Aprendiendo matemática de manera divertida desde el hogar: factores asociados al uso de la plataforma online Conecta Ideas
DOI:
https://doi.org/10.21754/iecos.v23i1.1554Palabras clave:
Conecta Ideas, Estructural, Modelo de aceptaciónResumen
El objetivo de este estudio consiste en analizar los factores asociados al uso en el hogar de la plataforma online “Conecta Ideas” por parte de estudiantes de 4to de primaria de instituciones públicas de Lima Metropolitana en el año 2019. El estudio se basa en un Modelo de Aceptación de Tecnología (TAM), el cual predice la adopción de una tecnología mediada por la percepción de utilidad, facilidad percibida, y disfrute. Utilizando un modelo estadístico de ecuaciones estructurales multi-nivel, se encuentra que el disfrute percibido es la variable que mejor predice el uso de la plataforma. A su vez, las estudiantes mujeres muestran mayores niveles de uso. Finalmente, estudiantes con mayor preocupación por la matemática muestran menores niveles de uso.
Descargas
Citas
Abdullah, F.; Ward, R.; Ahmed; A. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in Human Behavior 63(2016), 75-90.
Arias,E.; Cristia, J. y Cueto,S. (Eds.). (2020). Aprender Matemática en el Siglo XXI. A Sumar con Tecología. (pp. 263 – 278). Banco InterAmericano de Desarrollo.
Arias, E. y Cristia, J. (2014). El BID y la tecnología para mejorar el aprendizaje: ¿cómo promover programas efectivos? Nota Técnica IDB – TN – 670.
Araya, R. y Cristia, J. (2020). Guiando la tecnología para promover la práctica efectiva. En E. Arias, J. Cristiá y S. Cueto (Ed.), Aprender Matemática en el Siglo XXI. A Sumar con Tecnología. (pp. 263 – 278). Banco InterAmericano de Desarrollo.
Araya, R. Arias, E. Bottan, N. y Cristia, J. (2019). ¿Funciona la gamificación en la educación? Evidencia experimental de Chile. Documento de Trabajo del BID, IDB – WP982.
Azjen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs.
Banerjee, A.; Cole, S.; Duflo, E.; Linden, L. (2007). Remedying Education: Evidence from Two Randomized Experiments in India. The Quarterly Journal of Economics (122)3, 1235–1264. https://doi.org/10.1162/qjec.122.3.1235
Barrantes Cáceres, R., & Cozzubo Chaparro, A. (2019). Age for learning, age for teaching: the role of inter-generational, intra-household learning in Internet use by older adults in Latin America. Information, Communication & Society, 22(2), 250-266.
Bicchieri, C. (2017) Norms in the Wild. How to diagnose, measure and change social norms. New York, NY: Oxford University press
Busing, F. M. T. A. (1993). Distribution characteristics of variance estimates in two-level models. Preprint PRM, 93-04.
Bryk, A. S., Raudenbush, S. W., & Congdon, R. T. (1996). HLM: Hierarchical linear and nonlinear modeling with the HLM/2L and HLM/3L programs. Scientific Software International.
Claro, S., Paunesku, D., & Dweck, C. S. (2016). Growth mindset tempers the effects of poverty on academic achievement. Proceedings of the National Academy of Sciences, 113(31), 8664-8668.
Correa, J. J. (2004). Determinantes del rendimiento educativo de los estudiantes de secundaria en Cali: un análisis multinivel. Sociedad y Economía, (6), 81-105.
Cristia, C. Ibarrarán, P.; Cueto, S.; Santiago, A. y Severín, E. (2017). Technology and child development: evidence from the One Laptop per Child Program. American Economic Journal: Applied Economics (9)3, 295 – 320. Disponible en: https://www.aeaweb.org/articles?id=10.1257/app.20150385
Cueto, S.; Felipe, C.; León, J. (2018). Digital Access, Use and Skills Across Four Countries: Construction of Scales and Preliminary Results from the Young Lives Round 5 Survey. Documento Técnico 46 de Niños del Milenio. www.ninosdelmilenio.org
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487.
De Melo, G.; Machado, A. y Mirada,A. (2016). El impacto en el aprendizaje del programa Una Laptop por Niño. La evidencia de Uruguay. El Trimestre Económico (2) 334. DOI: 10.20430/ete.v84i334.305
Fairlie, R.; Robinson, J. (2013). Experimental evidence on the effects of home computers on academic achievement among cchoolchildren. Documento de trabajo del NBER núm. 19 060, NBER, Cambridge, Massachusetts.
Flavell, J. H. (1993) El desarrollo cognitivo. Madrid: Visor.
Gelman, A., & Hill, J. (2006). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.
Gestión, (2020). La opinión de más de 8000 familias sobre la educación remota durante la pandemia. Diario Gestión. https://gestion.pe/blog/bid/2020/06/la-opinion-de-mas-de-8000-familias-sobre-la-educacion-remota-durante-la-pandemia.html/?ref=gesr
Hanchane, S., & Mostafa, T. (2012). Solving endogeneity problems in multilevel estimation: an example using education production functions. Journal of Applied Statistics, 39(5), 1101-1114.
Hernández, S. (2008). El Modelo Constructivista con las Nuevas Tecnologías: Aplicado en el Proceso de Aprendizaje. Revista de Universidad y Sociedad de Conocimiento. Monográfico Comunicación y construcción del conocimiento en el nuevo espacio tecnológico (5) 2, 26 – 35.
Hox, J. (1998). Multilevel modeling: When and why. In Classification, data analysis, and data highways (pp. 147-154). Springer, Berlin, Heidelberg.
King, W.R. y He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management 43 (2006) 740–755
Kreft, I. G., & Yoon, B. (1994). Are multilevel techniques necessary? An attempt at demystification.
Lai, F.T.T. y Kwan, J. L.Y. (2016). Socioeconomic influence on adolescent problematic Internet use through school-related psychosocial factors and pattern of Internet use. Computers in Human Behavior, 68(2017), 121 – 136.
Lee, M. K., Cheung, C. M., & Chen, Z. (2005). Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. Information & Management, 42(8), 1095e1104.
Lee, E., Han, S., & Chung, Y. (2014). Internet use of consumers aged 40 and over: Factors that influence full adoption. Social Behavior and Personality: an international journal, 42(9), 1563-1574.
León, J., & Sugimaru, C. (2017). Las expectativas educativas de los estudiantes de secundaria de regiones amazónicas: un análisis de los factores asociados desde el enfoque de eficacia escolar. MISC.
Malamud, O. y Pop-Eleches, C. (2010). Home Computer Use and the Development of Human Capital. The Quarterly Journal of Economics (126)2, 987-1 027.
Mishra, P. y Koehler, M. (2006). Technological Pedagogical Content Knowledge: A Framework for Teacher Knowledge. Teachers College Records. 108, 1017-1054. http://dx.doi.org/10.1111/j.1467-9620.2006.00684.x
Mo, D.; Swinnen, J.; Zhang, L.; Hongmei, Y.; Qu, Z.; Boswell,M. y Rozelle, S. (2013). Can one-to-one computer narrow the digital divide and the educational gap in China? The case of Beijing migrant schools. World Development, vol. 46, núm. C, pp. 14-29.
Morán, F., Morán F. y Albán, J. (2017). Formación del Docente y su Adaptación al Modelo TPACK. Revista Ciencias Pedagógicas e Innovación, 5 (1), 51-60. http://dx.doi.org/10.26423/rcpi.v5i1.154
Morris, M. G., & Venkatesh, V. (2000). Age differences in technology adoption decisions: Implications for a changing work force. Personnel psychology, 53(2), 375-403.
Nikou, S.A. y Economides, A.A. (2017). Mobile-Based Assessment: Integrating acceptance and motivational factors into a combined model of Self-Determination Theory and Technology Acceptance. Computers in Human Behavior, 68(2017), 83-95.
Niess, M., van Zee, E. y Gillow-Wiles, H. (2010). Knowledge Growth in Teaching Mathematics/Science with Spreadsheets: Moving PCK to TPACK through Online Professional Development. Journal of Digital Learning in Teacher Education, 27(2), 42-52.
Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & education, 48(2), 250-267.
Niehaves, B., & Plattfaut, R. (2014). Internet adoption by the elderly: employing IS technology acceptance theories for understanding the age-related digital divide. European Journal of Information Systems, 23(6), 708-726.
OECD. (2015). Students, Computers and Learning: Making the Connection, PISA,OECD Publishing.
Porter, C.E. y Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Buissness Research 59(2006) 999-1007.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (Vol. 1). sage.
Sanchez, R. A., & Hueros, A. D. (2010). Motivational factors that influence the acceptance of moodle using TAM. Computers in Human Behavior, 26(6),1632-1640.
Scherer R., Rohatgi A. & Hatlevik O.E. (2010). Students’ profiles of ICT use: identification, determinants, and relations to achievement in a computer and information literacy test. Computers in Human Behavior. DOI: 10.1016/j.chb.2017.01.034.
Schere, R., Siddiq, F. y Tondeur, J.(2019). The technology acceptance model (TAM): A meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Computers & Education 128(2019), 13 -35.
Snijders, T. A., & Bosker, R. J. (1994). Modeled variance in two-level models. Sociological methods & research, 22(3), 342-363.
StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.
Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of educational research, 79(2), 625-649.
Tarhini, A., Hone, K.S., Liu, X. Factors affecting students’ acceptance of e-learning environments in developing countries: A structural equation modeling approach. International Journal of Information and Education Technology, 3(1): 54 - 59
UNESCO (2016). Informe de resultados TERCE. Factores asociados. Disponible en: http://www.eduy21.org/Publicaciones/Terce%203.pdf
Van der Leeden, R., & Busing, F. M. T. A. (1994). First iteration versus IGLS/RIGLS estimates in two-level models: A Monte Carlo study with ML3. Preprint PRM, 94(03).
Varela, L. A. Y., Tovar, L. A. R., & Chaparro, J. (2010). Modelo de aceptación tecnológica (TAM): un estudio de la influencia de la cultura nacional y del perfil del usuario en el uso de las TIC. Innovar. Revista de Ciencias Administrativas y Sociales, 20(36), 187-203.
Vekiri, I. (2010). Socioeconomic differences in elementary students’ ICT beliefs and out-of-school experiences. Computers and Education (54) 4, 941 – 950. https://doi.org/10.1016/j.compedu.2009.09.029
Venkatesh, V. y Davis, F. (1996) A model of the antecedents of perceived ease of use: Development and Test. Decision Sciences 27(3), 451.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342 - 365.
Wu, B. y Chen, X. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. 67(2017), 221-232
Yang, Q.-F., Chang, S.-C., Hwang, G.-J., & Zou, D. (2020). Balancing cognitive complexity and gaming level: Effects of a cognitive complexity-based competition game on EFL students’ English vocabulary learning performance, anxiety and behaviors. Computers & Education, 103808.
Yaslin, M.E.; Kutlu, B. (2019). Examination of students’ acceptance of and intention to use learning management systems using extended TAM. British Journal of Educational Techology (50)5 pp 2414–2432.
Yu, M.; Yuen, A.H.K. y Park, J. (2012). Students’ computer use at home: a study on family environment and parental influence. Research and Practice in Technology Enhaced Learning (7)1, 3-23. Disponible en: https://www.researchgate.net/publication/255960367_Students’_computer_use_at_home_a_study_on_family_environment_and_parental_influence
Descargas
Publicado
Cómo citar
Número
Sección
Licencia
Derechos de autor 2022 Claudia Sugimara, Carla Glave
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
CC BY 4.0 DEED Attribution 4.0 International
