1230; Fernandez, D. B., & Lujan-Mora, S. (2017). Comparison of applications for educational data mining in Engineering Education. 2017 IEEE World Engineering Education Conference (EDUNINE), 81–85. https://doi.org/10.1109/EDUNINE.2017.7918187; Gerbig, R. (2017). Deep, seamless, multi-format, multi-notation definition and use of domain-specific languages. Verlag Dr. Hut.; Hübscher, R., Puntambekar, S., & Nye, A. H. (2007). Domain Specific Interactive Data Mining. Workshop on Data Mining for User Modeling at the 11th International Conference on User Modeling, 81–90. http://www.educationaldatamining.org/UM2007/Hubscher.pdf; Hand, D. J. (David J. ), Mannila, Heikki., & Smyth, Padhraic. (2001). Principles of data mining. MIT Press.; He, X., Zhao, K., & Chu, X. (2021). AutoML: A survey of the state-of-the-art. Knowledge-Based Systems, 212, 106622.; Inmon, W. H., & Linstedt, D. (2014). Data architecture: a primer for the data scientist: big data, data warehouse and data vault. Morgan Kaufmann.; Iung, A., Carbonell, J., Marchezan, L., Rodrigues, E., Bernardino, M., Basso, F. P., & Medeiros, B. (2020). Systematic mapping study on domain-specific language development tools. Empirical Software Engineering, 25(5), 4205–4249. https://doi.org/10.1007/S10664-020-09872-1/TABLES/9; Jaramillo Valvuena, S., & Londoño, J. M. (2014). Sistemas para almacenar grandes volúmenes de datos. Revista Gerencia Tecnológica Informática, 13(37), 17–28. http://revistas.uis.edu.co/index.php/revistagti/article/view/4689; Jiménez Celorrio, S., & de la Rosa Turbides, T. (2009). Learning-Based Planning. In Encyclopedia of Artificial Intelligence (pp. 1024–1028). IGI Global. https://doi.org/10.4018/978-1-59904-849-9.ch151; Jindal, R., & Borah, M. D. (2013). A Survey on Educational Data Mining and Research Trends. International Journal of Database Management Systems, 5(3), 53–73. https://doi.org/10.5121/ijdms.2013.5304; Kantardzic, M. (2011). Data mining : concepts, models, methods, and algorithms (Segunda). John Wiley & Sons, Inc.; Karagiannis, D., Mayr, H. C., & Mylopoulos, J. (2016). Domain-specific conceptual modeling: Concepts, methods and tools. In Domain-Specific Conceptual Modeling: Concepts, Methods and Tools. Springer International Publishing. https://doi.org/10.1007/978-3-319-39417-6; Kautz, H., & Selman, B. (1998). The Role of Domain-Specific Knowledge in the Planning as Satisfiability Framework. In American Association for Artificial Intelligence (pp. 181–189). http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1259; Karmaker, S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., & Veeramachaneni, K. (2021). Automl to date and beyond: Challenges and opportunities. ACM Computing Surveys (CSUR), 54(8), 1-36.; Krahn, H., Rumpe, B., & Völkel, S. (2014, September 22). Roles in Software Development using Domain Specific Modeling Languages. 6th OOPSLA Workshop on Domain-Specific Modeling (DSM’ 06). http://arxiv.org/abs/1409.6618; Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data : an introduction to data mining (Second). John Wiley & Sons, Inc.; Leédeczi, Á., Bakay, Á., MarÓti, M., Völgyesi, P., Nordstrom, G., Sprinkle, J., & Karsai, G. (2001). Composing domain-specific design environments. Computer, 34(11), 44–51. https://doi.org/10.1109/2.963443; Lin, Y., Gray, J., & Jouault, F. (2017). DSMDiff: a differentiation tool for domain-specific models. European Journal of Information Systems, 16(4), 349–361. https://doi.org/10.1057/PALGRAVE.EJIS.3000685; Liu, Y., Li, J., Ming, Z., Song, H., Weng, X., & Wang, J. (2019). Domain-specific data mining for residents’ transit pattern retrieval from incomplete information. Journal of Network and Computer Applications, 134, 62–71. https://doi.org/10.1016/J.JNCA.2019.02.016; López, C. P. (2007). Minería de datos: técnicas y herramientas. In Paraninfo. Paraninfo. https://books.google.com.pe/books?id=wz-D_8uPFCEC&printsec=frontcover&source=gbs_ge_summary_r&cad=0#v=onepage&q&f=false; Lu, W., Zhou, Y., Yu, J., & Jia, C. (2019). Concept Extraction and Prerequisite Relation Learning from Educational Data. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 9678–9685. https://doi.org/10.1609/AAAI.V33I01.33019678; Maldonado, C. E. (2017). Tipología de modelos científicos de explicación. Ciencia y complejidad. Sociología y Tecnociencia, 7(2), 58–72. https://doi.org/https://doi.org/10.24197/st.2.2017.58-72; Matignon, R. (2007). Data mining using SAS enterprise miner. https://books.google.com/books?hl=es&lr=&id=h7FAN3lijRMC&oi=fnd&pg=PP1&dq=semma+data+mining&ots=C9RYcqzLYC&sig=vk2668sC7hkUa2Y97SpkPybfLfc; Ministerio de Educación. (2019). Marco Estratégico 2019 – 2022. https://www.mineducacion.gov.co/1759/articles-382974_recurso_3.pdf; Ministerio de Educación. (2020ª). Funciones y deberes – Ministerio de Educación Nacional de Colombia. https://www.mineducacion.gov.co/portal/Ministerio/ oodle ción-Institucional/85252:Funciones-y-deberes; Ministerio de Educación. (2020b). Normatividad y documentos – Ministerio de Educación Nacional de Colombia. https://www.mineducacion.gov.co/1759/w3-article-357542.html?_noredirect=1; Ministerio de Educación. (2020c). Sistemas de Información. https://www.mineducacion.gov.co/portal/micrositios-institucionales/Sistemas-de-Informacion/; Ministerio de Educación Nacional. (2020). Sistema educativo colombiano . https://www.mineducacion.gov.co/portal/Preescolar-basica-y-media/Sistema-de-educacion-basica-y-media/233839:Sistema-educativo-colombiano; Mitrofanova, Y. S., Sherstobitova, A. A., & Filippova, O. A. (2019). Modeling smart learning processes based on educational data mining tools. In V. Uskov, R. Howlett, & L. Jain (Eds.), Smart Education and e-Learning 2019. Smart Innovation, Systems and Technologies (Vol. 144, pp. 561–571). Springer. https://doi.org/10.1007/978-981-13-8260-4_49; Nguyen, B.-A., & Yang, D.-L. (2012). A Semi-Automatic Approach to Construct Vietnamese Ontology from Online Text. INTERNATIONAL REVIEW OF RESEARCH IN OPEN AND DISTANCE LEARNING, 13(5, SI), 148–172.; Niu, S., Liu, Y., Wang, J., & Song, H. (2020). A Decade Survey of Transfer Learning (2010–2020). IEEE Transactions on Artificial Intelligence, 1(2); Nordstrom, G., Sztipanovits, J., Karsai, G., & Ledeczi, A. (1999). Metamodeling-rapid design and evolution of domain-specific modeling environments. Proceedings – ECBS 1999, IEEE Conference and Workshop on Engineering of Computer-Based Systems, 68–74. https://doi.org/10.1109/ECBS.1999.755863; Oliveira, C., & Da Silva, J. C. (2009). Mineração de Dados: Conceitos, Tarefas, Métodos e Ferramentas. www.inf.ufg.br; Orozco Iguasnia, W. A., Villao Balón, A. J., Orozco Iguasnia, J., & Villarroel Sánchez, M. V. (2021). Aplicación de técnicas de minería de datos para predecir el desempeño académico de los estudiantes de la escuela ‘Lic. Angélica Villón L.’ Revista Científica y Tecnológica UPSE, 8(2), 68–75. https://doi.org/10.26423/RCTU.V8I2.637; Parekh, V., Gwo, J., & Finin, T. (2004). Mining Domain Specific Texts and Glossaries to Evaluate and Enrich Domain Ontologies. International Conference of Information and Knowledge Engineering. http://aisl.umbc.edu/resources/94.pdf; Patwari, P., Chaudhuri, S. R., Banerjee, A., Natarajan, S., & Pandey, S. (2016, November 22). A complementary domain specific design environment aiding SysML. ISSE 2016 – 2016 International Symposium on Systems Engineering – Proceedings Papers. https://doi.org/10.1109/SysEng.2016.7753164; Peinado, H. S. (2013). Legislación educativa colombiana. Editorial Magisterio. https://www.magisterio.com.co/libro/legislacion-educativa-colombiana; Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications, 41(4), 1432–1462.; Pérez Marqués, M. (2014). Minería de Datos a través de ejemplos (1aed.). RC Libros.; Probert, D. R., & Ridgman, T. W. (2013). Structuring technology and innovation management executive education: the research contribution. ISPIM Conference Proceedings. www.ispim.org.; Recker, M., & Lee, J. E. (2016). Analyzing Learner and Instructor Interactions within Learning Management Systems: Approaches and Examples. In Learning, Design, and Technology (pp. 1–23). Springer International Publishing. https://doi.org/10.1007/978-3-319-17727-4_7-1; Riquelme, J. C., Ruiz, R., & Gilbert, K. (2006). Minería de Datos: Conceptos y Tendencias. Revista Iberoamericana de Inteligencia Artificial, 29, 11–18. http://www.aepia.org; Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications, 33(1), 135–146. https://doi.org/10.1016/J.ESWA.2006.04.005; Romero, C. & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. https://doi.org/10.1002/widm.1075; Romero, C. & Ventura, S. (2020). Educational data mining and learning analytics: An updated survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 10(3), e1355. https://doi.org/10.1002/widm.1355; Romero, C. & Ventura, S. (2010). Educational Data Mining: A Review of the State of the Art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601–618. https://doi.org/10.1109/TSMCC.2010.2053532; Salgado Reyes, N., Beltrán Morales, J., Guaña Moya, J., Escobar Teran, C., Nicolalde Rodriguez, D., & Chafla Altamirano, G. (2019). Modelo para predecir el rendimiento académico basado en redes neuronales y analítica de aprendizaje. RISTI, 17, 258–266.; Scheuer, O., & McLaren, B. M. (2012). Educational Data Mining. In Encyclopedia of the Sciences of Learning (pp. 1075–1079). Springer US. https://doi.org/10.1007/978-1-4419-1428-6_618; Selvaraj, P., Burugari, V. K., Sumathi, D., Nayak, R. K., & Tripathy, R. (2019). Ontology based Recommendation System for Domain Specific Seekers. Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), 341–345. https://doi.org/10.1109/i-smac47947.2019.9032634; Shafique, U., & Qaiser, H. (2014). A Comparative Study of Data Mining Process Models (KDD, CRISP-DM and SEMMA). International Journal of Innovation and Scientific Research, 12(1), 217–222. https://www.researchgate.net/publication/268770881_A_Comparative_Study_of_Data_Mining_Process_Models_KDD_CRISP-DM_and_SEMMA; Slater, S., Joksimović, S., Kovanovic, V., Baker, R. S., & Gasevic, D. (2016). Tools for Educational Data Mining: A Review. Educational and Behavioral Statistics, 42(1), 85–106.; Timarán Pereira, R., Hidalgo Troya, A., & Caicedo Zambrano, J. (2020). Factores asociados al desempeño académico en Lectura Crítica en las pruebas Saber 11° con árboles de decisión - Dialnet. Investigación e Innovación En Ingenierías, 8(3), 29–37.; Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2020). Transfer Learning from Deep Neural Networks for Predicting Student Performance. Applied Sciences, 10(6), 2145. https://doi.org/10.3390/APP10062145; Vaisman, A., & Zimányi, E. (2014a). Data Warehouse Concepts. In Data Warehouse Systems (pp. 53–87). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-54655-6_3; Vaisman, A., & Zimányi, E. (2014b). Extraction, Transformation, and Loading. In Data Warehouse Systems (pp. 285–327). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-54655-6_8; Varde, A. S., & Tatti, N. (2014). A Panorama of Imminent Doctoral Research in Data Mining. ACM SIGMOD Record, 43(3), 71–74. https://doi.org/10.1145/2694428.2694442; Winch, C., Oancea, A., & Orchard, J. (2015). The contribution of educational research to teachers’ professional learning: philosophical understandings. Oxford Review of Education, 41(2), 202–216.; Winner, E., & Veloso, M. (2003). DISTILL: Towards Learning Domain-Specific Planners by Example. Itwentieth Nternational Conference on Machine Learning. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.3010; Xiao, W., Ji, P., & Hu, J. (2022). A survey on educational data mining methods used for predicting students’ performance. Engineering Reports, 4(5), e12482. https://doi.org/10.1002/ENG2.12482; Xu, Y., Rajpathak, D., Gibbs, I., & Klabjan, D. (2019). Automatic Ontology Learning from Domain-Specific Short Unstructured Text Data. Computer Science. http://arxiv.org/abs/1903.04360; Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020). Transfer Learining (1st ed.). Cambridge University Press.; Yukselturk, E., Ozekes, S., & Türel, Y. K. (2014). Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program. European Journal of Open, Distance and E-Learning, 17(1), 118–133. https://doi.org/10.2478/EURODL-2014-0008; Zeineddine, H., Braendle, U., & Farah, A. (2021). Enhancing prediction of student success: Automated machine learning approach. Computers & Electrical Engineering, 89, 106903.; Zhu, Y., Zhu, H., Liu, Q., Chen, E., Li, H., & Zhao, H. (2016). Exploring the procrastination of college students: A data-driven behavioral perspective. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9642). https://doi.org/10.1007/978-3-319-32025-0_17; https://repositorio.unal.edu.co/handle/unal/85851; Universidad Nacional de Colombia; Repositorio Institucional Universidad Nacional de Colombia; https://repositorio.unal.edu.co/
    • الدخول الالكتروني :
      https://repositorio.unal.edu.co/handle/unal/85851
      https://repositorio.unal.edu.co/
    • Rights:
      Atribución-NoComercial 4.0 Internacional ; http://creativecommons.org/licenses/by-nc/4.0/ ; info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.81B43504