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Factores que determinan la aceptación de las clases virtuales en los estudiantes de la facultad de ingeniería de la Universidad Nacional de Colombia

dc.rights.licenseAtribución-NoComercial-SinDerivadas 4.0 Internacional
dc.contributor.advisorPeña Reyes, José Ismael
dc.contributor.authorGordo González, Karen Madelaine
dc.date.accessioned2022-06-06T19:14:30Z
dc.date.available2022-06-06T19:14:30Z
dc.date.issued2022
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/81513
dc.descriptionilustraciones, gráficas, tablas
dc.description.abstractLa importancia que se le ha dado a las tecnologías de la información y las comunicaciones (TIC) en el sector educativo ha venido creciendo considerablemente. Cada vez más se pueden apreciar diferentes mecanismos tecnológicos enfocados a dar mejor soporte a los procesos de enseñanza y aprendizaje. Las clases virtuales hacen parte de dichos mecanismos tecnológicos y debido a la crisis de salud que surgió en el año 2020 por el virus SARS-COVD-2 y las estrategias para prevenir el contagio como el confinamiento obligatorio, la mayoría de las instituciones educativas optaron por esta modalidad con el fin de que los estudiantes siguieran recibiendo clases apropiada y oportunamente. Teniendo en cuenta esta situación, este trabajo se enfocó en identificar, por medio de la teoría UTAUT2, los factores que determinan la aceptación tecnológica de las clases virtuales para los estudiantes de ingeniería de la Universidad Nacional de Colombia. Para lograrlo, se usó una encuesta en línea que respondieron un total de 174 estudiantes, y los datos fueron analizados bajo el procedimiento de PLS-SEM, donde se observó que el factor determinante de aceptación de las clases virtuales es la motivación hedónica. (Texto tomado de la fuente).
dc.description.abstractInformation and Communication Technologies (ICTs) has grown exponentially in the past years in educational institutions. Different types of technology have focused on giving better support to the teaching and learning process. Virtual online classes are a type of these technologies and has been adopted by higher educational institutions around the world because the COVID-19 pandemic. Most institutions had to adjust its teaching processes inclass learning to online, in order to mitigate the spread of the virus and continue with the academic year. This study aims at identifying the determining factors of virtual classes acceptance for Engineering Faculty Students of Universidad Nacional de Colombia, using the unified theory of acceptance and use of technology extended (UTAUT2). Using an online survey, a total of 174 students enrolled in different programs of the engineering Faculty responded. The data was analyzed using the PLS-SEM procedure, which helped to observe that the main factor of virtual classes acceptance is the hedonic motivation.
dc.format.extentxvii, 129 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject.ddc370 - Educación::378 - Educación superior (Educación terciaria)
dc.titleFactores que determinan la aceptación de las clases virtuales en los estudiantes de la facultad de ingeniería de la Universidad Nacional de Colombia
dc.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programBogotá - Ingeniería - Maestría en Ingeniería - Ingeniería de Sistemas y Computación
dc.description.notesIncluye anexos
dc.coverage.countryColombia
dc.description.degreelevelMaestría
dc.description.degreenameMagíster en Ingeniería - Ingeniería de Sistemas y Computación
dc.description.researchareaUso de los sistemas de información
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentDepartamento de Ingeniería de Sistemas e Industrial
dc.publisher.facultyFacultad de Ingeniería
dc.publisher.placeBogotá, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Bogotá
dc.relation.referencesAhmed, A., Zahid, A., Wajid, H., Muhammad, H. R., & Vighio, S. (2019). Extending UTAUT2 toward acceptance of mobile learning in the context of higher education. Universal Access in the Information Society. https://doi.org/10.1007/s10209-019-00685-8
dc.relation.referencesAjzen, I., & Fishbein, M. (1973). Attitudinal and normative variables as predictors of specific behaviors. 27(1), 41–57.
dc.relation.referencesAlomary, A., & Woollard, J. (2015). How is technology accepted by users? A review of technology acceptance models and theories. November, 1–4.
dc.relation.referencesAlshehri, A., Rutter, M. J., & Smith, S. (2019). An implementation of the UTAUT model for understanding students’ perceptions of Learning Management Systems: A Study within Tertiary Institutions in Saudi Arabia. International Journal of Distance Education Technologies, 17(3), 1–24. https://doi.org/10.4018/IJDET.2019070101
dc.relation.referencesAl-tarawneh, J. M. (2019). Technology Acceptance Models and Adoption of Innovations: A Literature Review. 9(8), 833–857. https://doi.org/10.29322/IJSRP.9.08.2019.p92116
dc.relation.referencesArancibia M., M. L., Cabero Almenara, J., & Valdivia Zamorano, I. (2019). Estudio comparativo entre docentes y estudiantes sobre aceptación y uso de tecnologías con fines educativos en el contexto chileno. 11, 104–119.
dc.relation.referencesASCUN. (2021). ASCUN REVELA QUE MATRÍCULAS DEL AÑO 2020 DISMINUYERON, AUNQUE NO TANTO COMO SE PROYECTABA. https://www.ascun.org.co/noticias/detalle/ascun-revela-que-matriculas-del-ano-2020-disminuyeron-aunque-no-tanto-como-se-proyectaba
dc.relation.referencesAydemir, D., & Ulusu, N. N. (2020). Commentary: Challenges for PhD students during COVID-19 pandemic: Turning crisis into an opportunity. Biochemistry and Molecular Biology Education, March, 1–2. https://doi.org/10.1002/bmb.21351
dc.relation.referencesBagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254. https://doi.org/10.17705/1jais.00122
dc.relation.referencesCarlson, K. D., & Herdman, A. O. (2012). Understanding the impact of convergent validity on research results. Organizational Research Methods, 15(1), 17–32. https://doi.org/10.1177/1094428110392383
dc.relation.referencesChurchill, G. A. (1979). A Paradigm for Developing Better Measures of Marketing Constructs. Journal of Marketing Research, 16(1), 64. https://doi.org/10.2307/3150876
dc.relation.referencesCobos, J. V. (2015). HACIA EL FOMENTO DE LAS TIC EN EL SECTOR EDUCATIVO.
dc.relation.referencesCohen, J. (1998). Statistical Power Analysis for the Behavioral Sciences. Second Edition.
dc.relation.referencesCompeau, D. R., & Higgins, C. A. (1995). ASTM E2368-10, Standard Practice for Strain Controlled Thermomechanical Fatigue Testing. MIS Quarterly, 19(2), 189–211.
dc.relation.referencesDadayan, L., & Ferro, E. (2005). When Technology Meets the Mind : A Comparative Study of the Technology Acceptance Model. 137–144
dc.relation.referencesDavis, F. D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems Theory and Results.
dc.relation.referencesDavis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and Intrinsic Motivation to Use Computers in the Workplace
dc.relation.referencesDelone, W. H., & Mclean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. 4.
dc.relation.referencesDelone, W. H., & McLean, E. R. (2003). The DeLone and McLean Model of Information Systems Success : A Ten-Year Update. 19(4), 9–30.
dc.relation.referencesDulloo, R., Mokashi, J., & Puri, M. M. (1992). Exploring Technology Acceptance Theories and Models - A Comparative Analysis. 308–317.
dc.relation.referencesFornell, C., & Larcker, D. F. (2016). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research This, 18(1), 39–50.
dc.relation.referencesG. Dada, E., H. Alkali, A., & O. Oyewola, D. (2020). An Investigation into the Effectiveness of Asynchronous and Synchronous E-learning Mode on Students’ Academic Performance in National Open University (NOUN), Maiduguri Centre. International Journal of Modern Education and Computer Science, 11(5), 54–64. https://doi.org/10.5815/ijmecs.2019.05.06
dc.relation.referencesHair, J., Hult, T., Ringle, C., Sarstedt, M., Danks, N., & Ray, S. (2021). Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. http://www.
dc.relation.referencesHenseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
dc.relation.referencesIcek, A. (1991). The Theory of Planned Behavior Organizational Behavior and Human Decision Processes. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
dc.relation.referencesIordache, D. D., Balog, A., Pribeanu, C., & Lamanauskas, V. (2015). A Motivational Model for Facebook Acceptance by University Students.
dc.relation.referencesJohansen, R. R., & Bullen, C. V. (1988). Groupware: A Key to Managing Business Teams? 20–21.
dc.relation.referencesHair, J., Hult, T., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) Second Edition.
dc.relation.referencesJoshi, A., Kale, S., Chandel, S., & Pal, D. (2015). Likert Scale: Explored and Explained. British Journal of Applied Science & Technology, 7(4), 396–403. https://doi.org/10.9734/bjast/2015/14975
dc.relation.referencesKripanont, N. (2007). Examining a Technology Acceptance Model of Internet Usage by Academics within Thai Business Schools.
dc.relation.referencesKrischkowsky, A., Maurer, B., & Tscheligi, M. (2016). Captology and Technology Appropriation: Unintended Use as a Source for Designing Persuasive Technologies BT - Persuasive Technology (A. Meschtscherjakov, B. de Ruyter, V. Fuchsberger, M. Murer, & M. Tscheligi, Eds.; pp. 78–83). Springer International Publishing.
dc.relation.referencesLarsen, K., & Eargle, D. (2015). Theories Used in IS Research Wiki. http://is.theorizeit.org
dc.relation.referencesLee, P. L., & Pang, V. (2014). The influence of motivational orientations on academic achievement among working adults in continuing education. International Journal of Training Research, 12(1), 5–15. https://doi.org/10.5172/ijtr.2014.12.1.5
dc.relation.referencesMafraq, H. (2019). Maarefh - Proposed MOOCs ’ Platform for Saudi Arabia ’ s Higher Education Institutions. 77–82.
dc.relation.referencesMahande, R. D., Makassar, U. N., & Malago, J. D. (2016). an E-Learning Acceptance Evaluation Through Utaut Model in.
dc.relation.referencesMehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). Computers & Education The Influence of Values on E-learning Adoption. 141(December 2018). https://doi.org/10.1016/j.compedu.2019.103617
dc.relation.referencesMineducación. (2020). El programa “Profe en Tu Casa” llega a los territorios a través de siete canales regionales para acompañar el proceso de aprendizaje de los estudiantes y apoyar a sus familias. https://www.mineducacion.gov.co/portal/salaprensa/Noticias/395785:El-programa-Profe-en-Tu-Casa-llega-a-los-territorios-a-traves-de-siete-canales-regionales-para-acompanar-el-proceso-de-aprendizaje-de-los-estudiantes-y-apoyar-a-sus-familias
dc.relation.referencesMingers, J. (2006). Real ising Systems Thinking : Knowledge and Action in Management Science OR and Systems Thinking for Community Development Participatory Policy Design and Governance for a Global Age
dc.relation.referencesMinkman, E., Rutten, M. M., Ph, D., Sanden, M. C. A. van der, & Ph, D. (2016). Acceptance of Mobile Technology for Citizen Science in Water Resource Management. 1–9. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001043.
dc.relation.referencesMoore, G. C., & Benbasat, I. (1991). Moore and Benbasat.pdf. In Information Systems Research (Vol. 2, pp. 192–222).
dc.relation.referencesMorosan, C., & Defranco, A. (2016). International Journal of Hospitality Management It ’ s about time : Revisiting UTAUT2 to examine consumers ’ intentions to use NFC mobile payments in hotels ଝ. International Journal of Hospitality Management, 53, 17–29. https://doi.org/10.1016/j.ijhm.2015.11.003
dc.relation.referencesNguyen, T. D., Nguyen, T. M., Pham, Q., & Misra, S. (2014). Acceptance and Use of E-Learning Based on Cloud Computing : The Role of Consumer Innovativeness. 159–174.
dc.relation.referencesOlasina, G. (2019). Human and social factors affecting the decision of students to accept e-learning. Interactive Learning Environments, 27(3), 363–376. https://doi.org/10.1080/10494820.2018.1474233
dc.relation.referencesOrtega, L. (2017). Evaluación de los factores de la predisposición que inciden en la aceptación tecnológica de los empleados en el sector público en Colombia. http://bdigital.unal.edu.co/64366/1/luis fernando Trabajo Final Grado 2017-02_V2.9.pdf
dc.relation.referencesPeña Reyes, J. I., Moreno M, C. E., & Mejía S, I. S. (2012). Modelo de sistema de información para apoyar la gestión ambiental proactiva en PyMEs Information system model to support proactive environmental management in SME Modèle de système d ’ information mis en place pour une gestion efficace de l ’ environnemen
dc.relation.referencesQuezada, C. (2016). De telegrafía sin hilos a radiodifusión: Apropiación tecnológica de la radio en Chile, 1901-1931. Revista de Historia Iberoamericana, 9(1), 103–125. https://doi.org/10.3232/RHI.2016.V9.N1.05
dc.relation.referencesQuicaño Arones, C. (2019). Universidad Nacional Mayor de San Marcos Facultad de Ingeniería de Sistemas e Informática Unidad de Posgrado UTAUT2 adaptado para medir la intención del comportamiento en la aceptación tecnológica del servicio de internet de alta velocidad en cadenas hote.
dc.relation.referencesRamllah, Nurkhin, A. (2020). Analysis of Factors Affecting Behavioral Intention to UseE-Learning Uses the Unified Theory of Acceptance and Useof Technology Approach.
dc.relation.referencesRamlo, S. (2011). Using Word Clouds to Present Q Methodology Data and Findings The Preferences of Q Methodologists at the Factor Analytic Stage: An Examination of Practice View project. https://www.researchgate.net/publication/235799664
dc.relation.referencesRectoría, C. (2020a). COMUNICADO No. 07 A LA COMUNIDAD UNIVERSITARIA. 07, 8–9
dc.relation.referencesRectoría, C. (2020b). COMUNICADO No. 08 A LA COMUNIDAD UNIVERSITARIA. 08, 2020.
dc.relation.referencesReyes Peña, J. I., & Favier, M. (2006). E-Learning en Comunidades Aisladas . Una aislamiento del aprendiente en E-learning. Avances En Sistemas e Informática, 3(1), 21–27.
dc.relation.referencesRicardo B, C., & Iriarte D, F. (2017). Las TIC en la educación superior. In Universidad del Norte (Vol. 53, Issue 9). https://doi.org/10.1017/CBO9781107415324.004
dc.relation.referencesRizun, M., & Strzelecki, A. (2020). Students’ acceptance of the covid-19 impact on shifting higher education to distance learning in Poland. International Journal of Environmental Research and Public Health, 17(18), 1–19. https://doi.org/10.3390/ijerph17186468
dc.relation.referencesRondan Cataluña, F. J., Arenas Gaitán, J., & Ramírez Correa, P. E. (2015). A comparison of the different versions of popular technology acceptance models A non-linear perspective. https://doi.org/10.1108/K-09-2014-0184
dc.relation.referencesSabraz Nawaz, S., & Rusith, M. (2019). University students’ intention to use e-learning systems: A study of higher educational institutions in Sri Lanka. https://doi.org/10.1108/ITSE-11-2018-0092
dc.relation.referencesSamaradiwakara, G. D. M. N. (2014). Comparison of existing technology acceptance theories and models to suggest a well improved theory-model. 1(1), 21–36.
dc.relation.referencesSchepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information and Management, 44(1), 90–103. https://doi.org/10.1016/j.im.2006.10.007
dc.relation.referencesSharma, R., & Mishra, R. (2014). A Review of Evolution of Theories and Models of Technology Adoption. 6(2), 17–29.
dc.relation.referencesShoaib Farooq, Muhammad; Salam, Maimoona;Jaafar, Norizan; Fayolle, Alain; Ayupp, Kartinah; Radovic-Markovic, Mirjana; Sajid, A. (2016). Acceptance and Use of Lecture Capture System (LCS) in Executive Business Studies Extending UTAUT2.
dc.relation.referencesSohn, K., & Kwon, O. (2020). Telematics and Informatics Technology acceptance theories and factors in fl uencing arti fi cial Intelligence-based intelligent products. 47(April 2019), 1–14. https://doi.org/10.1016/j.tele.2019.101324
dc.relation.referencesTamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2019). Use of ‘ Habit ’ Is not a Habit in Understanding Individual Technology Adoption : A Review of UTAUT2 Based Empirical Studies. Springer International Publishing. https://doi.org/10.1007/978-3-030-04315-5
dc.relation.referencesTamilmani, K., Rana, N. P., Prakasam, N., & Dwivedi, Y. K. (2019). The battle of Brain vs . Heart : A literature review and meta-analysis of “ hedonic motivation ” use in UTAUT2. International Journal of Information Management, 46(October 2018), 222–235. https://doi.org/10.1016/j.ijinfomgt.2019.01.008
dc.relation.referencesTang, Y. M., Chen, P. C., Law, K. M. Y., Wu, C. H., Lau, Y., Guan, J., He, D., & Ho, G. T. S. (2021). Comparative analysis of Student’s live online learning readiness during the coronavirus (COVID-19) pandemic in the higher education sector. Computers & Education, 168(March), 104211. https://doi.org/10.1016/j.compedu.2021.104211
dc.relation.referencesTarhini, M. E. A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA : Extending the Unified Theory. Educational Technology Research and Development. https://doi.org/10.1007/s11423-016-9508-8
dc.relation.referencesTavakol, M., & Dennick, R. (2011). Making sense of Cronbach’s alpha. International Journal of Medical Education, 2, 53–55. https://doi.org/10.5116/ijme.4dfb.8dfd
dc.relation.referencesTaylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. In Information Systems Research (Vol. 6, Issue 2, pp. 144–176). https://doi.org/10.1287/isre.6.2.144
dc.relation.referencesThompson, R., & Barclay, D. W. (1995). The partial least squares approach to causal modeling: Personal computer adoption and use as an illustration. https://www.researchgate.net/publication/313137896
dc.relation.referencesThompson, R., Higgins, C., & Howell, J. (1991). Personal Computing : Toward a Conceptual Model of Utilization . March
dc.relation.referencesTseng, T. H., Lin, S., Wang, Y., Liu, H., Tseng, T. H., Lin, S., Wang, Y., & Liu, H. (2019). Investigating teachers ’ adoption of MOOCs : the perspective of UTAUT2. Interactive Learning Environments, 0(0), 1–16. https://doi.org/10.1080/10494820.2019.1674888
dc.relation.referencesUNESCO. (2020). Impacto de COVID-19 en la educación. https://es.unesco.org/covid19/educationresponse
dc.relation.referencesUNESCO IESALC. (2020). COVID-19 y educación superior: De los efectos inmediatos al día después. 1–57.
dc.relation.referencesUniversidad Nacional de Colombia. (2020a). Encuesta de seguimiento y prospectiva de las actividades académicas en la UN 2020 Ficha técnica. http://red.unal.edu.co/cursos/dnia/un2020-02/qu_sabemos_de_nuestros_estudiantes.html
dc.relation.referencesUniversidad Nacional de Colombia. (2020b). RESOLUCIÓN 347 DE 2020. Por la cual se adoptan medidas en materia académica para afrontar los efectos del confinamiento por la emergencia sanitaria causada por el COVID-19. http://www.legal.unal.edu.co/rlunal/home/doc.jsp?d_i=95385
dc.relation.referencesVacchieri, A. (2013). Programa TIC y Educación Básica.
dc.relation.referencesVásquez Aunqui, J. L. (2019). Carrera de Ingeniería Industrial.
dc.relation.referencesVenkatesh, V. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly: Management Information Systems, 47(2), 252–269. https://doi.org/10.1006/mvre.1994.1019
dc.relation.referencesVenkatesh, V. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. 36(1), 157–178
dc.relation.referencesVenkatesh, V., & Bala, H. (2008). Technology Acceptance Model 3 and a Research Agenda on Interventions. 39(2), 273–315.
dc.relation.referencesVenkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model : Four Longitudinal Field Studies.
dc.relation.referencesVenkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. 27(3), 425–478.
dc.relation.referencesWang, Y., Wang, Y., & Jian, S. (2019). Investigating the Determinants of Students ’ Intention to Use Business Simulation Games. 2. https://doi.org/10.1177/0735633119865047
dc.relation.referencesWang, Y., Wang, Y., & Jian, S. (2020). Investigating the Determinants of Students ’ Intention to Use Business Simulation Games. 2. https://doi.org/10.1177/0735633119865047
dc.relation.referencesWebster, J., & Martocchio, J. J. (1992). Microcomputer playfulness: Development of a measure with workplace implications. MIS Quarterly: Management Information Systems, 16(2), 201–224. https://doi.org/10.2307/249576
dc.relation.referencesXiao, M. (2019). Factors Influencing eSports Viewership : An Approach Based on the Theory of Reasoned Action. 1–31. https://doi.org/10.1177/2167479518819482
dc.relation.referencesYan, P., Hui, S., Heng, B., Selvachandran, G., Quynh, L., Hoang, A., & Minh, T. (2020). Perception , acceptance and willingness of older adults in Malaysia towards online shopping : a study using the UTAUT and IRT models. Journal of Ambient Intelligence and Humanized Computing, 0123456789. https://doi.org/10.1007/s12652-020-01718-4
dc.relation.referencesZhihua, L., & Zhaojun, W. (2009). The Application of Maturity Model in the Schools ’ ICT Project. 2009 International Conference on Computational Intelligence and Software Engineering, 1–4. https://doi.org/10.1109/CISE.2009.5366438
dc.relation.referencesZwain, A. (2020). Investigating Determinants of Faculty and Students ’ Acceptance of E-Learning Management Systems using UTAUT2. January.
dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembMotivation in education
dc.subject.lembMotivación en educación
dc.subject.lembUniversities and colleges-alumni
dc.subject.lembEstudiantes de educación superior
dc.subject.proposalUTAUT2
dc.subject.proposalClases virtuales
dc.subject.proposalFactores de aceptación
dc.subject.proposalPLS-SEM
dc.subject.proposalCOVID-19
dc.subject.proposalVirtual classes
dc.subject.proposalPLS-SEM
dc.subject.proposalUTAUT2
dc.subject.unescoAprendizaje en línea
dc.subject.unescoElectronic learning
dc.title.translatedDetermining factors of virtual classes acceptance for Engineering Faculty students of Universidad Nacional de Colombia
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
dcterms.audience.professionaldevelopmentInvestigadores
dcterms.audience.professionaldevelopmentMaestros
dcterms.audience.professionaldevelopmentPadres y familias
dcterms.audience.professionaldevelopmentPersonal de apoyo escolar
dcterms.audience.professionaldevelopmentPúblico general


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