A Multivariate Analysis with MANOVA-Biplot of Learning Approaches in Health Science Students

Abstract

[EN] The acquisition of new knowledge by students represents a significant area of interest for universities, which seek to facilitate this process to enhance educational experience. There are two principal categories of learning approaches: surface and deep. The prevalence of a particular approach is contingent upon a number of individual and contextual factors. The aim of this study is to determine whether there are discernible differences in learning styles based on the geographical area of origin of the student. To this end, a multivariate analysis will be employed to compare the predominant learning approaches of health science university students using the Biggs R-SPQ-2F scale. A sample of 464 students was subjected to a multivariate analysis, specifically a Manova-Biplot, with the objective of facilitating the graphical representation of the relationships between the two learning approaches. A confirmatory factor analysis was conducted on the sample to corroborate the factor structure of the R-SPQ-2F. The findings indicated that the majority of students demonstrated proclivity towards deep learning, although their profiles exhibited heterogeneity related to their geographical context. The results may prove valuable in the characterization of the predominant learning approaches in a university community and the design of teaching strategies.