Grades and Learning Styles: New Software for Analyzing Correlations in a Longitudinal Study Across Different Branches of Knowledge and Years

Knowing how students engage with their materials helps educators design methodologies to enhance the learning process. To help teachers in determining students’ average learning style profiles and analyzing potential connections between learning styles and academic performance, new freely accessible software, integrated with Moodle, has been developed in this work. A comprehensive case study involving one thousand students across various subjects and degrees from several branches of knowledge in different years has been conducted using this tool. The analysis reveals low participation in the survey, with only 6 out of 19 courses having a participation rate that surpasses 50%), where respondents generally achieved higher grades compared to non-participants (on a grade scale from 0 to 10, participants scored on average one point higher than those who did not complete the questionnaire), suggesting that they were more motivated. Higher-level courses have higher participation rates, but overall participation has declined over the years. Despite slight differences in learning styles between courses, the general pattern remains similar. The correlation between learning styles and grades, analyzed through linear regression, shows that this model performs better when both learning styles and grade marks are distributed by score rather than by level.

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Publication Details

Journal
Applied Sciences
Published
2024-12-20
DOI
https://doi.org/10.3390/app142411941
Primary Topic
Learning Styles and Cognitive Differences
Type
article
Field-Weighted Citation Impact
0.00

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article

Grades and Learning Styles: New Software for Analyzing Correlations in a Longitudinal Study Across Different Branches of Knowledge and Years

Miguel A. Molina‐Cabello, José Serrano-Angulo, Karl Thurnhofer‐Hemsi, Jesús Benito-Picazo
Applied Sciences
Learning Styles and Cognitive Differences
article

Grades and Learning Styles: New Software for Analyzing Correlations in a Longitudinal Study Across Different Branches of Knowledge and Years

Miguel A. Molina‐Cabello, José Serrano-Angulo, Karl Thurnhofer‐Hemsi, Jesús Benito-Picazo
article en

Abstract

Knowing how students engage with their materials helps educators design methodologies to enhance the learning process. To help teachers in determining students’ average learning style profiles and analyzing potential connections between learning styles and academic performance, new freely accessible software, integrated with Moodle, has been developed in this work. A comprehensive case study involving one thousand students across various subjects and degrees from several branches of knowledge in different years has been conducted using this tool. The analysis reveals low participation in the survey, with only 6 out of 19 courses having a participation rate that surpasses 50%), where respondents generally achieved higher grades compared to non-participants (on a grade scale from 0 to 10, participants scored on average one point higher than those who did not complete the questionnaire), suggesting that they were more motivated. Higher-level courses have higher participation rates, but overall participation has declined over the years. Despite slight differences in learning styles between courses, the general pattern remains similar. The correlation between learning styles and grades, analyzed through linear regression, shows that this model performs better when both learning styles and grade marks are distributed by score rather than by level.

Applied SciencesVol. 14(24)
Instituto de Investigación Biomédica de Málaga (ES), Universidad de Málaga (ES), Software (Spain) (ES)
Nvidia, Universidad de Málaga, Ministerio de Ciencia e Innovación, Smithsonian Conservation Biology Institute
Quality Education
Openalex Percentile: Top 70%
Learning Styles and Cognitive Differences
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