Open Access Peer-reviewed Research Article

Teaching basic statistic concepts to student classes with diverse mathematical background using specialized applets

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Independent samples test
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Submitted   May 14, 2023
Published   Aug 4, 2023
Issue    Vol 3 No 2 (2023)
DOI   10.25082/AMLER.2023.02.007

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Dimitrios Kallivokas corresponding author
Department of Business Administration, University of West Attica, Athens, Greece

Abstract

This study examines whether the use of specialized software applications can have an effective role in understanding and clarifying concepts of the basic measures of central tendency and dispersion, so that the advantages over traditional teaching are clear. Higher education students need to have a deep understanding of the concepts of theory of probability, descriptive statistics measures and statistical conclusions in order to be able to be used in their studies and in their work. For this purpose during an introductory course in Statistics at a University of Applied Sciences, two separate groups of students consisting of randomly selected members were given the same worksheet. One of these groups was additionally given specialized application software developed to visualize these concepts in order to answer the questions on the worksheet. The investigated case is whether the appropriate use of specialized software can help to effectively understand and interpret basic descriptive statistic concepts in a realistic application. The main conclusion is that appropriate specialized software applications can lead to the deepening of statistic concepts by students and in general promote the statistical literacy to a much greater extent than traditional teaching.

Keywords
statistics in higher education, statistics education, statistic literacy

Article Details

How to Cite
Kallivokas, D. (2023). Teaching basic statistic concepts to student classes with diverse mathematical background using specialized applets. Advances in Mobile Learning Educational Research, 3(2), 801-804. https://doi.org/10.25082/AMLER.2023.02.007
Creative Commons License

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

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