Tipologías de estudiantes de Fisioterapia según el uso que hacen del campus virtual

  1. Mª Pilar Álvarez Vázquez
  2. Ana Álvarez-Méndez
  3. Carmen Bravo-Llatas
  4. Jesús Cristóbal Barrios
  5. Mª Teresa Angulo Carrere
Journal:
Revista d'innovació docent universitària: RIDU

ISSN: 2013-2298

Year of publication: 2020

Issue: 12

Pages: 74-81

Type: Article

DOI: 10.1344/RIDU2020.12.8 DIALNET GOOGLE SCHOLAR lock_openOpen access editor

More publications in: Revista d'innovació docent universitària: RIDU

Abstract

In this article we present the results obtained after processing the logs of the virtual space created for the subject “Human Anatomy III” (Physiotherapy Degree) in the Moodle platform. The analysis was performed using free software for statistical computing and RStudio graphics, an integrated development environment for R. A total of 19,611 logs, corresponding to the activity recorded in the 2017-2018 academic year were extracted, debugged and anonymized, to be analysed. The quantitative variables analysed were: the total number of visits to the virtualized course, the average of visits by weekday, by hours of the day and along the quarter, as well as the number of accesses to resources, self-assessments and URLs. In addition, the statistical analysis of the data was performed with the IBM SPSS v.25 software, analysing the relationship between the use of the virtual campus and academic performance. Non-parametric Spearman correlation tests and decision trees with two cut criteria were performed. The results obtained showed that the academic performance of the students of this subject is determined by their use of the virtual campus. Thus, it has been observed that students who failed (grades below 5 out of 10) had less activity on the Moodle platform, in all the variables analysed. By contrast, students with higher marks (grades between 8 and 10 out of 10) showed a significantly higher activity in the virtual space, especially in the number of visits and in the resources used.

Bibliographic References

  • Álvarez, M.P., Álvarez-Méndez, A., Angulo, M.T., Cristóbal, J., Bravo-Llatas, M.C. (2020) Learning analytics in human histology reveals different student clusters and different academic performance. Proceedings of INTED 2020, 14th International Technology, Education and Development Conference, en prensa.
  • Angulo, M.T., Álvarez-Méndez, A. (2007) CD-ROM interactivo para valoración biomecánica de la extremidad inferior. III Jornada Campus Virtual UCM: Innovación en el Campus Virtual: metodologías y herramientas. Editorial Complutense, Madrid, pp. 299-300.
  • Brown, M. (2011) Learning Analytics: the coming third wave. EDUCAUSE Learning Initiative. https://library.educause.edu/-/media/files/library/2011/4/elib1101-pdf.pdf
  • Cerezo, R., Sánchez-Santillán, M., Puerto, M., Núñez, J.C. (2016) Students’ LMS intereaction patterns and their relationship with achievement: A case study in higher education. Computers & Education, 96, pp. 42-54.
  • Chaparro, J., Iglesias, S., Pascual, F. (2010) Uso del registro de actividad de Moodle para un estudio del rendimiento académico de alumnos en entornos en línea y presencial. 4th International Conference on Industrial Engineering and Industrial Management XIV Congreso de Ingeniería de Organización, pp. 753-760. Donostia-San Sebastián.
  • Domínguez, D., Álvarez, J., Gil-Jaurena, I. (2016) Analítica del aprendizaje y Big Data: heurísticas y marcos interpretativos. DILEMATA, International Journal of Applied Ethics, 22, pp. 87-103.
  • Jenaro, C., Castaño, R., Martín, M.E., Flores, N. (2018) Rendimiento académico en educación superior y su asociación con la participación activa en la plataforma Moodle. Estudios Sobre Educación, 34, pp. 177-198.
  • Kotsiantis, S., Telios, N., Filippidi, A., Komis, V. (2013) Using learning analytics to identify successful learners in a blended learning course. International Journal of Technology Enhanced Learning, 5(2), pp. 133-150.
  • Konstantinidis, A., Grafton, C. (2013) Using Excel Macros to Analyse Moodle Logs. Conference Proceedings. 2nd Moodle Research Conference, pp. 33-39. Sousse, Tunisia.
  • Lee, J.E., Recker, M.M., Choi, H., Hong, W.J., Kim, N.J., Lee, K., Lefler, M., Louviere, J., Walker, A. (2016) Applying Data Mining Methods to Understand User Interactions within Learning Management Systems: Approaches and Lessons Learned. Journal of Educational Technology Development & Exchange, 8(2), pp. 99-116.
  • Macfadyen, L.P., Dawson, S. (2010) Mining LMS data to develop an “earlywarning system” for educators: A proof of concept. Computers & Education, 54(2), pp. 588-599.
  • Mwalumbwe, I., Mtebe, J. (2017) Using Learning Analytics to predictt students performance in Moodle Learning Management System: A case of Meya University of Science and Technology. The Electronic Journal of Information Systems in Developing Countries (EJISDC), 79(1), pp. 1-13.
  • Rossetti, S.R., Verdugo, M.L., Bayliss, D. (2017) Learning Analitics para determinar la realción entre uso de un Learning Management System y rendimiento académico. XXII Congreso Internacional de Contaduría, Administración e Informática. Ciudad de Méjico.
  • Shahiri, A.M., Husain, W., Rashid, N.A. (2015) The Third Information Systems International Conference. Procedia Computer Science, 72, pp. 414-422.
  • Sin, K., Muthu, L. (2015) Application of Big Data in education data mining and Learning Analitics. A literature review. Ictact Journal on Soft Computing: Special Issue on Soft Computing Model and Big Data Models for Big Data, 5(4), pp. 1035-1049.
  • Torres-Porras, J., Alcántara, J., Rubio, S. (2018) Virtual platforms use: a useful monitoring tool. EDMETIC. Revista de Educación Mediática y TIC, 7(1), pp. 242-255.
  • UCM (2019) Guía docente de la asignatura Anatomía Humana III, Grado en Fisioterapia. Recuperado de: https://enfermeria.ucm.es/data/cont/media/www/pag-128986/Anatom%C3%ADa%20Humana%20III.pdf