Predicting student drop-out rates using data mining techniques: A case study
The prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of Systems Engineering (SE) undergraduate students after 6 years of enrollment in a...
- Autores:
-
PEREZ GUTIERREZ, BORIS RAINIERO
Castellanos, Camilo
Correal, Dario
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2018
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/1650
- Acceso en línea:
- http://repositorio.ufps.edu.co/handle/ufps/1650
https://doi.org/10.1007/978-3-030-03023-0_10
- Palabra clave:
- Student drop out
Student desertion prediction
Educational data mining
Prediction models
- Rights
- openAccess
- License
- © Springer Nature Switzerland AG 2018
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Predicting student drop-out rates using data mining techniques: A case study |
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Predicting student drop-out rates using data mining techniques: A case study |
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Predicting student drop-out rates using data mining techniques: A case study Student drop out Student desertion prediction Educational data mining Prediction models |
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Predicting student drop-out rates using data mining techniques: A case study |
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Predicting student drop-out rates using data mining techniques: A case study |
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Predicting student drop-out rates using data mining techniques: A case study |
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Predicting student drop-out rates using data mining techniques: A case study |
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Predicting student drop-out rates using data mining techniques: A case study |
| dc.creator.fl_str_mv |
PEREZ GUTIERREZ, BORIS RAINIERO Castellanos, Camilo Correal, Dario |
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PEREZ GUTIERREZ, BORIS RAINIERO Castellanos, Camilo Correal, Dario |
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Student drop out Student desertion prediction Educational data mining Prediction models |
| topic |
Student drop out Student desertion prediction Educational data mining Prediction models |
| description |
The prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of Systems Engineering (SE) undergraduate students after 6 years of enrollment in a Colombian university. Original data is extended and enriched using a feature engineering process. Our experimental results showed that simple algorithms achieve reliable levels of accuracy to identify predictors of dropout. Decision Trees, Logistic Regression, Naive Bayes and Random Forest results were compared in order to propose the best option. Also, Watson Analytics is evaluated to establish the usability of the service for a non expert user. Main results are presented in order to decrease the dropout rate by identifying potential causes. In addition, we present some findings related to data quality to improve the students data collection process. |
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2018 |
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2021-12-02T14:56:38Z |
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2021-12-02T14:56:38Z |
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eng |
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eng |
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IEEE Colombian Conference on Applications in Computational Intelligence |
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Vol.833 (2018) |
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125 |
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(2018) |
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Pérez, B., Castellanos, C., & Correal, D. (2018, May). Predicting student drop-out rates using data mining techniques: A case study. In IEEE Colombian Conference on Applications in Computational Intelligence (pp. 111-125). Springer, Cham. |
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IEEE Colombian Conference on Applications in Computational Intelligence |
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© Springer Nature Switzerland AG 2018 |
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Atribución 4.0 Internacional (CC BY 4.0) |
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15 páginas |
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IEEE Colombian Conference on Applications in Computational Intelligence |
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Colombia |
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PEREZ GUTIERREZ, BORIS RAINIERO99eb843d4117f9d72803d9b802e06cca600Castellanos, Camilo7d9116c790f89e2959445a4878fbbd6c600Correal, Dariof2cc757ff67031b83e7adf85c47d6a546002021-12-02T14:56:38Z2021-12-02T14:56:38Z2018-12-14http://repositorio.ufps.edu.co/handle/ufps/1650https://doi.org/10.1007/978-3-030-03023-0_10The prevention of students dropping out is considered very important in many educational institutions. In this paper we describe the results of an educational data analytics case study focused on detection of dropout of Systems Engineering (SE) undergraduate students after 6 years of enrollment in a Colombian university. Original data is extended and enriched using a feature engineering process. Our experimental results showed that simple algorithms achieve reliable levels of accuracy to identify predictors of dropout. Decision Trees, Logistic Regression, Naive Bayes and Random Forest results were compared in order to propose the best option. Also, Watson Analytics is evaluated to establish the usability of the service for a non expert user. Main results are presented in order to decrease the dropout rate by identifying potential causes. In addition, we present some findings related to data quality to improve the students data collection process.15 páginasapplication/pdfengIEEE Colombian Conference on Applications in Computational IntelligenceColombiaIEEE Colombian Conference on Applications in Computational IntelligenceVol.833 (2018)125(2018)111833Pérez, B., Castellanos, C., & Correal, D. (2018, May). Predicting student drop-out rates using data mining techniques: A case study. In IEEE Colombian Conference on Applications in Computational Intelligence (pp. 111-125). Springer, Cham.IEEE Colombian Conference on Applications in Computational Intelligence© Springer Nature Switzerland AG 2018info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://link.springer.com/chapter/10.1007/978-3-030-03023-0_10Predicting student drop-out rates using data mining techniques: A case studyArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://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_970fb48d4fbd8a85Student drop outStudent desertion predictionEducational data miningPrediction modelsAl-Radaideh, Q.A., Al-Shawakfa, E.M., Al-Najjar, M.I.: Mining student data using decision trees. In: International Arab Conference on Information Technology (ACIT 2006), Yarmouk University, Jordan (2006)Aulck, L., Velagapudi, N., Blumenstock, J., West, J.: Predicting Student Dropout in Higher Education. arXiv preprint arXiv:1606.06364, June 2016Baradwaj, B.K., Pal, S.: Mining educational data to analyze students’ performance. arXiv preprint arXiv:1201.3417 (2012)Bhardwaj, B.K., Pal, S.: Data mining: a prediction for performance improvement using classification. arXiv preprint arXiv:1201.3418 (2012)Brunner, J.J., et al.: Higher Education in Regional and City Development Antioquia, Colombia (2016)Brunsden, V., Davies, M., Shevlin, M., Bracken, M.: Why do he students dropout? A test of Tinto’s model. J. Furth. High. Educ. 24(3), 301–310 (2000). https://doi.org/10.1080/030987700750022244Chapman, P., et al.: CRISP-DM 1.0. CRISP-DM Consortium 76, 3 (2000)Dekker, G.W., Pechenizkiy, M., Vleeshouwers, J.M.: Predicting students drop out: a case study. In: International Working Group on Educational Data Mining (2009). http://www.educationaldatamining.org/EDM2009/uploads/proceedings/dekker.pdfDevasia, T., Vinushree T P, Hegde, V.: Prediction of students performance using educational data mining. In: 2016 International Conference on Data Mining and Advanced Computing (SAPIENCE), pp. 91–95. IEEE, March 2016. https://doi.org/10.1109/SAPIENCE.2016.7684167, http://ieeexplore.ieee.org/document/7684167/Durso, S.D.O., Cunha, J.V.A.D.: Determinant factors for undergraduate student’s dropout in an accounting studies department of a Brazilian public university. Educação em Revista 34 (2018)de Educacion, M.: Spadies - sistema de prevencion y analisis a la desercion en las instituciones de educacion superior. www.mineducacion.gov.co/1621/article-156292.html. Accessed 18 July 2017Jing, L.: Data mining and its applications in higher education. New Dir. Inst. Res. 2002(113), 17–36 (2002). https://doi.org/10.1002/ir.35, https://onlinelibrary.wiley.com/doi/abs/10.1002/ir.35Kim, D., Kim, S.: Sustainable education: analyzing the determinants of university student dropout by nonlinear panel data models. Sustainability 10(4), 954 (2018)Kovacic, Z.: Early prediction of student success: mining students’ enrolment data. In: Proceedings of Informing Science & IT Education Conference (InSITE) (2010)Márquez-Vera, C., Cano, A., Romero, C., Noaman, A.Y.M., Mousa Fardoun, H., Ventura, S.: Early dropout prediction using data mining: a case study with high school students. Expert Syst. 33(1), 107–124 (2016). https://doi.org/10.1111/exsy.12135, http://doi.wiley.com/10.1111/exsy.12135Mishra, T., Kumar, D., Gupta, S.: Mining students’ data for prediction performance. In: 2014 Fourth International Conference on Advanced Computing & Communication Technologies (ACCT), pp. 255–262. IEEE (2014)Romero, C., Ventura, S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl. 33(1), 135 – 146 (2007). https://doi.org/10.1016/j.eswa.2006.04.005, http://www.sciencedirect.com/science/article/pii/S0957417406001266Seidman, A.: Retention revisited: R= E, Id+ E & In, Iv. Coll. Univ. 71(4), 18–20 (1996)Herzog, S.: Estimating student retention and degree completion time: decisiontrees and neural networks vis-á-vis regression. New Dir. Inst. Res. 2006(131), 17–33 (2006). https://doi.org/10.1002/ir.185, https://onlinelibrary.wiley.com/doi/abs/10.1002/ir.185Tekin, A.: Early prediction of students’ grade point averages at graduation: a data mining approach. Eurasian J. Educ. Res. 54, 207–226 (2014). https://eric.ed.gov/?id=EJ1057301Tinto, V.: Dropout from higher education: a theoretical synthesis of recent research. Rev. Educ. Res. 45(1), 89–125 (1975)Wirth, R.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, pp. 29–39 (2000)Yukselturk, E., Ozekes, S., Türel, Y.K.: Predicting dropout student: an application of data mining methods in an online education program. Eur. J. Open Distance E-Learn. 17(1), 118–133 (2014)ORIGINALPredicting student drop-out rates using data mining techniques. A case study.pdfPredicting student drop-out rates using data mining techniques. A case study.pdfapplication/pdf2517128https://repositorio.ufps.edu.co/bitstream/ufps/1650/1/Predicting%20student%20drop-out%20rates%20using%20data%20mining%20techniques.%20A%20case%20study.pdf6f174d7a5d57532fbc705b5a74d273dbMD51open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.ufps.edu.co/bitstream/ufps/1650/2/license.txt2f9959eaf5b71fae44bbf9ec84150c7aMD52open accessTEXTPredicting student drop-out rates using data mining techniques. A case study.pdf.txtPredicting student drop-out rates using data mining techniques. A case study.pdf.txtExtracted texttext/plain30567https://repositorio.ufps.edu.co/bitstream/ufps/1650/3/Predicting%20student%20drop-out%20rates%20using%20data%20mining%20techniques.%20A%20case%20study.pdf.txt84c95a7c80fe612c96944bee801c5e39MD53open accessTHUMBNAILPredicting student drop-out rates using data mining techniques. A case study.pdf.jpgPredicting student drop-out rates using data mining techniques. 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 incorporada 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.
0000-0001-9249-175699eb843d4117f9d72803d9b802e06cca600 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