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Use of data mining to identify trends between variables to improve implementation of an immersive environment
dc.contributor.author | Zamora Musa, Ronald | spa |
dc.contributor.author | Velez, Jeimy | spa |
dc.date.accessioned | 2018-11-24T20:32:50Z | |
dc.date.available | 2018-11-24T20:32:50Z | |
dc.date.issued | 2017 | |
dc.identifier.issn | 1816949X | spa |
dc.identifier.uri | http://hdl.handle.net/11323/1810 | spa |
dc.description.abstract | Globally, the implementation of immersive environments for leaming activities have been in constant growth whch indcates that their development must improve daily. For this reason, this study identifies trends (co-occurrences) and relatiomhps between variables associated with an immersive environment to improve its implementation. Results were found which show that a good design of information guides, organization of menus and useful instructiom generates that the users enjoy using the immersive environment for leaming and foments recommendations of use to other users. | spa |
dc.language.iso | eng | |
dc.publisher | Journal Of Engineering And Applied Sciences | spa |
dc.rights | Atribución – No comercial – Compartir igual | spa |
dc.subject | Association rules mining | eng |
dc.subject | Colombia | eng |
dc.subject | Data mining educational data mining | eng |
dc.subject | Immersive environment e-learning | eng |
dc.title | Use of data mining to identify trends between variables to improve implementation of an immersive environment | eng |
dc.type | Artículo de revista | spa |
dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
dc.identifier.instname | Corporación Universidad de la Costa | spa |
dc.identifier.reponame | REDICUC - Repositorio CUC | spa |
dc.identifier.repourl | https://repositorio.cuc.edu.co/ | spa |
dc.relation.references | Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data mining: A prediction for student's performance using classification method. World Journal of Computer Application and Technology, 2(2), 43-47 Angeli, C., Howard, S., Ma, J., Yang, J., & Kirschner, P. (2017). Data mining in educational technology classroom research: Can it make a contribution?. Computers & Education, 113, 226-242. Arantes, E., Stadler, A., Del Corso, J., & Catapan, A. (2016). Contribuições da educação profissional na modalidade a distância para a gestão e valorização da diversidade. Espacios, 37(22), E-1. Baker, R. S., & Siemens, G. (2014). Educational data mining and learning analytics. In K. Sawyer (Ed.), Cambridge handbook of the learning sciences (2nd ed., pp. 253e274). NY: Cambridge University Press Buja, A., & Lee, Y. S. (2001, August). Data mining criteria for tree-based regression and classification. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 27e36). ACM Chen, L. & Yang, Q. (2014). A group division method based on collaborative learning elements. In The 26th Chinese Control and Decision Conference (pp. 1701-1705). Changsha. Cho, Y. H., Yim, S. Y., & Paik, S. (2015). Physical and social presence in 3D virtual role-play for preservice teachers. The Internet and Higher Education, 25, 70–77 Comas-Gonzalez, Z., Echeverri-Ocampo, I., ZamoraMusa, R., Velez, J., Sarmiento, R. and Orellana, M. (2017). Tendencias recientes de la Educación Virtual y su fuerte conexión con los Entornos Inmersivos. Espacios, 38(15), p.4. Freire, P., Dandolini, G., De Souza, J., Trierweiller, A., Da Silva, S., & Sell, D. et al. (2016). Universidade Corporativa em Rede: Considerações Iniciais para um Novo Modelo de Educação Corporativa. Espacios, 37(5), E-5. Gunasekara, R., Wijegunasekara, M., & Dias, N. (2014). A Study on How to Improve the Perfomance of k-mean Data Mining Algorithm in a Parallel Environment. Journal Of Engineering And Applied Sciences, 9(10), 441 - 446. Kovács, P., Murray, N., Rozinaj, G., Sulema, Y., & Rybárová, R. (2015). Application of immersive technologies for education: State of the art. In 2015 International Conference on Interactive Mobile Communication Technologies and Learning (IMCL) (pp. 283 - 288). Thessaloniki. Kumar Ameta, G., & Pathak, V. (2016). A Survey on Improved Association Rule Mining for market based analysis. International Journal Of Advances In Computer Science And Technology, 5(12), 173-175. Lin, W., Alvarez, S. A., & Ruiz, C. (2002). Efficient adaptive-support association rule mining for recommender systems. Data Mining and Knowledge Discovery, 6(1), 83-105. Lovkesh. (2016). Enhancing E-Learning Through Data Mining in the Context of Education Data. International Conference On Computing For Sustainable Global Development (Indiacom) - IEEE, 109 - 113. Marengo, A., Pagano, A., & Barbone, A. (2013). Data mining methods to assess student behavior in adaptive e-learning processes. Fourth International Conference On E-Learning "Best Practices In Management, Design And Development Of ECourses: Standards Of Excellence And Creativity" - IEEE, 303 - 309. Maqsood, A. (2017). Study of Big Data: An Industrial Revolution Review of applications and challenges. International Journal Of Advanced Trends In Computer Science And Engineering, 6(3), 31-34. Medvedev, V., Kurasova, O., Bernatavičienė, J., Treigys, P., Marcinkevičius, V., & Dzemyda, G. (2017). A new web-based solution for modelling data mining processes. Simulation Modelling Practice And Theory, 76, 34-46. Mendoza, F., De la Hoz, A., De la Hoz, E., & Ariza, P. (2015). Feature selection, learning metrics and dimension reduction in training and classification processes in intrusion detection systems. Journal Of Theoretical And Applied Information Technology, 82(2), 291 - 298. Merceron, A., & Yacef, K. (2010). Measuring correlation of strong symmetric assocation rules in educational data. In C. Romero, S. Ventura, M. Pechenizkiy, & R. S. J. D. Baker (Eds.), Handbook of educational data mining (pp. 245e255). Boca Raton: Taylor & Francis Group Mohajer, A., Somarin, A., Yaghoobzadeh, M., & Gudakahriz, S. (2016). A Method Based on Data Mining for Detection of Intrusion in Distributed Databases. Journal Of Engineering And Applied Sciences, 11(7), 1493 - 1501. Mustami, M., Suryadin and Suardi Wekke, I. (2017). Learning Model Combined with Mind Maps and Cooperative Strategies for Junior High School Student. Journal of Engineering and Applied Sciences, 12(7), pp.1681 - 1686. Paez, H., Zabala, V. and Zamora, R. (2017). Análisis y actualización del programa de la asignatura Automatización Industrial en la formación profesional de ingenieros electrónicos. Educación en Ingeniería, 11(21), pp.39 - 44. Peng, J., Tan, W., & Liu, G. (2015). Virtual Experiment in Distance Education: Based on 3D Virtual Learning Environment. In 2015 International Conference of Educational Innovation through Technology (EITT) (pp. 81-84). Wuhan. Pollock, C. & Biles, J. (2016). Discovering the Lived Experience of Students Learning in Immersive Simulation. Clinical Simulation in Nursing, 12(8), 313-319. Poorani, M., Nithya, P., & Umamaheshwari, B. (2014). A Method for Mining Infrequent Causal Associations with Swarm Intelligence Optimization for Finding Adverse Drug Reaction. International Journal Of Computing, Communications And Networking, 3(1), 25-32. Romero, C., & Ventura, S. (2012). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining And Knowledge Discovery, 3(1), 12-27. Tawil, N., Zaharim, A., Shaari, I., Ismail, N. and Embi, M. (2012). The Acceptance of E-Learning in Engineering Mathematics in Enhancing Engineering Education. Journal of Engineering and Applied Sciences, 7(3), pp.279-284. Udupi, P., Sharma, N., & Jha, S. (2016). Educational Data Mining and Big Data Framework for eLearning Environment. 5Th International Conference On Reliability, Infocom Technologies And Optimization (ICRITO) (Trends And Future Directions) - IEEE, 258 - 261. Zamora-Musa, R. and Villa, J. (2013). Estudio de la alternativa de ambientes virtuales colaborativos como herramienta de apoyo a laboratorios teleoperados en ingeniería. WEEF – World Engineering Education Forum. Zamora, R., Velez, J. and Villa, J. (2016). Contributions of Collaborative and Immersive Environments in Development a Remote Access Laboratory: From Point of View of Effectiveness in Learning. In: F. Mendes Neto, R. de Souza and A. Sandro Gomes, ed., Handbook of Research on 3-D Virtual Environments and Hypermedia for Ubiquitous Learning, 1st ed. Pennsylvania: IGIGlobal, pp.1-28. Zamora-Musa, R., Velez, J., Paez-Logreira, H., Coba, J., Cano-Cano, C. and Martinez, O. (2017). Implementación de un recurso educativo abierto a través del modelo del diseño universal para el aprendizaje teniendo en cuenta evaluación de competencias y las necesidades individuales de los estudiantes. Espacios, 38(5), p.3. | spa |
dc.type.coar | http://purl.org/coar/resource_type/c_6501 | spa |
dc.type.content | Text | spa |
dc.type.driver | info:eu-repo/semantics/article | spa |
dc.type.redcol | http://purl.org/redcol/resource_type/ART | spa |
dc.type.version | info:eu-repo/semantics/acceptedVersion | spa |
dc.type.coarversion | http://purl.org/coar/version/c_ab4af688f83e57aa | spa |
dc.rights.coar | http://purl.org/coar/access_right/c_abf2 | spa |
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