Implementation of a predictive information system for university dropout prevention
The dropout of university students is one of the topics of a broad interest in higher education institutions and government education departments. Recent changes in education methods, socio-economic conditions, and the growing imitations of face to face interactions make it necessary to have tools t...
- Autores:
-
Guzmán Castillo, Stefania
Körner, Franziska
Pantoja García, Julia I.
Nieto Ramos, Lainet
Gómez Charris, Yulineth
Castro Sarmiento, Alex
Romero Conrado, Alfonso R.
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2021
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/13985
- Acceso en línea:
- https://hdl.handle.net/11323/13985
https://repositorio.cuc.edu.co/
- Palabra clave:
- Dropout
Prevention
Education
University
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Implementation of a predictive information system for university dropout prevention |
| title |
Implementation of a predictive information system for university dropout prevention |
| spellingShingle |
Implementation of a predictive information system for university dropout prevention Dropout Prevention Education University |
| title_short |
Implementation of a predictive information system for university dropout prevention |
| title_full |
Implementation of a predictive information system for university dropout prevention |
| title_fullStr |
Implementation of a predictive information system for university dropout prevention |
| title_full_unstemmed |
Implementation of a predictive information system for university dropout prevention |
| title_sort |
Implementation of a predictive information system for university dropout prevention |
| dc.creator.fl_str_mv |
Guzmán Castillo, Stefania Körner, Franziska Pantoja García, Julia I. Nieto Ramos, Lainet Gómez Charris, Yulineth Castro Sarmiento, Alex Romero Conrado, Alfonso R. |
| dc.contributor.author.none.fl_str_mv |
Guzmán Castillo, Stefania Körner, Franziska Pantoja García, Julia I. Nieto Ramos, Lainet Gómez Charris, Yulineth Castro Sarmiento, Alex Romero Conrado, Alfonso R. |
| dc.subject.proposal.eng.fl_str_mv |
Dropout Prevention Education University |
| topic |
Dropout Prevention Education University |
| description |
The dropout of university students is one of the topics of a broad interest in higher education institutions and government education departments. Recent changes in education methods, socio-economic conditions, and the growing imitations of face to face interactions make it necessary to have tools that allow us to consider a broad set of factors related to the dropout phenomenon. The objective of this article is to show the implementation results of a predictive information system (IS) for the prevention of university dropout in a higher education institution. The system allows the calculation of the risk of dropout per student and uses an alert generation procedure to coordinate interventions. The platform allowed measuring the impact of the intervention strategies on the permanence of the students. Likewise, it made possible the reorganization of the intervention process towards the students, prioritizing according to the risk level. |
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2021-11 |
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2025-02-25T22:50:11Z |
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2025-02-25T22:50:11Z |
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Stefania Guzmán-Castillo, Franziska Körner, Julia I. Pantoja-García, Lainet Nieto-Ramos, Yulineth Gómez-Charris, Alex Castro-Sarmiento, Alfonso R. Romero-Conrado, Implementation of a Predictive Information System for University Dropout Prevention,Procedia Computer Science,Volume 198, 2022, Pages 566-571, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2021.12.287 |
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1877-0509 |
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https://hdl.handle.net/11323/13985 |
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10.1016/j.procs.2021.12.287 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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Stefania Guzmán-Castillo, Franziska Körner, Julia I. Pantoja-García, Lainet Nieto-Ramos, Yulineth Gómez-Charris, Alex Castro-Sarmiento, Alfonso R. Romero-Conrado, Implementation of a Predictive Information System for University Dropout Prevention,Procedia Computer Science,Volume 198, 2022, Pages 566-571, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2021.12.287 1877-0509 10.1016/j.procs.2021.12.287 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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eng |
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eng |
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Procedia Computer Science |
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Spady WG. Dropouts from higher education: Toward an empirical model. Interchange 1971;2:38–62. https://doi.org/10.1007/BF02282469 Freitas FADS, Vasconcelos FFX, Peixoto SA, Hassan MM, Ali Akber Dewan M, de Albuquerque VHC, et al. IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electron 2020;9:1–14. https://doi.org/10.3390/electronics9101613 Narayanasamy SK, Elçi A. An effective prediction model for online course dropout rate. Int J Distance Educ Technol 2020;18:94–110. https://doi.org/10.4018/IJDET.2020100106 Patacsil FF. Survival analysis approach for early prediction of student dropout using enrollment student data and ensemble models. Univers J Educ Res 2020;8:4036–47. https://doi.org/10.13189/ujer.2020.080929 Gopalakrishnan A, Kased R, Yang H, Love MB, Graterol C, Shada A. A multifaceted data mining approach to understanding what factors lead college students to persist and graduate. Proc. Comput. Conf. 2017, vol. 2018- Janua, 2018, p. 372–81. https://doi.org/10.1109/SAI.2017.8252128 Rolandus Hagedoorn T, Spanakis G. Massive open online courses temporal profiling for dropout prediction. Proc. - Int. Conf. Tools with Artif. Intell. ICTAI, vol. 2017- Novem, 2018, p. 231–8. https://doi.org/10.1109/ICTAI.2017.00045 Rastrollo-Guerrero JL, Gómez-Pulido JA, Durán-Domínguez A. Analyzing and predicting students’ performance by means of machine learning: A review. Appl Sci 2020;10. https://doi.org/10.3390/app10031042 Hassan H, Anuar S, Ahmad NB. Students’ performance prediction model using meta-classifier approach. vol. 1000. 2019. https://doi.org/10.1007/978-3-030-20257-6_19 Figueroa-Canas J, Sancho-Vinuesa T. Early prediction of dropout and final exam performance in an online statistics course. Rev Iberoam Tecnol Del Aprendiz 2020;15:86–94. https://doi.org/10.1109/RITA.2020.2987727 Aldowah H, Al-Samarraie H, Alzahrani AI, Alalwan N. Factors affecting student dropout in MOOCs: a cause and effect decision‐ making model. J Comput High Educ 2020;32:429–54. https://doi.org/10.1007/s12528-019-09241-y Skalka J, Drlik M. Automated assessment and microlearning units as predictors of at-risk students and students’ outcomes in the introductory programming courses. Appl Sci 2020;10. https://doi.org/10.3390/app10134566 Viloria A, Padilla JG, Vargas-Mercado C, Hernández-Palma H, Llinas NO, David MA. Integration of data technology for analyzing university dropout. Procedia Comput. Sci., vol. 155, 2019, p. 569–74. https://doi.org/10.1016/j.procs.2019.08.079 Punlumjeak W, Rugtanom S, Jantarat S, Rachburee N. Improving classification of imbalanced student dataset using ensemble method of voting, bagging, and adaboost with under-sampling technique. Lect. Notes Electr. Eng., vol. 449, 2017, p. 27–34. https://doi.org/10.1007/978-981-10-6451-7_4 Zeng W, Chin S-C, Zeimet B, Kuang R, Chi C-L. Dropout prediction in home care training. Proc. 10th Int. Conf. Educ. Data Mining, EDM 2017, 2017, p. 442–3 Feki-Sahnoun W, Njah H, Hamza A, Barraj N, Mahfoudi M, Rebai A, et al. Using general linear model, Bayesian Networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms. Ecol Inform 2018;43:12–23. https://doi.org/10.1016/j.ecoinf.2017.10.017 Toivonen T, Jormanainen I. Evolution of decision tree classifiers in open ended educational data mining. ACM Int. Conf. Proceeding Ser., New York, NY, USA: ACM; 2019, p. 290–6. https://doi.org/10.1145/3362789.3362880 Wang C, Sun J. LogitBoost algorithm considering the cost of misclassification and its application in the classification of mobile user value. Xitong Gongcheng Lilun yu Shijian/System Eng Theory Pract 2019;39:2702–12. https://doi.org/10.12011/1000-6788-2018- 0194-11 Baswardono W, Kurniadi D, Mulyani A, Arifin DM. Comparative analysis of decision tree algorithms: Random forest and C4.5 for airlines customer satisfaction classification. J Phys Conf Ser 2019;1402:066055. https://doi.org/10.1088/1742-6596/1402/6/066055 Shin Y. Application of Stochastic Gradient Boosting Approach to Early Prediction of Safety Accidents at Construction Site. Adv Civ Eng 2019;2019:1–9. https://doi.org/10.1155/2019/1574297 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2021 Elsevier B.Vhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Guzmán Castillo, StefaniaKörner, FranziskaPantoja García, Julia I.Nieto Ramos, LainetGómez Charris, YulinethCastro Sarmiento, AlexRomero Conrado, Alfonso R.2025-02-25T22:50:11Z2025-02-25T22:50:11Z2021-11Stefania Guzmán-Castillo, Franziska Körner, Julia I. Pantoja-García, Lainet Nieto-Ramos, Yulineth Gómez-Charris, Alex Castro-Sarmiento, Alfonso R. Romero-Conrado, Implementation of a Predictive Information System for University Dropout Prevention,Procedia Computer Science,Volume 198, 2022, Pages 566-571, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2021.12.2871877-0509https://hdl.handle.net/11323/1398510.1016/j.procs.2021.12.287Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The dropout of university students is one of the topics of a broad interest in higher education institutions and government education departments. Recent changes in education methods, socio-economic conditions, and the growing imitations of face to face interactions make it necessary to have tools that allow us to consider a broad set of factors related to the dropout phenomenon. The objective of this article is to show the implementation results of a predictive information system (IS) for the prevention of university dropout in a higher education institution. The system allows the calculation of the risk of dropout per student and uses an alert generation procedure to coordinate interventions. The platform allowed measuring the impact of the intervention strategies on the permanence of the students. Likewise, it made possible the reorganization of the intervention process towards the students, prioritizing according to the risk level.6 páginasapplication/pdfengElsevier B.V.Netherlandshttps://www.sciencedirect.com/science/article/pii/S1877050921025266?via%3DihubImplementation of a predictive information system for university dropout preventionArtículo 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_970fb48d4fbd8a85Procedia Computer ScienceSpady WG. Dropouts from higher education: Toward an empirical model. Interchange 1971;2:38–62. https://doi.org/10.1007/BF02282469Freitas FADS, Vasconcelos FFX, Peixoto SA, Hassan MM, Ali Akber Dewan M, de Albuquerque VHC, et al. IoT system for school dropout prediction using machine learning techniques based on socioeconomic data. Electron 2020;9:1–14. https://doi.org/10.3390/electronics9101613Narayanasamy SK, Elçi A. An effective prediction model for online course dropout rate. Int J Distance Educ Technol 2020;18:94–110. https://doi.org/10.4018/IJDET.2020100106Patacsil FF. Survival analysis approach for early prediction of student dropout using enrollment student data and ensemble models. Univers J Educ Res 2020;8:4036–47. https://doi.org/10.13189/ujer.2020.080929Gopalakrishnan A, Kased R, Yang H, Love MB, Graterol C, Shada A. A multifaceted data mining approach to understanding what factors lead college students to persist and graduate. Proc. Comput. Conf. 2017, vol. 2018- Janua, 2018, p. 372–81. https://doi.org/10.1109/SAI.2017.8252128Rolandus Hagedoorn T, Spanakis G. Massive open online courses temporal profiling for dropout prediction. Proc. - Int. Conf. Tools with Artif. Intell. ICTAI, vol. 2017- Novem, 2018, p. 231–8. https://doi.org/10.1109/ICTAI.2017.00045Rastrollo-Guerrero JL, Gómez-Pulido JA, Durán-Domínguez A. Analyzing and predicting students’ performance by means of machine learning: A review. Appl Sci 2020;10. https://doi.org/10.3390/app10031042Hassan H, Anuar S, Ahmad NB. Students’ performance prediction model using meta-classifier approach. vol. 1000. 2019. https://doi.org/10.1007/978-3-030-20257-6_19Figueroa-Canas J, Sancho-Vinuesa T. Early prediction of dropout and final exam performance in an online statistics course. Rev Iberoam Tecnol Del Aprendiz 2020;15:86–94. https://doi.org/10.1109/RITA.2020.2987727Aldowah H, Al-Samarraie H, Alzahrani AI, Alalwan N. Factors affecting student dropout in MOOCs: a cause and effect decision‐ making model. J Comput High Educ 2020;32:429–54. https://doi.org/10.1007/s12528-019-09241-ySkalka J, Drlik M. Automated assessment and microlearning units as predictors of at-risk students and students’ outcomes in the introductory programming courses. Appl Sci 2020;10. https://doi.org/10.3390/app10134566Viloria A, Padilla JG, Vargas-Mercado C, Hernández-Palma H, Llinas NO, David MA. Integration of data technology for analyzing university dropout. Procedia Comput. Sci., vol. 155, 2019, p. 569–74. https://doi.org/10.1016/j.procs.2019.08.079Punlumjeak W, Rugtanom S, Jantarat S, Rachburee N. Improving classification of imbalanced student dataset using ensemble method of voting, bagging, and adaboost with under-sampling technique. Lect. Notes Electr. Eng., vol. 449, 2017, p. 27–34. https://doi.org/10.1007/978-981-10-6451-7_4Zeng W, Chin S-C, Zeimet B, Kuang R, Chi C-L. Dropout prediction in home care training. Proc. 10th Int. Conf. Educ. Data Mining, EDM 2017, 2017, p. 442–3Feki-Sahnoun W, Njah H, Hamza A, Barraj N, Mahfoudi M, Rebai A, et al. Using general linear model, Bayesian Networks and Naive Bayes classifier for prediction of Karenia selliformis occurrences and blooms. Ecol Inform 2018;43:12–23. https://doi.org/10.1016/j.ecoinf.2017.10.017Toivonen T, Jormanainen I. Evolution of decision tree classifiers in open ended educational data mining. ACM Int. Conf. Proceeding Ser., New York, NY, USA: ACM; 2019, p. 290–6. https://doi.org/10.1145/3362789.3362880Wang C, Sun J. LogitBoost algorithm considering the cost of misclassification and its application in the classification of mobile user value. Xitong Gongcheng Lilun yu Shijian/System Eng Theory Pract 2019;39:2702–12. https://doi.org/10.12011/1000-6788-2018- 0194-11Baswardono W, Kurniadi D, Mulyani A, Arifin DM. Comparative analysis of decision tree algorithms: Random forest and C4.5 for airlines customer satisfaction classification. J Phys Conf Ser 2019;1402:066055. https://doi.org/10.1088/1742-6596/1402/6/066055Shin Y. Application of Stochastic Gradient Boosting Approach to Early Prediction of Safety Accidents at Construction Site. 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>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.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>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).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>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).</li>
      <li>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.</li>
      <li>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.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>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.</li>
          <li>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.</li>
        </ol>
      </li>
      <li>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.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>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.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>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.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>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.</li>
      <li>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.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>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.</li>
      <li>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.</li>
      <li>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.</li>
      <li>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.</li>
    </ol>
  </li>
  <br/>
</ol>
 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