Evaluación de técnicas de minería de datos para la predicción del rendimiento académico

Leticia Laura Ochoa; Karina Rosas Paredes; César Baluarte Araya

doi:10.18687/LACCEI2017.1.1.368

Evaluación de técnicas de minería de datos para la predicción del rendimiento académico

Leticia Laura Ochoa, Karina Rosas Paredes, César Baluarte Araya

Escuela Profesional de Ingeniería de Sistemas

Universidad Nacional de San Agustín

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

Título traducido de la contribución	Evaluation of data mining techniques for the prediction of academic performance
Idioma original	Español
Título de la publicación alojada	15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology
Subtítulo de la publicación alojada	Global Partnership for Development and Engineering Education, LACCEI 2017
Editores	Maria M. Larrondo Petrie, Humberto Alvarez
Editorial	Latin American and Caribbean Consortium of Engineering Institutions
ISBN (versión digital)	9780999344309
DOI	https://doi.org/10.18687/LACCEI2017.1.1.368
Estado	Publicada - 2017
Evento	15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017 - Boca Raton, Estados Unidos Duración: 19 jul. 2017 → 21 jul. 2017

Serie de la publicación

Nombre	Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology
Volumen	2017-July
ISSN (versión digital)	2414-6390

Conferencia

Conferencia	15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017
País/Territorio	Estados Unidos
Ciudad	Boca Raton
Período	19/07/17 → 21/07/17

Acceder al documento

10.18687/LACCEI2017.1.1.368

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Ochoa, L. L., Rosas Paredes, K., & Baluarte Araya, C. (2017). Evaluación de técnicas de minería de datos para la predicción del rendimiento académico. En M. M. L. Petrie, & H. Alvarez (Eds.), 15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Global Partnership for Development and Engineering Education, LACCEI 2017 (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2017-July). Latin American and Caribbean Consortium of Engineering Institutions. https://doi.org/10.18687/LACCEI2017.1.1.368

Ochoa, Leticia Laura ; Rosas Paredes, Karina ; Baluarte Araya, César. / Evaluación de técnicas de minería de datos para la predicción del rendimiento académico. 15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Global Partnership for Development and Engineering Education, LACCEI 2017. editor / Maria M. Larrondo Petrie ; Humberto Alvarez. Latin American and Caribbean Consortium of Engineering Institutions, 2017. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology).

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booktitle = "15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology",

note = "15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017 ; Conference date: 19-07-2017 Through 21-07-2017",

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Ochoa, LL, Rosas Paredes, K & Baluarte Araya, C 2017, Evaluación de técnicas de minería de datos para la predicción del rendimiento académico. En MML Petrie & H Alvarez (eds.), 15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Global Partnership for Development and Engineering Education, LACCEI 2017. Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology, vol. 2017-July, Latin American and Caribbean Consortium of Engineering Institutions, 15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017, Boca Raton, Estados Unidos, 19/07/17. https://doi.org/10.18687/LACCEI2017.1.1.368

Evaluación de técnicas de minería de datos para la predicción del rendimiento académico. / Ochoa, Leticia Laura; Rosas Paredes, Karina; Baluarte Araya, César.
15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Global Partnership for Development and Engineering Education, LACCEI 2017. ed. / Maria M. Larrondo Petrie; Humberto Alvarez. Latin American and Caribbean Consortium of Engineering Institutions, 2017. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology; Vol. 2017-July).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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T1 - Evaluación de técnicas de minería de datos para la predicción del rendimiento académico

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AU - Baluarte Araya, César

PY - 2017

Y1 - 2017

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DO - 10.18687/LACCEI2017.1.1.368

M3 - Contribución a la conferencia

AN - SCOPUS:85046285882

T3 - Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology

BT - 15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology

A2 - Petrie, Maria M. Larrondo

A2 - Alvarez, Humberto

PB - Latin American and Caribbean Consortium of Engineering Institutions

T2 - 15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology, LACCEI 2017

Y2 - 19 July 2017 through 21 July 2017

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Ochoa LL, Rosas Paredes K, Baluarte Araya C. Evaluación de técnicas de minería de datos para la predicción del rendimiento académico. En Petrie MML, Alvarez H, editores, 15th LACCEI International Multi-Conference for Engineering, Education Caribbean Conference for Engineering and Technology: Global Partnership for Development and Engineering Education, LACCEI 2017. Latin American and Caribbean Consortium of Engineering Institutions. 2017. (Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology). doi: 10.18687/LACCEI2017.1.1.368