Differential evolution clustering and data mining for determining learning routes in moodle
Data mining techniques are being widely used in the field of education from the arise of e-learning platforms like Moodle, WebCT, Claroline, and others, and the virtual learning system they entail. Information systems store all activities in files or databases which, correctly processed, may offer r...
- Autores:
-
Viloria, Amelec
Crissien Borrero, Tito
Vargas Villa, Jesús
Torres, Maritza
García Guiliany, Jesús
Vargas Mercado, Carlos
Orellano Llinas, Nataly
Batista Zea, Karina
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10748
- Acceso en línea:
- https://hdl.handle.net/11323/10748
https://repositorio.cuc.edu.co/
- Palabra clave:
- Clustering
Data mining
Differential evolution
K-means
Moodle
- Rights
- closedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Differential evolution clustering and data mining for determining learning routes in moodle |
| title |
Differential evolution clustering and data mining for determining learning routes in moodle |
| spellingShingle |
Differential evolution clustering and data mining for determining learning routes in moodle Clustering Data mining Differential evolution K-means Moodle |
| title_short |
Differential evolution clustering and data mining for determining learning routes in moodle |
| title_full |
Differential evolution clustering and data mining for determining learning routes in moodle |
| title_fullStr |
Differential evolution clustering and data mining for determining learning routes in moodle |
| title_full_unstemmed |
Differential evolution clustering and data mining for determining learning routes in moodle |
| title_sort |
Differential evolution clustering and data mining for determining learning routes in moodle |
| dc.creator.fl_str_mv |
Viloria, Amelec Crissien Borrero, Tito Vargas Villa, Jesús Torres, Maritza García Guiliany, Jesús Vargas Mercado, Carlos Orellano Llinas, Nataly Batista Zea, Karina |
| dc.contributor.author.none.fl_str_mv |
Viloria, Amelec Crissien Borrero, Tito Vargas Villa, Jesús Torres, Maritza García Guiliany, Jesús Vargas Mercado, Carlos Orellano Llinas, Nataly Batista Zea, Karina |
| dc.subject.proposal.eng.fl_str_mv |
Clustering Data mining Differential evolution K-means Moodle |
| topic |
Clustering Data mining Differential evolution K-means Moodle |
| description |
Data mining techniques are being widely used in the field of education from the arise of e-learning platforms like Moodle, WebCT, Claroline, and others, and the virtual learning system they entail. Information systems store all activities in files or databases which, correctly processed, may offer relevant data to the teacher. This paper reports the use of data mining techniques and Differential Evolution Clustering for discovering learning routes frequently applied in the Moodle Platform. Data were obtained form 4.115 university students monitored in an online course using Moodle 3.1. Firstly, students were grouped according to the data from a final qualifications report in a course. Secondly, the data of the Moodle logs about each cluster/group of students was used separately with the aim of obtaining more specific and precise models of the students behavior in the processes. © 2019, Springer Nature Singapore Pte Ltd. |
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2019 |
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2019-07-30 |
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2024-02-20T19:40:18Z |
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2024-02-20T19:40:18Z |
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Viloria, A. et al. (2019). RETRACTED CHAPTER: Differential Evolution Clustering and Data Mining for Determining Learning Routes in Moodle. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_18 |
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Corporación Universidad de la Costa |
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Viloria, A. et al. (2019). RETRACTED CHAPTER: Differential Evolution Clustering and Data Mining for Determining Learning Routes in Moodle. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_18 1865-0929 10.1007/978-981-32-9563-6_18 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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Communications in Computer and Information Science |
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1. Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching - learning process through knowledge data discovery (big data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 670–679. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_63 2. Ballesteros Román, A.: Minería de Datos Educativa Aplicada a la Investigación de Patrones de Aprendizaje en Estudiante en Ciencias. Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, México City (2012) 3. Ben Salem, S., Naouali, S., Chtourou, Z.: A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput. Electr. Eng. 68, 463–483 (2018). https://doi.org/10.1016/j.compeleceng.2018.04.023 4. Chakraborty, S., Das, S.: Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148–156 (2018). https://doi.org/10.1016/j.spl.2018.01.015 5. Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M.: Inice: a new approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.034 6. Rahman, M.A., Islam, M.Z., Bossomaier, T.: ModEx and seed-detective: two novel techniques for high quality clustering by using good initial seeds in K-Means. J. King Saud Univ. - Comput. Inf. Sci. 27, 113–128 (2015). https://doi.org/10.1016/j.jksuci.2014.04.002 7. Rahman, M.A., Islam, M.Z.: A hybrid clustering technique combining a novel genetic algorithm with K-means. Knowl.-Based Syst. 71, 345–365 (2014). https://doi.org/10.1016/j.knosys.2014.08.011 8. Ramadas, M., Abraham, A., Kumar, S.: FSDE-forced strategy differential evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci (2016). https://doi.org/10.1016/j.jksuci.2016.12.005 9. Yaqian, Z., Chai, Q.H., Boon, G.W.: Curvature-based method for determining the number of clusters. Inf. Sci. (2017). https://doi.org/10.1016/j.ins.2017.05.024 10. Tîrnăucă, C., Gómez-Pérez, D., Balcázar, J.L., Montaña, J.L.: Global optimality in k-means clustering. Inf. Sci. (Ny) 439–440, 79–94 (2018). https://doi.org/10.1016/j.ins.2018.02.001 11. Xiang, W., Zhu, N., Ma, S., Meng, X., An, M.: A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing (2015). https://doi.org/10.1016/j.neucom.2015.01.058 12. Garcia, A.J., Flores, W.G.: Automatic clustering using nature-inspired metaheuristics: a survey. Appl. Soft Comput (2016). https://doi.org/10.1016/j.asoc.2015.12.001 13. Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans 38, 218–237(2008). https://doi.org/10.1109/TSMCA.2007.909595 14. Costa, C., Alvelos, H., Teixeira, L.: The use of MOODLE e-learning platform: a study in a Portuguese University. Procedia Technology 5, 334–343 (2012) 15. El-Bahsh, R., Daoud, M.: Evaluating the use of MOODLE to achieve effective and interactive learning: a case study at the German Jordanian University. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications, pp. 1–5 (2016) 16. Coll, S.D., Treagust, D.: Blended learning environment: an approach to enhance student’s learning experiences outside school (LEOS). MIER J. Educ. Stud. Trends Pract. 7, 2 (2018) 17. Kuo, R., Suryani, E., Yasid, A.: Automatic clustering combining differential evolution algorithm and k-means algorithm. In: Lin, Y.K., Tsao, Y.C., Lin, S.W. (eds.) Proceedings of the Institute of Industrial Engineers Asian Conference 2013, pp. 1207–1215. Springer, Singapore (2013). https://doi.org/10.1007/978-981-4451-98-7_143 18. Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24 (2017). https://doi.org/10.1016/j.swevo.2016.05.003 19. Kaya, I.: A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny) 179, 1552–1566 (2009). https://doi.org/10.1016/j.ins.2008.09.024 20. Dobbie, G., Sing, Y., Riddle, P., Ur, S.: Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol. Comput. 17, 1–13 (2014). https://doi.org/10.1016/j.swevo.2014.02.001 21. Departamento Administrativo Nacional de Estadística.: Página principal. Recuperado de: DANE (2018). http://www.dane.gov.co/ 22. Torres-Samuel, M., Vásquez, C.L., Viloria, A., Varela, N., Hernández-Fernandez, L., Portillo-Medina, R.: Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 188–199. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_18 23. Vásquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017) 24. Torres-Samuel, M., et al.: Efficiency analysis of the visibility of Latin American Universities and their impact on the ranking web. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 235–243. Springer, Cham (2018). https://doi.org/10.1007/978-3-319- 93803-5_18 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© Copyright 2019 Elsevier B.V., All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbViloria, AmelecCrissien Borrero, TitoVargas Villa, JesúsTorres, MaritzaGarcía Guiliany, JesúsVargas Mercado, CarlosOrellano Llinas, NatalyBatista Zea, Karina2024-02-20T19:40:18Z2024-02-20T19:40:18Z2019-07-30Viloria, A. et al. (2019). RETRACTED CHAPTER: Differential Evolution Clustering and Data Mining for Determining Learning Routes in Moodle. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2019. Communications in Computer and Information Science, vol 1071. Springer, Singapore. https://doi.org/10.1007/978-981-32-9563-6_181865-0929https://hdl.handle.net/11323/1074810.1007/978-981-32-9563-6_18Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Data mining techniques are being widely used in the field of education from the arise of e-learning platforms like Moodle, WebCT, Claroline, and others, and the virtual learning system they entail. Information systems store all activities in files or databases which, correctly processed, may offer relevant data to the teacher. This paper reports the use of data mining techniques and Differential Evolution Clustering for discovering learning routes frequently applied in the Moodle Platform. Data were obtained form 4.115 university students monitored in an online course using Moodle 3.1. Firstly, students were grouped according to the data from a final qualifications report in a course. Secondly, the data of the Moodle logs about each cluster/group of students was used separately with the aim of obtaining more specific and precise models of the students behavior in the processes. © 2019, Springer Nature Singapore Pte Ltd.9 páginasapplication/pdfengSpringer Science and Business Media Deutschland GmbHGermanyhttps://www2.scopus.com/record/display.uri?eid=2-s2.0-85070022699&doi=10.1007%2f978-981-32-9563-6_18&origin=inward&txGid=3cff69a9be8ae1c9d9a42861721a164cDifferential evolution clustering and data mining for determining learning routes in moodleArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Communications in Computer and Information Science1. Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the design of a student pattern recognition tool to facilitate the teaching - learning process through knowledge data discovery (big data). In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 670–679. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_632. Ballesteros Román, A.: Minería de Datos Educativa Aplicada a la Investigación de Patrones de Aprendizaje en Estudiante en Ciencias. Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, México City (2012)3. Ben Salem, S., Naouali, S., Chtourou, Z.: A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput. Electr. Eng. 68, 463–483 (2018). https://doi.org/10.1016/j.compeleceng.2018.04.0234. Chakraborty, S., Das, S.: Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148–156 (2018). https://doi.org/10.1016/j.spl.2018.01.0155. Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M.: Inice: a new approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.0346. Rahman, M.A., Islam, M.Z., Bossomaier, T.: ModEx and seed-detective: two novel techniques for high quality clustering by using good initial seeds in K-Means. J. King Saud Univ. - Comput. Inf. Sci. 27, 113–128 (2015). https://doi.org/10.1016/j.jksuci.2014.04.0027. Rahman, M.A., Islam, M.Z.: A hybrid clustering technique combining a novel genetic algorithm with K-means. Knowl.-Based Syst. 71, 345–365 (2014). https://doi.org/10.1016/j.knosys.2014.08.0118. Ramadas, M., Abraham, A., Kumar, S.: FSDE-forced strategy differential evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci (2016). https://doi.org/10.1016/j.jksuci.2016.12.0059. Yaqian, Z., Chai, Q.H., Boon, G.W.: Curvature-based method for determining the number of clusters. Inf. Sci. (2017). https://doi.org/10.1016/j.ins.2017.05.02410. Tîrnăucă, C., Gómez-Pérez, D., Balcázar, J.L., Montaña, J.L.: Global optimality in k-means clustering. Inf. Sci. (Ny) 439–440, 79–94 (2018). https://doi.org/10.1016/j.ins.2018.02.00111. Xiang, W., Zhu, N., Ma, S., Meng, X., An, M.: A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing (2015). https://doi.org/10.1016/j.neucom.2015.01.05812. Garcia, A.J., Flores, W.G.: Automatic clustering using nature-inspired metaheuristics: a survey. Appl. Soft Comput (2016). https://doi.org/10.1016/j.asoc.2015.12.00113. Das, S., Abraham, A., Konar, A.: Automatic clustering using an improved differential evolution algorithm. IEEE Trans. Syst. Man, Cybern. - Part A Syst. Humans 38, 218–237(2008). https://doi.org/10.1109/TSMCA.2007.90959514. Costa, C., Alvelos, H., Teixeira, L.: The use of MOODLE e-learning platform: a study in a Portuguese University. Procedia Technology 5, 334–343 (2012)15. El-Bahsh, R., Daoud, M.: Evaluating the use of MOODLE to achieve effective and interactive learning: a case study at the German Jordanian University. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications, pp. 1–5 (2016)16. Coll, S.D., Treagust, D.: Blended learning environment: an approach to enhance student’s learning experiences outside school (LEOS). MIER J. Educ. Stud. Trends Pract. 7, 2 (2018)17. Kuo, R., Suryani, E., Yasid, A.: Automatic clustering combining differential evolution algorithm and k-means algorithm. In: Lin, Y.K., Tsao, Y.C., Lin, S.W. (eds.) Proceedings of the Institute of Industrial Engineers Asian Conference 2013, pp. 1207–1215. Springer, Singapore (2013). https://doi.org/10.1007/978-981-4451-98-7_14318. Piotrowski, A.P.: Review of differential evolution population size. Swarm Evol. Comput. 32, 1–24 (2017). https://doi.org/10.1016/j.swevo.2016.05.00319. Kaya, I.: A genetic algorithm approach to determine the sample size for attribute control charts. Inf. Sci. (Ny) 179, 1552–1566 (2009). https://doi.org/10.1016/j.ins.2008.09.02420. Dobbie, G., Sing, Y., Riddle, P., Ur, S.: Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evol. Comput. 17, 1–13 (2014). https://doi.org/10.1016/j.swevo.2014.02.00121. Departamento Administrativo Nacional de Estadística.: Página principal. Recuperado de: DANE (2018). http://www.dane.gov.co/22. Torres-Samuel, M., Vásquez, C.L., Viloria, A., Varela, N., Hernández-Fernandez, L., Portillo-Medina, R.: Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 188–199. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_1823. Vásquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017)24. Torres-Samuel, M., et al.: Efficiency analysis of the visibility of Latin American Universities and their impact on the ranking web. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943, pp. 235–243. Springer, Cham (2018). https://doi.org/10.1007/978-3-319- 93803-5_181781701071ClusteringData miningDifferential evolutionK-meansMoodlePublicationORIGINALDifferential evolution clustering and data mining for determining learning routes in Moodle.pdfDifferential evolution clustering and data mining for determining learning routes in Moodle.pdfArtículoapplication/pdf42716https://repositorio.cuc.edu.co/bitstreams/90dc75d1-e353-4930-b1de-dee00bbcfe63/download90ee345a4ec37dc18ef8bb1e5e95fc0eMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/697428e7-9337-4946-90cd-3ef772edf5b5/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTDifferential evolution clustering and data mining for determining learning routes in Moodle.pdf.txtDifferential evolution clustering and data mining for determining learning routes in Moodle.pdf.txtExtracted 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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