Model for predicting academic performance in virtual courses through supervised learning

Since virtual courses are asynchronous and non-presential environments, the following of student tasks can be a hard work. Virtual Education and Learning Environments (VELE) often provide tools for this purpose (Zaharia et al. in Commun ACM 59(11):56-65, 2016, [1]). In Moodle, some plugins take info...

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Autores:
Silva, Jesús
Garcia Cervantes, Evereldys
Cabrera, Danelys
García, Silvia
Binda, María Alejandra
Pineda Lezama, Omar Bonerge
Lamby Barrios, Juan Guillermo
Vargas Mercado, Carlos
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7710
Acceso en línea:
https://hdl.handle.net/11323/7710
https://doi.org/10.1007/978-981-15-7234-0_92
https://repositorio.cuc.edu.co/
Palabra clave:
Virtual education environments
Supervised learning
Moodle
Neural networks
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
id RCUC2_e23ab2b46def3df24ef21756f85b5c22
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7710
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Model for predicting academic performance in virtual courses through supervised learning
title Model for predicting academic performance in virtual courses through supervised learning
spellingShingle Model for predicting academic performance in virtual courses through supervised learning
Virtual education environments
Supervised learning
Moodle
Neural networks
title_short Model for predicting academic performance in virtual courses through supervised learning
title_full Model for predicting academic performance in virtual courses through supervised learning
title_fullStr Model for predicting academic performance in virtual courses through supervised learning
title_full_unstemmed Model for predicting academic performance in virtual courses through supervised learning
title_sort Model for predicting academic performance in virtual courses through supervised learning
dc.creator.fl_str_mv Silva, Jesús
Garcia Cervantes, Evereldys
Cabrera, Danelys
García, Silvia
Binda, María Alejandra
Pineda Lezama, Omar Bonerge
Lamby Barrios, Juan Guillermo
Vargas Mercado, Carlos
dc.contributor.author.spa.fl_str_mv Silva, Jesús
Garcia Cervantes, Evereldys
Cabrera, Danelys
García, Silvia
Binda, María Alejandra
Pineda Lezama, Omar Bonerge
Lamby Barrios, Juan Guillermo
Vargas Mercado, Carlos
dc.subject.spa.fl_str_mv Virtual education environments
Supervised learning
Moodle
Neural networks
topic Virtual education environments
Supervised learning
Moodle
Neural networks
description Since virtual courses are asynchronous and non-presential environments, the following of student tasks can be a hard work. Virtual Education and Learning Environments (VELE) often provide tools for this purpose (Zaharia et al. in Commun ACM 59(11):56-65, 2016, [1]). In Moodle, some plugins take information about students’ activities, providing statistics to the teacher. This information may not be accurate with respect to leadership ability or risk of abandonment. The use of artificial neural networks (ANNs) can help predict student behavior and draw conclusions at early stages of the learning process in a VELE. This paper proposes a plugin for Moodle that analyzes social metrics through graph theory. This article outlines the advantages of integrating an ANN into this development that complements the use of the graph to provide rich conclusions about student performance in a Moodle virtual course.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2021-01-18T20:48:16Z
dc.date.available.none.fl_str_mv 2021-01-18T20:48:16Z
dc.date.issued.none.fl_str_mv 2021
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-7234-0_92
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
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url https://hdl.handle.net/11323/7710
https://doi.org/10.1007/978-981-15-7234-0_92
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
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dc.relation.references.spa.fl_str_mv 1. Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin MJ, Ghodsi A, Gonzalez J, Shenker S, Stoica I (2016) Apache spark: a unified engine for big data processing. Commun ACM 59(11):56–65
2. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, VLDB, pp 487–499
3. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 55–60
4. Hahsler M, Karpienko R (2017) Visualizing association rules in hieralchical groups. J Bus Econ 87:317–335
5. Yuan M, Ouyang Y, Xiong Z, Sheng H (2013) Sentiment classification of web review using association rules. In: Ozok AA, Zaphiris P (eds) Online communities and social computing. OCSC 2013. Lecture notes in computer science, vol 8029. Springer, Berlin
6. Silverstein C, Brin S, Motwani R, Ullman J (2000) Scalable techniques for mining causal structures. Data Min Knowl Disc 4(2–3):163–192
7. Amelec V, Carmen V (2015) Relationship between variables of performance social and financial of microfinance institutions. Adv Sci Lett 21(6):1931–1934
8. Amelec V, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206
9. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data, Springer, Cham, pp 3–11
10. Cagliero L, Fiori A (2012) Analyzing Twitter user behaviors and topic trends by exploiting dynamic rules. In: Behavior computing: modeling, analysis, mining and decision. Springer, Berlin, pp 267–287
11. Erlandsson F, Bródka P, Borg A, Johnson H (2016) Finding influential users in social media using association rule learning. Entropy 18:16
12. Meduru M, Mahimkar A, Subramanian K, Padiya PY, Gunjgur PN (2017) Opinion mining using twitter feeds for political analysis. Int J Comput (IJC) 25(1):116–123
13. Abascal-Mena R, López-Ornelas E, Zepeda-Hernández JS (2013) User generated content: an analysis of user behavior by mining political tweets. In: Ozok AA, Zaphiris P (eds) Online communities and social computing. OCSC 2013. Lecture notes in computer science, vol 8029. Springer, Berlin
14. Dehkharghani R, Mercan H, Javeed A, Saygin Y (2014) Sentimental causal rule discovery from Twitter. Expert Syst Appl 41(10):4950–4958
15. Oladokun VO, Adebanjo AT, Charles-Owaba OE (2008) Predicting students’ academic performance using artificial neural network: a case study of an engineering course. Pac J Sci Technol 9(1):72–79
16. Finkel JR, Grenager T, Manning C (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43nd annual meeting of the association for computational linguistics (ACL 2005) pp 363–370
17. Halachev P (2012) Prediction of e-learning efficiency by neural networks. Cybern Inf Technol 12(2):98–108
18. Collins M (2002) Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP 2002), pp 1–8
19. Amelec V et al (2019) Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput Sci 155:575–580
20. Torres Samuel M, Vásquez C, Viloria A, Hernández Fernandez L, Portillo Medina R (2018) Analysis of patterns in the university Word Rankings Webometrics, Shangai, QS and SIRScimago: case Latin American. Lecture notes in computer science (Including subseries Lecture Notes in Artificial Intelligent and Lecture Notes in Bioinformatics)
21. Jacznik R, Tassara M, D’Uva I, Baldino G (2016) Herramienta de software pedagógica para identificar relaciones y comportamientos en entornos de educación virtual, CyTal
22. Lykourentzou I, Giannoukos I, Mpardis G, Nikolopoulos V, Loumos V (2009) Early and dynamic student achievement prediction in e-learning courses using neural networks. J Am Soc Inf Sci Technol 60(2):372–380
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dc.source.spa.fl_str_mv Advances in Intelligent Systems and Computing
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spelling Silva, JesúsGarcia Cervantes, EvereldysCabrera, DanelysGarcía, SilviaBinda, María AlejandraPineda Lezama, Omar BonergeLamby Barrios, Juan GuillermoVargas Mercado, Carlos2021-01-18T20:48:16Z2021-01-18T20:48:16Z2021https://hdl.handle.net/11323/7710https://doi.org/10.1007/978-981-15-7234-0_92Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Since virtual courses are asynchronous and non-presential environments, the following of student tasks can be a hard work. Virtual Education and Learning Environments (VELE) often provide tools for this purpose (Zaharia et al. in Commun ACM 59(11):56-65, 2016, [1]). In Moodle, some plugins take information about students’ activities, providing statistics to the teacher. This information may not be accurate with respect to leadership ability or risk of abandonment. The use of artificial neural networks (ANNs) can help predict student behavior and draw conclusions at early stages of the learning process in a VELE. This paper proposes a plugin for Moodle that analyzes social metrics through graph theory. This article outlines the advantages of integrating an ANN into this development that complements the use of the graph to provide rich conclusions about student performance in a Moodle virtual course.Silva, JesúsGarcia Cervantes, EvereldysCabrera, Danelys-will be generated-orcid-0000-0002-9486-9764-600García, SilviaBinda, María AlejandraPineda Lezama, Omar BonergeLamby Barrios, Juan Guillermo-will be generated-orcid-0000-0001-5358-0270-600Vargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Advances in Intelligent Systems and Computinghttps://link.springer.com/chapter/10.1007/978-981-15-7234-0_92Virtual education environmentsSupervised learningMoodleNeural networksModel for predicting academic performance in virtual courses through supervised learningArtículo 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/acceptedVersion1. Zaharia M, Xin RS, Wendell P, Das T, Armbrust M, Dave A, Meng X, Rosen J, Venkataraman S, Franklin MJ, Ghodsi A, Gonzalez J, Shenker S, Stoica I (2016) Apache spark: a unified engine for big data processing. Commun ACM 59(11):56–652. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th international conference on very large data bases, VLDB, pp 487–4993. Manning CD, Surdeanu M, Bauer J, Finkel J, Bethard SJ, McClosky D (2014) The Stanford CoreNLP natural language processing toolkit. In: Proceedings of the 52nd annual meeting of the association for computational linguistics: system demonstrations, pp 55–604. Hahsler M, Karpienko R (2017) Visualizing association rules in hieralchical groups. J Bus Econ 87:317–3355. Yuan M, Ouyang Y, Xiong Z, Sheng H (2013) Sentiment classification of web review using association rules. In: Ozok AA, Zaphiris P (eds) Online communities and social computing. OCSC 2013. Lecture notes in computer science, vol 8029. Springer, Berlin6. Silverstein C, Brin S, Motwani R, Ullman J (2000) Scalable techniques for mining causal structures. Data Min Knowl Disc 4(2–3):163–1927. Amelec V, Carmen V (2015) Relationship between variables of performance social and financial of microfinance institutions. Adv Sci Lett 21(6):1931–19348. Amelec V, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–12069. Kamatkar SJ, Kamble A, Viloria A, Hernández-Fernandez L, Cali EG (2018) Database performance tuning and query optimization. In: International conference on data mining and big data, Springer, Cham, pp 3–1110. Cagliero L, Fiori A (2012) Analyzing Twitter user behaviors and topic trends by exploiting dynamic rules. In: Behavior computing: modeling, analysis, mining and decision. Springer, Berlin, pp 267–28711. Erlandsson F, Bródka P, Borg A, Johnson H (2016) Finding influential users in social media using association rule learning. Entropy 18:1612. Meduru M, Mahimkar A, Subramanian K, Padiya PY, Gunjgur PN (2017) Opinion mining using twitter feeds for political analysis. Int J Comput (IJC) 25(1):116–12313. Abascal-Mena R, López-Ornelas E, Zepeda-Hernández JS (2013) User generated content: an analysis of user behavior by mining political tweets. In: Ozok AA, Zaphiris P (eds) Online communities and social computing. OCSC 2013. Lecture notes in computer science, vol 8029. Springer, Berlin14. Dehkharghani R, Mercan H, Javeed A, Saygin Y (2014) Sentimental causal rule discovery from Twitter. Expert Syst Appl 41(10):4950–495815. Oladokun VO, Adebanjo AT, Charles-Owaba OE (2008) Predicting students’ academic performance using artificial neural network: a case study of an engineering course. Pac J Sci Technol 9(1):72–7916. Finkel JR, Grenager T, Manning C (2005) Incorporating non-local information into information extraction systems by Gibbs sampling. In: Proceedings of the 43nd annual meeting of the association for computational linguistics (ACL 2005) pp 363–37017. Halachev P (2012) Prediction of e-learning efficiency by neural networks. Cybern Inf Technol 12(2):98–10818. Collins M (2002) Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms. In: Proceedings of the 2002 conference on empirical methods in natural language processing (EMNLP 2002), pp 1–819. Amelec V et al (2019) Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput Sci 155:575–58020. Torres Samuel M, Vásquez C, Viloria A, Hernández Fernandez L, Portillo Medina R (2018) Analysis of patterns in the university Word Rankings Webometrics, Shangai, QS and SIRScimago: case Latin American. Lecture notes in computer science (Including subseries Lecture Notes in Artificial Intelligent and Lecture Notes in Bioinformatics)21. Jacznik R, Tassara M, D’Uva I, Baldino G (2016) Herramienta de software pedagógica para identificar relaciones y comportamientos en entornos de educación virtual, CyTal22. Lykourentzou I, Giannoukos I, Mpardis G, Nikolopoulos V, Loumos V (2009) Early and dynamic student achievement prediction in e-learning courses using neural networks. J Am Soc Inf Sci Technol 60(2):372–380PublicationORIGINALModel for predicting academic performance in virtual courses through supervised learning.pdfModel for predicting academic performance in virtual courses through supervised learning.pdfapplication/pdf114252https://repositorio.cuc.edu.co/bitstreams/b62f53a0-fe6d-4f21-bf29-1e14bac83059/download002b0ad46fe01149205c982483555df6MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/c24cc657-9fe3-4a23-a9d8-a364e81b636c/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/04d299ff-1422-433a-aff8-07576b45d99e/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILModel for predicting academic performance in virtual courses through supervised learning.pdf.jpgModel for predicting academic performance in virtual courses through supervised 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