Impact of different course contents on working memory of elementary school students

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Abstract

Students tend to have poor learning efficiency when distracted by numerous internal and external factors in class. And there were many evidences demonstrate that students' attention plays a significant role in teaching. Therefore, this study aims to probe into working memory of elementary school students by administering three different courses, including math, physical education (PE), and athletics training courses, to three experimental groups. It compares the impact of the three courses on students' attention. In this study, purposive sampling was implemented to select 36 fifth graders from an elementary school in Taichung City, who were divided into three experimental groups. The data were analyzed based on the correctness of students' responses to attention tasks. The experimental test was employed using display duration of 0.3 and 0.4 seconds, respectively. A Wilcoxon matched-pairs signed-ranks test and Kruskal-Wallis one-way analysis of variance by ranks were conducted to identify the significance of the difference between the experimental groups (α=.05). When a substantial difference existed between experimental groups, the researcher implemented a post hoc comparison using a Mann-Whitney U test. The experimental results show that all three groups scored strikingly higher on the post-tests than on the pretests, reaching a significant different (p .05). The conclusions of this study were as follows: (1) the implementation of different courses has a significant and impact on the working memory of higher-grade elementary school students; (2) different course contents may influence working memory of students; (3) regular athletic training is helpful in enhancing student attention. © 2013 Springer-Verlag Berlin Heidelberg.

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APA

Hsu, T. Y., Lin, F. L., Chang, C. L., & Peng, H. T. (2013). Impact of different course contents on working memory of elementary school students. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8019 LNAI, pp. 316–324). Springer Verlag. https://doi.org/10.1007/978-3-642-39360-0_35

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