La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico
La toma de decisiones en educación superior está cada vez más basada en evidencias. Es recurrente el uso de modelos predictivos, modelaciones y de mediaciones tecnológicas para analizar datos y monitorear rutas de aprendizaje. A través de un estudio bibliométrico con una base de datos de 16324 artíc...
- Autores:
-
González Jiménez, Dulfay Astrid
Salazar-Tabima, Jerfenzon
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Universidad Autónoma de Occidente
- Repositorio:
- RED: Repositorio Educativo Digital UAO
- Idioma:
- spa
- OAI Identifier:
- oai:red.uao.edu.co:10614/13973
- Acceso en línea:
- https://hdl.handle.net/10614/13973
https://red.uao.edu.co/
- Palabra clave:
- Indicadores científicos
Science indicators
Innovación educativa
Analítica académica
Efectividad académica
Toma de decisión
Educational innovation
Academic analytics
Academic effectiveness
Decision making
- Rights
- openAccess
- License
- Derechos reservados - Pontificia Universidad Católica del Ecuador Sede Ambato, 2021
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La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico |
| dc.title.alternative.eng.fl_str_mv |
Academic analytics and decision making in higher education: A bibliometric study |
| title |
La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico |
| spellingShingle |
La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico Indicadores científicos Science indicators Innovación educativa Analítica académica Efectividad académica Toma de decisión Educational innovation Academic analytics Academic effectiveness Decision making |
| title_short |
La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico |
| title_full |
La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico |
| title_fullStr |
La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico |
| title_full_unstemmed |
La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico |
| title_sort |
La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico |
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González Jiménez, Dulfay Astrid Salazar-Tabima, Jerfenzon |
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González Jiménez, Dulfay Astrid Salazar-Tabima, Jerfenzon |
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Indicadores científicos |
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Indicadores científicos Science indicators Innovación educativa Analítica académica Efectividad académica Toma de decisión Educational innovation Academic analytics Academic effectiveness Decision making |
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Science indicators |
| dc.subject.proposal.spa.fl_str_mv |
Innovación educativa Analítica académica Efectividad académica Toma de decisión |
| dc.subject.proposal.eng.fl_str_mv |
Educational innovation Academic analytics Academic effectiveness Decision making |
| description |
La toma de decisiones en educación superior está cada vez más basada en evidencias. Es recurrente el uso de modelos predictivos, modelaciones y de mediaciones tecnológicas para analizar datos y monitorear rutas de aprendizaje. A través de un estudio bibliométrico con una base de datos de 16324 artículos científicos arbitrados en Scopus, se analizó el estado actual de la investigación sobre la toma de decisiones con analítica académica en educación superior. El refinamiento mediante herramientas Vosviewer y Bibliometrix, arrojó 1515 artículos sobre analítica académica y toma de decisiones, publicadas en un total de 800 revistas, los autores se concentran en Estados Unidos, Irán y Reino-Unido. Autores como Salas-Rueda, Ricardo, Sheikhbardsiri, Hojjat y Farokhzadian Jamileh, son los que tienen mayor número de publicaciones. Dos son los enfoques que prevalecen en las investigaciones estudiadas: el orientado en el usuario (estudiante), y el orientado a la gestión educativa. Para el primero, el análisis del comportamiento, percepciones, autorregulación, modelos predictivos y monitoreo de trayectorias, son los principales objetos de interés y, para el segundo, el uso de tecnologías, y análisis de un gran volumen de datos, es lo más prevalente, tanto para la toma de decisiones, como para planes de mejoramiento y gestión. |
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2021 |
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González-Jiménez, D. A. & Salazar-Tabima, J. (2021). La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométrico. Veritas & Research, 3(2), 122-33. http://revistas.pucesa.edu.ec/ojs/index.php?journal=VR&page=article&op=view&path%5B%5D=91 |
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Abbasgholizadeh Rahimi, S., Rodriguez, C., Croteau, J., Sadeghpour, A., Navali, A.-M., & Légaré, F. (2021). Continuing professional education of Iranian healthcare professionals in shared decision-making: lessons learned. BMC Health Services Research, 21(1). https://doi.org/10.1186/s12913-021-06233-6 Alia, A., Japelj Pavešić, B., & Rožman, M. (2022). Opportunity to Learn Mathematics and Science. In IEA Research for Education (Vol. 13). https://doi.org/10.1007/978-3-030-85802-5_3 Amala Jayanthi, M., & Elizabeth Shanthi, I. (2020). Role of Educational Data Mining in Student Learning Processes with Sentiment Analysis: A Survey. International Journal of Knowledge and Systems Science, 11(4), 31–44. https://doi.org/10.4018/IJKSS.2020100103 Ardanuy J. (2012). Breve introducción a la bibliometría [Internet]. España: Universidad de Barcelona. [citado el 12 de enero de 2018]. Disponible en: http://diposit.ub.edu/dspace/bitstream/2445/30962/1/breve%20introduccion%20bibliometria.pdf Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), 959-975. Börgeson, E., Sotak, M., Kraft, J., Bagunu, G., Biörserud, C., & Lange, S. (2021). Challenges in PhD education due to COVID-19 - disrupted supervision or business as usual: a cross-sectional survey of Swedish biomedical sciences graduate students. BMC Medical Education, 21(1). https://doi.org/10.1186/s12909-021-02727-3 Bozdoğan, A. E., Demir, A., & Şahinpınar, D. (2022). Bibliometric assessment based on web of science database: Educational research articles on botanic gardens, national parks, and natural monuments. Participatory Educational Research, 9(1), 303–323. https://doi.org/10.17275/per.22.17.9.1 de Oliveira, C. F., Sobral, S. R., Ferreira, M. J., & Moreira, F. (2021). How does learning analytics contribute to prevent students’ dropout in higher education: A systematic literature review. Big Data and Cognitive Computing, 5(4). https://doi.org/10.3390/bdcc5040064 Campaña-Chaglla, J., & Pérez, O. (2021). Educación como determinante de la movilidad económica en las familias. Veritas & Research, 3(1), 90-100. Recuperado a partir de []=57 Đerić, I., Elezović, I., & Brese, F. (2022). Teachers, Teaching and Student Achievement. In IEA Research for Education (Vol. 13). https://doi.org/10.1007/978-3-030-85802-5_7 Elihami, E. (2021). Bibliometric analysis of islamic education learning loss in the COVID-19 pandemic. Linguistics and Culture Review, 5, 851–859. https://doi.org/10.37028/lingcure.v5nS1.1469 Farokhzadian, J., Forughameri, G., & Mohseny, M. (2020). Health promoting behaviors of staff in a university of medical sciences in southeast of Iran. International Journal of Adolescent Medicine and Health, 32(5). https://doi.org/10.1515/ijamh-2017-0208 Ferns, S., Phatak, A., Benson, S., & Kumagai, N. (2021). Building employability capabilities in data science students: An interdisciplinary, industry-focused approach. Teaching Statistics, 43(S1), S216–S225. https://doi.org/10.1111/test.12272 Gamazo, A., & Martínez-Abad, F. (2020). An Exploration of Factors Linked to Academic Performance in PISA 2018 Through Data Mining Techniques. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.575167 Garg, K., Chaurasia, B., Gienapp, A. J., Splavski, B., & Arnautovic, K. I. (2022). Bibliometric Analysis of Publications From 2011–2020 in 6 Major Neurosurgical Journals (Part 1): Geographic, Demographic, and Article Type Trends. World Neurosurgery, 157, 125–134. https://doi.org/10.1016/j.wneu.2021.10.091 Kanwar, P., & Rathore, M. (2021). Cognitive Study of Data Mining Techniques in Educational Data Mining for Higher Education. In Lecture Notes in Networks and Systems (Vol. 190). https://doi.org/10.1007/978-981-16-0882-7_20 Kulkanjanapiban, P., & Silwattananusarn, T. (2022). Comparative analysis of Dimensions and Scopus bibliographic data sources: An approach to university research productivity. International Journal of Electrical and Computer Engineering, 12(1), 706–720. https://doi.org/10.11591/ijece.v12i1.pp706-720 Lameva, B., Džumhur, Ž., & Rožman, M. (2022). Characteristics of Principals and Schools in the Dinaric Region. In IEA Research for Education (Vol. 13). https://doi.org/10.1007/978-3-030-85802-5_8 Li, J., & Jiang, Y. (2021). The Research Trend of Big Data in Education and the Impact of Teacher Psychology on Educational Development During COVID-19: A Systematic Review and Future Perspective. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.753388 Li, Z., He, P., Bian, T., Xiao, Y., Gao, L., & Liu, H. (2022). Bibliometric and visualized analysis of ferroptosis mechanism research. Chinese Journal of Tissue Engineering Research, 26(8), 1258–1265. https://doi.org/10.12307/2022.224 Mohammadian, S., Alishahi, A. G., & Rafiei, M. (2021). Causal model of acceptance and use of information and communication technology by students of Tabriz University of medical sciences in educational and research purposes based on the UTAUT model. Iranian Journal of Information Processing and Management, 36(2), 391–418. Mousavinasab, E., Zarifsanaiey, N., R. Niakan Kalhori, S., Rakhshan, M., Keikha, L., & Ghazi Saeedi, M. (2021). Intelligent tutoring systems: a systematic review of characteristics, applications, and evaluation methods. Interactive Learning Environments, 29(1), 142–163. https://doi.org/10.1080/10494820.2018.1558257 Narayanan, M. D. K., Deora, H., Garg, K., & Grotenhuis, J. A. (2022). A Comparative Scientometric Analysis of the 100 Most Cited Articles of Acta Neurochirurgica (Wien) and World Neurosurgery. World Neurosurgery, 157, 106–122. https://doi.org/10.1016/j.wneu.2021.10.099 Nielsen, W., Georgiou, H., Jones, P., & Turney, A. (2020). Digital Explanation as Assessment in University Science. Research in Science Education, 50(6), 2391–2418. https://doi.org/10.1007/s11165-018-9785-9 Ochoa, X., & Wise, A. F. (2021). Supporting the shift to digital with student-centered learning analytics. Educational Technology Research and Development, 69(1), 357–361. https://doi.org/10.1007/s11423-020-09882-2 Page, M. J., Moher, D., Fidler, F. M., Higgins, J. P. T., Brennan, S. E., Haddaway, N. R., … McKenzie, J. E. (2021). The REPRISE project: protocol for an evaluation of REProducibility and Replicability In Syntheses of Evidence. Systematic Reviews, 10(1). https://doi.org/10.1186/s13643-021-01670-0 Patall, E. A. (2021). Implications of the open science era for educational psychology research syntheses. Educational Psychologist, 56(2), 142–160. https://doi.org/10.1080/00461520.2021.1897009 Prado, J. W., Alcantara, V. D., Carvalho, F. D., Vieira, K. C., Machado, L. K. C., & Tonelli, D. F. (2016). Multivariate analysis of credit risk and bankruptcy research data: A bibliometric study involving different knowledge fields (1968–2014). Scientometrics, 106(3), 1007–1029. doi:10.1007/s11192-015- 1829-6. Roegman, R., Kenney, R., Maeda, Y., & Johns, G. (2021). When Data-Driven Decision Making Becomes Data-Driven Test Taking: A Case Study of a Midwestern High School. Educational Policy, 35(4), 535–565. https://doi.org/10.1177/0895904818823744 Rojas-Lamorena, Á. J., Del Barrio-García, S., & Alcántara-Pilar, J. M. (2022). A review of three decades of academic research on brand equity: A bibliometric approach using co-word analysis and bibliographic coupling. Journal of Business Research, 139, 1067–1083. https://doi.org/10.1016/j.jbusres.2021.10.025 Romero, C., & Ventura, S. (2020). 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Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, 2021(3), 184–189. https://doi.org/10.33271/nvngu/2021-3/184 Tipton, E., Spybrook, J., Fitzgerald, K. G., Wang, Q., & Davidson, C. (2021). Toward a System of Evidence for All: Current Practices and Future Opportunities in 37 Randomized Trials. Educational Researcher, 50(3), 145–156. https://doi.org/10.3102/0013189X20960686 Topuz, K., Jones, B. D., Sahbaz, S., & Moqbel, M. (2021). Methodology to combine theoretical knowledge with a data-driven probabilistic graphical model. Journal of Business Analytics. https://doi.org/10.1080/2573234X.2021.1937351 Tsiakmaki, M., Kostopoulos, G., Kotsiantis, S., & Ragos, O. (2021). Fuzzy-based active learning for predicting student academic performance using autoML: a step-wise approach. Journal of Computing in Higher Education, 33(3), 635–667. https://doi.org/10.1007/s12528-021-09279-x Van Eck, N.J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. Vrapi, R., Alia, A., & Brese, F. (2022). Characteristics of High- and Low-Performing Students. In IEA Research for Education (Vol. 13). https://doi.org/10.1007/978-3-030-85802-5_9 Wang, R. (2022). Application of Internet of Things in Online Teaching Platform. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 98). https://doi.org/10.1007/978-3-030-89511-2_96 Wang, Y. (2021). Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256–270. https://doi.org/10.1108/JEA-10-2020-0216 Yan, H., Lin, F., & Kinshuk. (2021). Including Learning Analytics in the Loop of Self-Paced Online Course Learning Design. International Journal of Artificial Intelligence in Education, 31(4), 878–895. https://doi.org/10.1007/s40593-020-00225-z Zhang, R., Zhao, W., & Wang, Y. (2021). Big data analytics for intelligent online education. Journal of Intelligent and Fuzzy Systems, 40(2), 2815–2825. https://doi.org/10.3233/JIFS-189322 |
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González Jiménez, Dulfay Astridvirtual::1994-1Salazar-Tabima, Jerfenzon024f022dd04455c53b88ff5be6440cfe2022-06-14T19:50:57Z2022-06-14T19:50:57Z202126973375https://hdl.handle.net/10614/13973Universidad Autónoma de OccidenteRepositorio Educativo Digitalhttps://red.uao.edu.co/La toma de decisiones en educación superior está cada vez más basada en evidencias. Es recurrente el uso de modelos predictivos, modelaciones y de mediaciones tecnológicas para analizar datos y monitorear rutas de aprendizaje. A través de un estudio bibliométrico con una base de datos de 16324 artículos científicos arbitrados en Scopus, se analizó el estado actual de la investigación sobre la toma de decisiones con analítica académica en educación superior. El refinamiento mediante herramientas Vosviewer y Bibliometrix, arrojó 1515 artículos sobre analítica académica y toma de decisiones, publicadas en un total de 800 revistas, los autores se concentran en Estados Unidos, Irán y Reino-Unido. Autores como Salas-Rueda, Ricardo, Sheikhbardsiri, Hojjat y Farokhzadian Jamileh, son los que tienen mayor número de publicaciones. Dos son los enfoques que prevalecen en las investigaciones estudiadas: el orientado en el usuario (estudiante), y el orientado a la gestión educativa. Para el primero, el análisis del comportamiento, percepciones, autorregulación, modelos predictivos y monitoreo de trayectorias, son los principales objetos de interés y, para el segundo, el uso de tecnologías, y análisis de un gran volumen de datos, es lo más prevalente, tanto para la toma de decisiones, como para planes de mejoramiento y gestión.Decision-making in higher education is increasingly evidence-based. The use of predictive models, modeling and technological mediations to analyze data and monitor learning paths is recurrent. Through a bibliometric study with a database of 16,324 scientific articles refereed in Scopus, the current state of research on decision-making with academic analytics in higher education was analyzed. The refinement using Vosviewer and Bibliometrix tools, yielded 1515 articles on academic analytics and decision making, published in a total of 800 journals, whose authors are concentrated in the United States, Iran and the United Kingdom. Authors such as Salas-Rueda, Ricardo, Sheikhbardsiri, Hojjat and Farokhzadian Jamileh, are the ones with the highest number of publications. Two are the approaches that prevail in the studies studied: the user-oriented (student) and the educational management-oriented. For the first, the analysis of behavior, perceptions, self-regulation, predictive models and monitoring of trajectories, are the main objects of interest and, for the second, the use of technologies, and analysis of a large volume of data, is the most prevalent, both for decision-making, and for improvement and management plans12 páginasapplication/pdfspaPontificia Universidad Católica del EcuadorEcuadorDerechos reservados - Pontificia Universidad Católica del Ecuador Sede Ambato, 2021https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)http://purl.org/coar/access_right/c_abf2La analítica académica y la toma de decisiones en educación superior: Un estudio bibliométricoAcademic analytics and decision making in higher education: A bibliometric 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_970fb48d4fbd8a85Indicadores científicosScience indicatorsInnovación educativaAnalítica académicaEfectividad académicaToma de decisiónEducational innovationAcademic analyticsAcademic effectivenessDecision making13321223González-Jiménez, D. 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Un estudio bibliométrico.pdfLa analítica académica y la toma de decisiones en educación superior. Un estudio bibliométrico.pdfTexto archivo completo del artículo de revista, PDFapplication/pdf313563https://red.uao.edu.co/bitstreams/32f0b4e3-8d10-473e-9986-9ee867951e52/downloadabc18edb592d624f195df54adb18e418MD53TEXTLa analítica académica y la toma de decisiones en educación superior. Un estudio bibliométrico.pdf.txtLa analítica académica y la toma de decisiones en educación superior. Un estudio bibliométrico.pdf.txtExtracted texttext/plain42079https://red.uao.edu.co/bitstreams/40aa62c7-6caf-40d0-a9e6-a85c65e95a64/downloadff6ae7abfa7e3310b78ea092c172851fMD54THUMBNAILLa analítica académica y la toma de decisiones en educación superior. Un estudio bibliométrico.pdf.jpgLa analítica académica y la toma de decisiones en educación superior. Un estudio bibliométrico.pdf.jpgGenerated Thumbnailimage/jpeg12482https://red.uao.edu.co/bitstreams/6cf8f6a6-3476-4c0c-adac-a3017a157d80/downloadfbff220110b22bcc25b1aef8a82e35e6MD5510614/13973oai:red.uao.edu.co:10614/139732024-03-05 15:33:33.023https://creativecommons.org/licenses/by-nc-nd/4.0/Derechos reservados - Pontificia Universidad Católica del Ecuador Sede Ambato, 2021open.accesshttps://red.uao.edu.coRepositorio Digital Universidad Autonoma de Occidenterepositorio@uao.edu.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 |