Learning Analytics and Higher Music EducationPerspectives and Challenges

  1. Lorenzo de Reizábal, Margarita 1
  2. Benito Gómez, Manuel 2
  1. 1 Centro Superior de Música del País Vasco- Musikene
  2. 2 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Journal:
Artseduca

ISSN: 2254-0709

Year of publication: 2023

Issue: 34

Pages: 219-228

Type: Article

DOI: 10.6035/ARTSEDUCA.6831 DIALNET GOOGLE SCHOLAR lock_openDialnet editor

More publications in: Artseduca

Abstract

Continually monitoring student learning, improving tutoring, predicting academic risks such as performance drops or dropouts, assessing more objectively or understanding the behavior of student groups are some of the tasks that have been beyond the reach of music teachers. The current technology of massive data processing (Big Data) and its analysis (Learning Analytics-LA) allows to achieve these goals with relative ease. The possibility of extracting individual behavior patterns facilitates attention to diversity, reduces school dropout and failure, and opens the possibility of implementing new educational strategies. The phenomenon of data-based education has led to different types of studies. This paper reflects on three trends or fundamental perspectives in the use of the collection of massive information applied to learning and teaching. We offer an overview of research and applications of Learning Analytics specifically in the field of music education, as well as a reflection on its possible practical uses in higher music education in conservatories. For this purpose, we discuss some practical examples of how this technological methodology could be incorporated into music and music education research, and its influence on possible new educational paradigms that lead to innovation on teaching-learning process through new technological resources.

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