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

Revista:
Artseduca

ISSN: 2254-0709

Año de publicación: 2023

Número: 34

Páginas: 219-228

Tipo: Artículo

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

Otras publicaciones en: Artseduca

Resumen

Monitorizar continuamente el aprendizaje de los alumnos, mejorar las tutorías, predecir riesgos académicos como caídas o abandonos en el rendimiento, evaluar de forma más objetiva o comprender el comportamiento de los grupos de alumnos son algunas de las tareas que han estado fuera del alcance del profesorado de música. La tecnología actual de procesamiento masivo de datos (Big Data) y su análisis (Learning Analytics-LA) permite alcanzar estos objetivos con relativa facilidad. La posibilidad de extraer patrones de comportamiento individuales facilita la atención a la diversidad, reduce la deserción y el fracaso escolar y abre la posibilidad de implementar nuevas estrategias educativas. El fenómeno de la educación basada en datos ha dado lugar a diferentes tipos de estudios. Este trabajo reflexiona sobre tres tendencias o perspectivas fundamentales en el uso de la recopilación masiva de información aplicada al aprendizaje y la enseñanza. Ofrecemos una visión general de la investigación y las aplicaciones de Learning Analytics específicamente en el campo de la educación musical, así como una reflexión sobre sus posibles usos prácticos en la educación musical superior en los conservatorios. Para ello, discutimos algunos ejemplos prácticos de cómo esta metodología tecnológica podría incorporarse a la investigación musical y en educación musical, y su influencia en posibles nuevos paradigmas educativos que lleven a la innovación en el proceso de enseñanza-aprendizaje a través de nuevos recursos tecnológicos.

Referencias bibliográficas

  • Citas Algarni, A. (2016), Data Mining in Education. International Journal of Advanced Computer Science and Applications, 7(6), 456461.
  • Atkinson, S. (2015), Academia, https://www.academia.edu/12188434/Adaptive_Learning_and_Learning_Analytics_a_new_learning_design_paradigm
  • Baker, R. S. (2010). Data mining for education. International Encyclopedia of Education, 112-118.
  • Barocas, S., & Boyd, D. (2017), Engaging the ethics of data science in practice,
  • Communications of the ACM, 60(11), 23-25, https://doi.org/10.1145/3144172
  • Burrows, J., & Kumar, V. (2018), The Objective Ear: assessing the progress of a music task, Smart Learning Environments, 5(1), 1-13.
  • Casquero, O., Ovelar, R., Romo, J., Benito, M., & Alberdi, M. (2016), Students' personal networks in virtual and personal learning environments: a case study in higher education using learning analytics approach, Interactive Learning Environments, 24(1), 49-67.
  • Castro, F., Vellido, A., Nebot, A., & Mugica, F. (2007), Applying data mining techniques to e-learning problems, In Evolution of teaching and learning paradigms in intelligent environment, Berlin, Heidelberg: Springer.
  • Dollinger, M., & Lodge, J. M. (2018), Co-creation strategies for learning analytics,
  • Proceedings of the 8th International Conference on Learning Analytics and
  • Knowledge (pp. 97-101), https://doi.org/10.1145/3170358.3170372
  • Domínguez Figaredo, D., Reich, J., & Ruipérez-Valiente, J. A. (2020), Analítica del
  • aprendizaje y educación basada en datos: Un campo en expansión, RIED Revista Iberoamericana de Educación a Distancia, 23(2), 33-43. http://dx.doi.org/10.5944/ried.23.2.27105
  • Foster, E., & Siddle, R. (2019), The Effectiveness of Learning Analytics for Identifying at-Risk Students in Higher Education, Assessment & Evaluation in Higher Education, 1-13.
  • Garcia-Tinizaray, D., Ordonez-Briceno, K., & Torres-Diaz, C. (2014). Learning analytics to predict dropout of distance students. CAMPUS VIRTUALES, 3(1), 120-127.
  • Gedrimiene, E., Silvola, A., Pursiainen, J., Rusanen, J., & Muukkonen, H. (2019), Learning Analytics in Education: Literature Review and Case Examples From Vocational Education, Scandinavian Journal of Educational Research, 1-15.
  • Goldstein, P. J., & Katz, R. N. (2005), Academic analytics: The uses of management information and technology in higher education, Educause, 8, 1-12.
  • Guillot, C., Guillot, R., & Kumar, V. (2015), Enhancing Music Process through Analytics, M. Chang & F. Al-Shamali, 2015 Proceedings of Science and Technology Innovations (pp. 93-104), Athabasca, CA: Faculty of Science and Technology, Athabasca University.
  • Hartong, S., & Förschler, A. (2019), Opening the black box of data-based school monitoring: Data infrastructures, flows and practices in state education agencies, Big Data & Society, https://doi.org/10.1177/2053951719853311
  • Huang, A. Y., Lu, O. H., Huang, J. C., Yin, C. J., & Yang, S. J. (2019), Predicting Students’ Academic Performance by Using Educational Big Data and Learning Analytics: Evaluation of Classification Methods and Learning Logs, Interactive Learning Environments, 1-25.
  • Hwang, G.-J., & Chen, P.-Y. (2019), Effects of a Collective Problem-Solving Promotion-Based Flipped Classroom on Students’ Learning Performances and Interactive Patterns, Interactive Learning Environments, 1-16.
  • Kumar, K., & Kumar, V. (2018), Advancing learning through smart learning analytics: a review of case studies, Asian Association of Open Universities Journal, 13(1), 1-12.
  • Le, C. V., Pardos, Z. A., Meyer, S. D., Thorp, R. (2018). Communication at scale in a MOOC using predictive engagement analytics. In Mavrikis, M., Porayska- Pomsta, K., Luckin, R. (Eds.), Proceedings of the 19th International Conference on Artificial Intelligence in Education (pp. 239–252). Springer. https://doi.org/10.1007/978-3-319-93843-1_18
  • Leitner, P., Ebner, M., & Ebner, M. (2019), Learning Analytics Challenges to Overcome in Higher Education Institutions, Utilizing Learning Analytics to Support Study Success (pp. 91-104), Springer, Cham.
  • Mandinach, E. B., & Gummer, E. S. (2016), Data literacy for educators: Making it count in teacher preparation and practice, Teachers College Press.
  • Montgomery, A. P., Mousavi, A., Carbonaro, M., Hayward, D. V., & Dunn, W. (2019), Using learning analytics to explore self‐regulated learning in flipped blended learning music teacher education, British Journal of Educational Technology, 50(1), 114-127.
  • Oblinger, D. (2012), Let’s talk analytics, Educause Review, 47(4), 10-13. http://www.educause.edu/ecar
  • Shibani, A., Knight, S., & Buckingham Shum, S. (2020), Educator perspectives on
  • learning analytics in classroom practice, The Internet and Higher Education, 46, https://doi.org/10.1016/j.iheduc.2020.100730
  • Siemens, G. (2012), Learning analytics: Envisioning a research discipline and a domain of practice, 2nd International Conference on Learning Analytics and Knowledge, (pp. 4-8), Vancouver: ACM.
  • Strauss, V. (2019, September 11), Is New York State about to gut its student data privacy law?, Washington Post, https://washingtonpost.com/education/2019/09/11/is-new-york-state-about-gut- its-student-data-privacy-law/
  • Sun, F.-R., Hu, H.-Z., Wan, R.-G., Fu, X., & Wu, S.-J. (2019), A Learning Analytics Approach to Investigating Pre-Service Teachers’ Change of Concept of Engagement in the Flipped Classroom, Interactive Learning Environments, 1-16.
  • Tsai, Y. S., & Gasevic, D. (2017), Learning analytics in higher education challenges
  • and policies: a review of eight learning analytics policies, Proceedings of the
  • seventh international learning analytics & knowledge conference (pp. 233-242).
  • Whitehill, J., Williams, J. J., Lopez, G., Coleman, C. A., Reich, J. (2015). Beyond prediction: First steps toward automatic intervention in MOOC student stopout. In Proceedings of the 8th International Conference on Educational Data Mining, Madrid, Spain. International Educational Data Mining Society. http://dx.doi.org/10.2139/ssrn.2611750
  • Zhang, H., & Almeroth, K. (2010). Moodog: Tracking student activity in online course management systems. Journal of Interactive Learning Research, 21(3), 407-429.
Fuente de los datos: Dialnet