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Model for predicting academic performance through artificial intelligence

dc.creatorSilva, Jesús
dc.creatorRomero, Ligia
dc.creatorsolano, darwin
dc.creatorFernández, Claudia
dc.creatorPineda, Omar
dc.creatorRojas, Karina
dc.date.accessioned2020-11-12T21:10:43Z
dc.date.available2020-11-12T21:10:43Z
dc.date.issued2020
dc.identifier.issn2194-5357
dc.identifier.urihttps://hdl.handle.net/11323/7291
dc.description.abstractDuring the transit of students in the acquisition of competencies that allow them a good future development of their profession, they face the constant challenge of overcoming academic subjects. According to the learning theory, the probability of success of his studies is a multifactorial problem, with learning-teaching interaction being a transcendental element (Muñoz-Repiso and Gómez-Pablos in Edutec. Revista Electrónica de Tecnología Educativa 52: a291–a291 (2015), [1]. This research describes a predicative model of academic performance using neural network techniques on a real data set.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costaspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.sourceAdvances in Intelligent Systems and Computingspa
dc.subjectAcademic performancespa
dc.subjectBig dataspa
dc.subjectNeural networksspa
dc.subjectLearning analyticsspa
dc.titleModel for predicting academic performance through artificial intelligencespa
dc.typePreprintspa
dcterms.referencesMuñoz-Repiso AGV, Gómez-Pablos VB (2015) Evaluación de una experiencia de aprendizaje colaborativo con TIC desarrollada en un centro de Educación Primaria. Edutec. Revista Electrónica de Tecnología Educativa 51:a291–a291spa
dcterms.referencesFernández M, Valverde J (2014) Comunidades de práctica: un modelo de modelo de intervención desde el aprendizaje colaborativo en entornos virtuales. Revista Comunicar 42:97–105spa
dcterms.referencesVasquez C, Torres M, Viloria A (2017) Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J Eng Appl Sci 12(11):2963–2965spa
dcterms.referencesTorres-Samuel M, Vásquez C, Viloria A, Lis-Gutiérrez JP, Borrero TC, Varela N (2018) Web visibility profiles of Top100 Latin American universities. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham, pp 1–12spa
dcterms.referencesViloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching—learning process through knowledge data discovery (big data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham, pp 1–12spa
dcterms.referencesAbu A (2016) Educational data mining & students’ performance prediction. Int J Adv Comput Sci Appl (IJACSA), 212–220spa
dcterms.referencesDaud A, Radi N, Ayaz R, Lytras M, Abbas F (2017) Predicting student performance using advanced learning analytics. In: Proceedings of the 26th international conference on world wide web companion. WWW ‘17 Companion, Australia, pp 415–421spa
dcterms.referencesViloriaa A, Lezamab OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206spa
dcterms.referencesGonzález JC, Ramos S, Hernández S (2017) Modelo Difuso del Rendimiento Académico Bi-explicado. Revista de Sistemas y Gestión Educativa, 55–64spa
dcterms.referencesHamasa H, Indiradevi S, Kizhakkethottam J (2016) Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technol, 326–332spa
dcterms.referencesHu Y, Lo C, Shih S (2014) Developing early warning systems to predict students’ online learning performance. Comput Hum Behav, 469–478spa
dcterms.referencesHuang S, Fang N (2013) Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Comput Educ, 133–145spa
dcterms.referencesIl-Hyun J, Yeonjeong P, Jeonghyun K, Jongwoo S (2014) Analysis of online behavior and prediction of learning performance in blended learning environments. Educ Technol Int, 71–88spa
dcterms.referencesRojas P (2017) Learning analytics: a literature review. Educ Educ, 106–128spa
dcterms.referencesSchalk P, Wick D, Turner P, Ramsdell M (2011) Predictive assessment of student performance for early strategic guidance. In: Frontiers in education conference (FIE). Rapid City, Estados Unidos de Américaspa
dcterms.referencesUsman O, Adenubi A (2013) Artificial neural network (ANN) model for predicting students’ academic performance. J Sci Inf Technol, 23–37spa
dcterms.referencesYe C, Biswas G (2014) Early prediction of student dropout and performance in MOOCs using higher granularity temporal information. J Learn Analytics, 169–172spa
dcterms.referencesZacharis NZ (2016) Predicting student academic performance in blended learning using artificial neural networks. Int J Artif Intell Appl (IJAIA), 17–29spa
dcterms.referencesExpósito C (2018). Valores básicos del profesorado. Una aproximación desde el modelo axiológico de Shalom Schwartz. Educación y educadores. 307–325. Universidad de la sabana, Colombiaspa
dcterms.referencesFerrer J (2017) Labor docente del profesor principiante universitario: reto de la universidad en espacios globalizados. Ponencia presentada en jornadas científicas Dr. José Gregorio Hernández. Universidad Dr. José Gregorio Hernández. Venezuelaspa
dcterms.referencesFondón I, Madero M, Sarmiento A (2010) Principales problemas de los profesores principiantes en la enseñanza universitaria. En Formación universitaria 3(2):21–28spa
dcterms.referencesFontrodona J (2003) Ciencia y práctica en la acción directiva. Ediciones Rialp, Españaspa
dcterms.referencesGewerc A, Montero L, Lama M (2014) Colaboración y redes sociales en la enseñanza universitaria. Comunicar 42(21):55–63spa
dcterms.referencesGómez L, García C (2014) Las competencias sociales como dinamizadoras de la interacción y el aprendizaje colaborativo. Ediciones hispanoamericanas. Universidad nacional abierta y a distancia, Colombiaspa
dcterms.referencesGros B (2008) Aprendizaje, conexiones y artefactos de la producción colaborativa de conocimiento. Editorial Cedisa, Españaspa
dcterms.referencesHernández-Sellés N, González-Sanmamedy M, Muñoz-Carril PC (2015) El rol docente en las ecologías de aprendizaje: análisis de una experiencia de aprendizaje colaborativo en entornos virtuales. Profesorado. Revista de Currículum y Formación de Profesorado 19(2):147–163spa
dc.type.hasVersioninfo:eu-repo/semantics/draftspa
dc.source.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090094518&doi=10.1007%2f978-981-15-6876-3_41&partnerID=40&md5=8195fed2ff7082bc4a3977ff9b05616bspa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccessspa
dc.date.embargoEnd2021-01-31


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