Detection of at-risk students with Learning Analytics Techniques

Consuelo Saiz Manzanares, María; Marticorena Sánchez, Raúl; Arnaiz González, Álvar; del Camino Escolar Llamazares, María; Queiruga Dios, Miguel Ángel

doi:10.30552/ejihpe.v8i3.273

Open AccessArticle

Detection of at-risk students with Learning Analytics Techniques

by

María Consuelo Saiz Manzanares

^*

,

Raúl Marticorena Sánchez

,

Álvar Arnaiz González

,

María del Camino Escolar Llamazares

and

Miguel Ángel Queiruga Dios

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

Submission received: 25 August 2018 / Revised: 15 September 2018 / Accepted: 17 September 2018 / Published: 17 September 2018

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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

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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. https://doi.org/10.30552/ejihpe.v8i3.273

AMA Style

Consuelo Saiz Manzanares M, Marticorena Sánchez R, Arnaiz González Á, del Camino Escolar Llamazares M, Queiruga Dios MÁ. Detection of at-risk students with Learning Analytics Techniques. European Journal of Investigation in Health, Psychology and Education. 2018; 8(3):129-142. https://doi.org/10.30552/ejihpe.v8i3.273

Chicago/Turabian Style

Consuelo Saiz Manzanares, María, Raúl Marticorena Sánchez, Álvar Arnaiz González, María del Camino Escolar Llamazares, and Miguel Ángel Queiruga Dios. 2018. "Detection of at-risk students with Learning Analytics Techniques" European Journal of Investigation in Health, Psychology and Education 8, no. 3: 129-142. https://doi.org/10.30552/ejihpe.v8i3.273

APA Style

Consuelo Saiz Manzanares, M., Marticorena Sánchez, R., Arnaiz González, Á., del Camino Escolar Llamazares, M., & Queiruga Dios, M. Á. (2018). Detection of at-risk students with Learning Analytics Techniques. European Journal of Investigation in Health, Psychology and Education, 8(3), 129-142. https://doi.org/10.30552/ejihpe.v8i3.273

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