Model for predicting academic performance through artificial intelligence
During 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,...
- Autores:
-
Silva, Jesús
Romero, Ligia
solano, darwin
Fernández, Claudia
Pineda, Omar
Rojas, Karina
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7291
- Acceso en línea:
- https://hdl.handle.net/11323/7291
https://repositorio.cuc.edu.co/
- Palabra clave:
- Academic performance
Big data
Neural networks
Learning analytics
- Rights
- closedAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Model for predicting academic performance through artificial intelligence |
| title |
Model for predicting academic performance through artificial intelligence |
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Model for predicting academic performance through artificial intelligence Academic performance Big data Neural networks Learning analytics |
| title_short |
Model for predicting academic performance through artificial intelligence |
| title_full |
Model for predicting academic performance through artificial intelligence |
| title_fullStr |
Model for predicting academic performance through artificial intelligence |
| title_full_unstemmed |
Model for predicting academic performance through artificial intelligence |
| title_sort |
Model for predicting academic performance through artificial intelligence |
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Silva, Jesús Romero, Ligia solano, darwin Fernández, Claudia Pineda, Omar Rojas, Karina |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Romero, Ligia solano, darwin Fernández, Claudia Pineda, Omar Rojas, Karina |
| dc.subject.spa.fl_str_mv |
Academic performance Big data Neural networks Learning analytics |
| topic |
Academic performance Big data Neural networks Learning analytics |
| description |
During 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. |
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2020 |
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2020-11-12T21:10:43Z |
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2020 |
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2021-01-31 |
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Corporación Universidad de la Costa |
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eng |
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Muñoz-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–a291 Fernández M, Valverde J (2014) Comunidades de práctica: un modelo de modelo de intervención desde el aprendizaje colaborativo en entornos virtuales. Revista Comunicar 42:97–105 Vasquez 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):2963–2965 Torres-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 1–12 Viloria 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 1–12 Abu A (2016) Educational data mining & students’ performance prediction. Int J Adv Comput Sci Appl (IJACSA), 212–220 Daud 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 415–421 Viloriaa A, Lezamab OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206 González JC, Ramos S, Hernández S (2017) Modelo Difuso del Rendimiento Académico Bi-explicado. Revista de Sistemas y Gestión Educativa, 55–64 Hamasa H, Indiradevi S, Kizhakkethottam J (2016) Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technol, 326–332 Hu Y, Lo C, Shih S (2014) Developing early warning systems to predict students’ online learning performance. Comput Hum Behav, 469–478 Huang S, Fang N (2013) Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Comput Educ, 133–145 Il-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, 71–88 Rojas P (2017) Learning analytics: a literature review. Educ Educ, 106–128 Schalk 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 América Usman O, Adenubi A (2013) Artificial neural network (ANN) model for predicting students’ academic performance. J Sci Inf Technol, 23–37 Ye C, Biswas G (2014) Early prediction of student dropout and performance in MOOCs using higher granularity temporal information. J Learn Analytics, 169–172 Zacharis NZ (2016) Predicting student academic performance in blended learning using artificial neural networks. Int J Artif Intell Appl (IJAIA), 17–29 Expósito C (2018). Valores básicos del profesorado. Una aproximación desde el modelo axiológico de Shalom Schwartz. Educación y educadores. 307–325. Universidad de la sabana, Colombia Ferrer 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. Venezuela Fondón I, Madero M, Sarmiento A (2010) Principales problemas de los profesores principiantes en la enseñanza universitaria. En Formación universitaria 3(2):21–28 Fontrodona J (2003) Ciencia y práctica en la acción directiva. Ediciones Rialp, España Gewerc A, Montero L, Lama M (2014) Colaboración y redes sociales en la enseñanza universitaria. Comunicar 42(21):55–63 Gómez L, García C (2014) Las competencias sociales como dinamizadoras de la interacción y el aprendizaje colaborativo. Ediciones hispanoamericanas. Universidad nacional abierta y a distancia, Colombia Gros B (2008) Aprendizaje, conexiones y artefactos de la producción colaborativa de conocimiento. Editorial Cedisa, España Hernández-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):147–163 |
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Silva, JesúsRomero, Ligiasolano, darwinFernández, ClaudiaPineda, OmarRojas, Karina2020-11-12T21:10:43Z2020-11-12T21:10:43Z20202021-01-312194-5357https://hdl.handle.net/11323/7291Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/During 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.Silva, JesúsRomero, Ligiasolano, darwin-will be generated-orcid-0000-0001-8996-0953-600Fernández, ClaudiaPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Rojas, Karinaapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbAdvances in Intelligent Systems and Computinghttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85090094518&doi=10.1007%2f978-981-15-6876-3_41&partnerID=40&md5=8195fed2ff7082bc4a3977ff9b05616bAcademic performanceBig dataNeural networksLearning analyticsModel for predicting academic performance through artificial intelligencePre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersionMuñoz-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–a291Fernández M, Valverde J (2014) Comunidades de práctica: un modelo de modelo de intervención desde el aprendizaje colaborativo en entornos virtuales. Revista Comunicar 42:97–105Vasquez 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):2963–2965Torres-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 1–12Viloria 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 1–12Abu A (2016) Educational data mining & students’ performance prediction. Int J Adv Comput Sci Appl (IJACSA), 212–220Daud 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 415–421Viloriaa A, Lezamab OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206González JC, Ramos S, Hernández S (2017) Modelo Difuso del Rendimiento Académico Bi-explicado. Revista de Sistemas y Gestión Educativa, 55–64Hamasa H, Indiradevi S, Kizhakkethottam J (2016) Student academic performance prediction model using decision tree and fuzzy genetic algorithm. Procedia Technol, 326–332Hu Y, Lo C, Shih S (2014) Developing early warning systems to predict students’ online learning performance. Comput Hum Behav, 469–478Huang S, Fang N (2013) Predicting student academic performance in an engineering dynamics course: a comparison of four types of predictive mathematical models. Comput Educ, 133–145Il-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, 71–88Rojas P (2017) Learning analytics: a literature review. Educ Educ, 106–128Schalk 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 AméricaUsman O, Adenubi A (2013) Artificial neural network (ANN) model for predicting students’ academic performance. J Sci Inf Technol, 23–37Ye C, Biswas G (2014) Early prediction of student dropout and performance in MOOCs using higher granularity temporal information. J Learn Analytics, 169–172Zacharis NZ (2016) Predicting student academic performance in blended learning using artificial neural networks. Int J Artif Intell Appl (IJAIA), 17–29Expósito C (2018). Valores básicos del profesorado. Una aproximación desde el modelo axiológico de Shalom Schwartz. Educación y educadores. 307–325. Universidad de la sabana, ColombiaFerrer 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. VenezuelaFondón I, Madero M, Sarmiento A (2010) Principales problemas de los profesores principiantes en la enseñanza universitaria. En Formación universitaria 3(2):21–28Fontrodona J (2003) Ciencia y práctica en la acción directiva. Ediciones Rialp, EspañaGewerc A, Montero L, Lama M (2014) Colaboración y redes sociales en la enseñanza universitaria. Comunicar 42(21):55–63Gómez L, García C (2014) Las competencias sociales como dinamizadoras de la interacción y el aprendizaje colaborativo. Ediciones hispanoamericanas. Universidad nacional abierta y a distancia, ColombiaGros B (2008) Aprendizaje, conexiones y artefactos de la producción colaborativa de conocimiento. Editorial Cedisa, EspañaHernández-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. 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