Educational Data Mining

Peña-Ayala, Alejandro

doi:10.1007/978-3-319-02738-8

Cited by 30 publications

(2 citation statements)

References 157 publications

(216 reference statements)

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“…Por lo tanto, quedó definido que un promedio de calificación inferior a 75 o un porcentaje de materias aprobadas inferior a 0.8 son indicadores de un bajo rendimiento académico, y en consecuencia existe alta probabilidad de riesgo académico para el estudiante. Existen estudios en los que se adoptaron criterios similares para la determinación de perfiles de rendimiento académico (Baker et al, 2015;Martínez, Karanik, Giovannini y Pinto, 2015;Padua, 2019;Río-Jenaro, Calle, Martín y Robaina, 2018).…”

Section: Preprocesamientounclassified

“…Las problemáticas derivadas del bajo rendimiento académico han sido temas comunes de preocupación y estudio en el contexto universitario (Padua, 2019). El rendimiento académico, también llamado desempeño académico o rendimiento escolar, es un constructo complejo que implica el logro de objetivos de aprendizaje establecidos en cursos o programas de estudio y puede ser entendido como un nivel de conocimientos demostrado, lo que involucra procesos de evaluación (Lamas, 2015).…”

Section: Introductionunclassified

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Modelos predictivos de riesgo académico en carreras de computación con minería de datos educativos

Franco

Martínez

Domínguez

2021

Red

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Los problemas de bajo rendimiento académico y rezago son recurrentes en instituciones educativas de nivel superior, especialmente al inicio de los estudios universitarios. En el contexto local, análisis diagnósticos han mostrado altos índices de reprobación y bajo rendimiento académico. En este trabajo, se utilizaron datos sociodemográficos y resultados de exámenes de admisión de 415 alumnos de las carreras del área de computación de la Universidad Autónoma de Yucatán (México), inscritos entre 2016 y 2019. El objetivo es generar modelos predictivos de riesgo académico, empleando métodos de la minería de datos educativa, que sirvan como herramientas de detección temprana de condiciones de riesgo académico y faciliten el despliegue de estrategias de intervención educativa. Se siguieron las etapas del Proceso de Extracción de Conocimiento en Bases de Datos, concretamente, se aplicaron técnicas de clasificación para el análisis, obtención y validación de los modelos. Los resultados muestran que el mejor modelo corresponde al algoritmo LMT, con un valor de precisión de 75.42% y un 0.805 para el área bajo la curva ROC. Se logró identificar a los mejores atributos predictores, particularmente las pruebas del examen de ingreso a licenciatura fueron muy significativas. Se propone el desarrollo de herramientas informáticas para la detección precoz de riesgo académico y estrategias de intervención educativa oportuna. The problems of poor academic performance and lag are recurrent in higher-level educational institutions, especially at the beginning of university studies. The early detection of academic risk conditions enables the implementation of educational intervention measures to address factors of poor school performance, associated with the particular contexts of the students. The purpose of this study was to generate predictive models of academic risk, using educational data mining methods, specifically classification or prediction techniques, for the analysis, obtaining and validation of the models. The data used correspond to admission exam results and sociodemographic data of 415 students of the computer science majors at the Autonomous University of Yucatán (Mexico), enrolled between 2016 and 2019. The results show that the best model corresponding to the algorithm of LMT classification, with a precision value of 75.42% and 0.805 for the area under the ROC curve. It was possible to identify the best predictive attributes, particularly the bachelor entrance exam tests were very significant. The development of computer tools for the early detection of academic risk and strategies for timely educational intervention is proposed.

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Section: Preprocesamientounclassified

Section: Introductionunclassified

Modelos predictivos de riesgo académico en carreras de computación con minería de datos educativos

Franco

Martínez

Domínguez

2021

Red

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Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Peña-Ayala

2018

WIREs Data Min & Knowl

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Before the emergence of computer‐based educational systems (CBES) whose aims of providing teaching and learning experiences to hundreds even thousands of users, an explosion of information (e.g., students' log data) demands sophisticated methods to gather, analyze, and interpret learners' traces to regulate and enhance education. Thus, learning analytics (LA) arises as a knowledge discovery paradigm that provides valuable findings and facilitates stakeholders to understand the learning process and its implications. Therefore, a landscape of the LA nature, its underlying factors, and applications achieved is outlined in this paper according to a suggested LA Taxonomy that classifies the LA duty from a functional perspective. The aim is to provide an idea of the LA toil, its research lines, and trends to inspire the development of novel approaches for improving teaching and learning practices. Furthermore, the scope of this review covers recently published papers in prestigious journals and conferences, where the works dated from 2016 are summarized and those corresponding to 2014–2015 are cited according to the proposed LA taxonomy. A glimpse is sketched of LA, where underlying elements frame the field foundations to ground the approaches. Moreover, LA strengths, weaknesses, challenges, and risks are highlighted to advice how the LA arena could be enhanced and empowered. In addition, this review offers an insight of the recent LA labor, as well as motivates readers to enrich the LA achievements. This work promotes the LA practice giving an account of the job being achieved and reported in literature, as well as a reflection of the state‐of‐the‐art and an acumens vision to inspire future labor. This article is categorized under: Application Areas > Education and Learning Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction

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Using Process Mining Techniques to Discover Student’s Activities, Navigation Paths, and Behavior in LMS Moodle

Dolák

2019

Lecture Notes in Computer Science

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Educational Data Mining

Cited by 30 publications

References 157 publications

Modelos predictivos de riesgo académico en carreras de computación con minería de datos educativos

Modelos predictivos de riesgo académico en carreras de computación con minería de datos educativos

Learning analytics: A glance of evolution, status, and trends according to a proposed taxonomy

Using Process Mining Techniques to Discover Student’s Activities, Navigation Paths, and Behavior in LMS Moodle

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