<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The paper aims to explore the relation between students’ Moodle activity during different semester phases, measured through the total number of logs and quiz logs, and their final test score. The study takes a quantitative approach to the analysis: Spearman’s rank test is used to identify the presence and direction of correlation between variables in a sample of 53 observations, and linear regression is performed to probe the strength of this relation. The results show that there is a similar significant moderate correlation for the first and the second month of the formal instruction period and no significant correlation for the examination period. The outcomes of linear regression are statistically significant and suggest that Moodle activity can account for about 25% of the variation in the final score. Both students’ quiz activity during the formal instruction period and during the examination period are significantly moderately correlated with the final test score, which results in the suggestion that diverse interactive content that allows practicing and revising the material is of great importance in an effective e-learning course. A relatively robust moderate correlation between Moodle activity in the first month and the final test score suggests that educators can use Moodle statistics from as early as the first four weeks of instruction to identify procrastinating students and low achievers.</p>
</abstract>

The paper aims to explore the relation between students’ Moodle activity during different semester phases, measured through the total number of logs and quiz logs, and their final test score. The study takes a quantitative approach to the analysis: Spearman’s rank test is used to identify the presence and direction of correlation between variables in a sample of 53 observations, and linear regression is performed to probe the strength of this relation. The results show that there is a similar significant moderate correlation for the first and the second month of the formal instruction period and no significant correlation for the examination period. The outcomes of linear regression are statistically significant and suggest that Moodle activity can account for about 25% of the variation in the final score. Both students’ quiz activity during the formal instruction period and during the examination period are significantly moderately correlated with the final test score, which results in the suggestion that diverse interactive content that allows practicing and revising the material is of great importance in an effective e-learning course. A relatively robust moderate correlation between Moodle activity in the first month and the final test score suggests that educators can use Moodle statistics from as early as the first four weeks of instruction to identify procrastinating students and low achievers.

Relation between Moodle Activity and Student Performance in the Context of EFL Training in Higher Education

Journal of Language and Cultural Education

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The paper aims to explore the relation between students’ Moodle activity during different semester phases, measured through the total number of logs and quiz...

Relation between Moodle Activity and Student Performance in the Context of EFL Training in Higher Education

Published Online: Mar 01, 2023

Page range: 25 - 37

DOI: https://doi.org/10.2478/jolace-2022-0003

Keywords
e-learning, LMS Moodle, educational data mining, EFL, course performance

© 2022 Xenia Liashuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Relation between Moodle Activity and Student Performance in the Context of EFL Training in Higher Education

Published Online: Mar 01, 2023

Page range: 25 - 37

DOI: https://doi.org/10.2478/jolace-2022-0003

Keywordse-learning, LMS Moodle, educational data mining, EFL, course performance

© 2022 Xenia Liashuk, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
e-learning, LMS Moodle, educational data mining, EFL, course performance