Next Article in Journal
Creativity, paranormal beliefs and cognitive impairment in the elderly
Previous Article in Journal
Structural and functional social support in elderly objective and subjective health ratings
Article Menu

Article Versions

Export Article

European Journal of Investigation in Health, Psychology and Education is published by MDPI from Volume 10 Issue 1 (2020). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with University Association of Education and Psychology.
Open AccessArticle

Detection of at-risk students with Learning Analytics Techniques

Facultad de Ciencias de la Salud. C/ Comendadores, s/n. C.P.: 09001. Burgos (España)
*
Author to whom correspondence should be addressed.
Eur. J. Investig. Health Psychol. Educ. 2018, 8(3), 129-142; https://doi.org/10.30552/ejihpe.v8i3.273 (registering DOI)
Received: 25 August 2018 / Revised: 15 September 2018 / Accepted: 17 September 2018 / Published: 17 September 2018
PDF [634 KB, uploaded 16 April 2019]

Abstract

The way of teaching and learning in twenty-first century society continues to change. At present, a high percentage of teaching takes place through Learning Management Systems that apply Learning Analytics Techniques. The use of these tools, among other things, facilitates knowledge of student learning patterns and the detection of at-risk students. The aim of this study is to establish the most effective learning patterns of the students on the platform in a hierarchical order of importance. It was conducted over two academic years with 122 students of Health Sciences. The instruments used were the Moodle v.3.1 platform and the analysis of logs with Machine Learning regression techniques. The results indicated that the Automatic Linear Prediction Model detected by order of importance: average visits per day, student self-assessment questionnaires, and teacher feedback. The percentage variance of the final results explained by these variables was 50.8%. Likewise, the effectiveness of the behavioral pattern explained 64.1% of the variance in those results, finding three clusters of effectiveness in the behavioral patterns that were detected.
Keywords: Learning Management System; learning analytics; automatic lineal model; at-risk students; university Learning Management System; learning analytics; automatic lineal model; at-risk students; university
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Consuelo Saiz Manzanares, M.; Marticorena Sánchez, R.; Arnaiz González, Á.; del Camino Escolar Llamazares, M.; Queiruga Dios, M.Á. Detection of at-risk students with Learning Analytics Techniques. Eur. J. Investig. Health Psychol. Educ. 2018, 8, 129-142.

Show more citation formats Show less citations formats

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Eur. J. Investig. Health Psychol. Educ. EISSN 2254-9625 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top