Open Access Peer-reviewed Research Article

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

Main Article Content

Dimitrios Kallivokas corresponding author

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

References

  1. Chance, B., & Rossman, A. (2006). Using simulation to teach and learn statistics. In Proceedings of the Seventh International Conference on Teaching Statistics (pp. 1-6). Voorburg, The Netherlands: International Statistical Institute.
  2. Dinov, I. D., Sanchez, J., & Christou, N. (2008). Pedagogical utilization and assessment of the statistic online computational resource in introductory probability and statistics courses. Computers & Education, 50(1), 284-300. https://doi.org/10.1016/j.compedu.2006.06.003
  3. Potter, G., Wong, J., Alcaraz, I., & Chi, P. (2016). Web application teaching tools for statistics using R and shiny. Technology Innovations in Statistics Education, 9(1). https://doi.org/10.5070/T591027492
  4. Fawcett, L. (2018). Using interactive shiny applications to facilitate research-informed learning and teaching. Journal of Statistics Education, 26(1), 2-16. https://doi.org/10.1080/10691898.2018.1436999
  5. Fessakis, G. (2019). Introduction to Digital Technology Applications in Education: From Information and Communication Technologies (ICT) to Digital Ability and Computational Thinking, Athens: Gutenberg, ISBN:978-960-01-1998-5.
  6. Garfield, J. (1995). How students learn statistics. International Statistical Review, 63(1),25-34. https://doi.org/10.2307/1403775
  7. Gialamas, V., Barkatsas, A., & Kasimati, K. (2006). Investigating the interaction between students' achievement and attitudes in the learning process of Mathematics using Information and Communication Technologies. Data Analysis Notebooks.
  8. Lee, C. D. (2014). Worksheet Usage, Reading Achievement, Classes' Lack of Readiness, and Science Achievement: A Cross-Country Comparison. International Journal of Education in Mathematics, Science and Technology, 2(2), 96-106. https://doi.org/10.18404/ijemst.38331
  9. Mills, D. J. (2002). Using Computer Simulation Methods to Teach Statistics: A Review of the Literature. Journal of Statistics Education, 10(1). https://doi.org/10.1080/10691898.2002.11910548
  10. Mills, J. D. (2004). Learning Abstract Statistics Concepts Using Simulation. Educational Research Quarterly, 28(4), 18-33.
  11. Rowell, G. H. (2004). Assessment of Using Technology for Teaching Statistics, in the ARTIST Roundtable Conference on Assessment in Statistics, Lawrence University.
  12. Sasmaz Oren, F., & Ormanci, U. (2012). An Application about Pre-Service Teachers' Development and Use of Worksheets and an Evaluation of Their Opinions about the Application. Educational Sciences: Theory and Practice, 12(1), 263-270.
  13. Schneiter, K. (2008). Two applets for teaching hypothesis testing. Journal of Statistics Education, 16(3). https://doi.org/10.1080/10691898.2008.11889575
  14. Lu, Y. (2022). Web-Based Applets for Facilitating Simulations and Generating Randomized Datasets for Teaching Statistics. Journal of Statistics and Data Science Education, 1-9. https://doi.org/10.1080/26939169.2022.2146614
  15. Jennings, M. J., Zumbo, B. D., & Joula, J. F. (2002). The robustness of validity and efficiency of the related samples t-test in the presence of outliers. Psicologica, 23(2), 425-450.