A Case Study: Data Mining Applied to Student Enrollment

Daniel Victoria; Jhonny Estrella; Juan Pablo Peche; Álvaro Ortigosa; César Vialardi Sacín; Alfredo Barrientos Padilla; Jorge Chue Gallardo

A Case Study: Data Mining Applied to Student Enrollment

Daniel Victoria, Jhonny Estrella, Juan Pablo Peche, Álvaro Ortigosa, César Vialardi Sacín, Alfredo Barrientos Padilla, Jorge Chue Gallardo

Producción científica: Contribución a una revista › Artículo (Contribución a Revista) › revisión exhaustiva

9 Citas (Scopus)

Resumen

One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students’ past performance. The objective is to improve students’ performance in general, by reducing the rate of misguided enrollment decisions.

Idioma original	Inglés estadounidense
Publicación	Default journal
Estado	Publicada - 1 ene. 2010
Publicado de forma externa	Sí

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Categorías Repositorio Ulima

Ciencias sociales / Educación

Temas Repositorio Ulima

Data mining
Estudiantes universitarios
Minería de datos
Rendimiento académico

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title = "A Case Study: Data Mining Applied to Student Enrollment",

abstract = "One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students{\textquoteright} past performance. The objective is to improve students{\textquoteright} performance in general, by reducing the rate of misguided enrollment decisions.",

author = "Daniel Victoria and Jhonny Estrella and Peche, {Juan Pablo} and {\'A}lvaro Ortigosa and {Vialardi Sac{\'i}n}, C{\'e}sar and {Barrientos Padilla}, Alfredo and {Chue Gallardo}, Jorge",

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AU - Victoria, Daniel

AU - Estrella, Jhonny

AU - Peche, Juan Pablo

AU - Ortigosa, Álvaro

AU - Vialardi Sacín, César

AU - Barrientos Padilla, Alfredo

AU - Chue Gallardo, Jorge

PY - 2010/1/1

Y1 - 2010/1/1

N2 - One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students’ past performance. The objective is to improve students’ performance in general, by reducing the rate of misguided enrollment decisions.

AB - One of the main problems faced by university students is deciding the right learning path based on available information such as courses, schedules and professors. In this context, this paper presents a recommender system based on data mining. This recommender system intends to create awareness of the difficulty and amount of workload entailed by a chosen set of courses. For the purpose of building the underlying model, this paper describes the generation of domain specific variables that are capable of representing students’ past performance. The objective is to improve students’ performance in general, by reducing the rate of misguided enrollment decisions.

UR - https://hdl.handle.net/20.500.12724/1345

M3 - Article (Contribution to Journal)

JO - Default journal

JF - Default journal

ER -

A Case Study: Data Mining Applied to Student Enrollment

Resumen

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Categorías Repositorio Ulima

Temas Repositorio Ulima

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