Using advanced learning technologies with university students: an analysis with machine learning techniques

  1. Marticorena-Sánchez, Raúl
  2. Ochoa-Orihuel, Javier
  3. Sáiz-Manzanares, María Consuelo
  1. 1 Universidad de Burgos
    info

    Universidad de Burgos

    Burgos, España

    ROR https://ror.org/049da5t36

Revista:
Electronics

ISSN: 2079-9292

Año de publicación: 2021

Volumen: 10

Número: 21

Páginas: 2620

Tipo: Artículo

DOI: 10.3390/ELECTRONICS10212620 GOOGLE SCHOLAR

Otras publicaciones en: Electronics

Resumen

The use of advanced learning technologies (ALT) techniques in learning management systems (LMS) allows teachers to enhance self-regulated learning and to carry out the personalized monitoring of their students throughout the teaching–learning process. However, the application of educational data mining (EDM) techniques, such as supervised and unsupervised machine learning, is required to interpret the results of the tracking logs in LMS. The objectives of this work were (1) to determine which of the ALT resources would be the best predictor and the best classifier of learning outcomes, behaviours in LMS, and student satisfaction with teaching; (2) to determine whether the groupings found in the clusters coincide with the students’ group of origin. We worked with a sample of third-year students completing Health Sciences degrees. The results indicate that the combination of ALT resources used predict 31% of learning outcomes, behaviours in the LMS, and student satisfaction. In addition, student access to automatic feedback was the best classifier. Finally, the degree of relationship between the source group and the found cluster was medium (C = 0.61). It is necessary to include ALT resources and the greater automation of EDM techniques in the LMS to facilitate their use by teachers.

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