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International Journal of Educational Technology in Higher Education
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  • Special Section: Learning Analytics: Intelligent Decision Support Systems for Learning Environments
  • Learning Analytics: Intelligent Decision Support Systems for Learning Environments
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Educational Data Mining and Learning Analytics: differences, similarities, and time evolution

Minería de datos educativos y análisis de datos sobre aprendizaje: diferencias, parecidos y evolución en el tiempo

International Journal of Educational Technology in Higher Education volume 12, pages 98–112 (2015)Cite this article

Abstract

Technological progress in recent decades has enabled people to learn in different ways. Universities now have more educational models to choose from, i.e., b-learning and e-learning. Despite the increasing opportunities for students and instructors, online learning also brings challenges due to the absence of direct human contact. Online environments allow the generation of large amounts of data related to learning/teaching processes, which offers the possibility of extracting valuable information that may be employed to improve students’ performance. In this paper, we aim to review the similarities and differences between Educational Data Mining and Learning Analytics, two relatively new and increasingly popular fields of research concerned with the collection, analysis, and interpretation of educational data. Their origins, goals, differences, similarities, time evolution, and challenges are addressed, as are their relationship with Big Data and MOOCs.

Resumen

El progreso tecnológico de las últimas décadas ha hecho posible una diversidad de formas de aprendizaje. Hoy en día las universidades ofrecen múltiples modelos de enseñanza entre los que poder elegir, por ejemplo aprendizaje mixto (b-learning) o aprendizaje electrónico. Aunque cada vez son más numerosas las oportunidades para alumnos y profesores, el aprendizaje en línea también plantea dificultades debidas a la falta de contacto humano directo. Los entornos en linea permiten generar grandes cantidades de datos relacionados con los procesos de enseñanza-aprendizaje, de los que se puede extraer una valiosa información que se puede usar para mejorar el desempeño del alumnado. En este trabajo queremos estudiar los parecidos y diferencias entre la minería de datos educativos y el análisis de datos sobre aprendizaje, dos campos de investigación relativamente nuevos y crecientemente populares relacionados con la recogida, el análisis y la interpretación de datos educativos. Trataremos su origen, objetivos, diferencias y parecidos, evolución en el tiempo y retos a los que se enfrentan, así como su relación con los macrodatos y los cursos en línea abiertos y masivos (MOOC).

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Authors and Affiliations

  1. Computer Science, Multimedia and Telecommunication Dept., Open University of Catalonia (UOC), 08018, Rambla del Poblenou 156, Barcelona, Spain

    Laura Calvet Liñán (PhD Student) & Ángel Alejandro Juan Pérez (Associate Professor of Operations Research)

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  1. Laura Calvet Liñán
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  2. Ángel Alejandro Juan Pérez
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Correspondence to Laura Calvet Liñán.

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Calvet Liñán, L., Juan Pérez, Á.A. Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. Int J Educ Technol High Educ 12, 98–112 (2015). https://doi.org/10.7238/rusc.v12i3.2515

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