Daniel Fleming, School of Sport, Exercise & Rehabilitation Sciences, University of Hul
Statistical literacy is defined as ‘people’s ability to interpret and critically evaluate statistical information, data-related arguments, or stochastic phenomena...’ (Gal, 2002, p.2). Recent work has spoken to a crisis in relation to the general population’s statistical literacy. Specifically, the COVID-19 pandemic highlighted the significant lack of knowledge citizens have when statistics and/or probability is involved (Muniz-Rodriguez et al., 2020). In a world where data is more prominent than ever – reports suggest that data benefited the UK economy by up to £241 billion between 2015 and 2020 alone (CEBR, 2016) - it seems appropriate that citizens of that world should have a strong foundation of statistical knowledge. However, research methods and statistics have historically been identified as the ‘worst’ courses that students take while studying at university (Hogg, 1991). This presents a challenge for instructors of research methods and/or statistics classes. One approach to enhance the experience and learning in this domain may be interactive or game-based learning with immediate feedback (Qian & Clark, 2016). These components have demonstrated both medium and large effect sizes when implemented in studies testing the efficacy of game-based learning on 21st Century skills included in Qian and Clark’s (2016) review of over 97 effect sizes across 29 studies.
With this in mind, and as an instructor of a level 5 module titled Research Methods, I have been developing and implementing an interactive and feedback offering ShinyApp to enhance student’s learning experiences in statistics-based sessions. While in the early stages of development, the app has modules in progress related to descriptive statistics and distributions, t-tests, linear regression, and multiple regression – electing to avoid analysis of variance modelling in line with recommendations from Huang (2020). The app is designed to allow students to manipulate input parameters such as sample size, effect size, and noise, and see the output update in real time in both APA table format and a visualization. Interactive prompts then guide students through tasks in each module (currently in development) and provide feedback on why their response was correct or incorrect, allowing them to refine their understanding of why their interpretation was appropriate or not. I hope to continue to develop this project and use it in my modules, as well as making it available to anyone who would like to use it in their classes across campus or across the academic community.
References
Centre for Economics and Business Research. (2016). The Value of Big Data and the Internet of Things to the UK Economy. https://www.sas.com/content/dam/SAS/en_gb/doc/analystreport/cebr-value-of-big-data.pdf
Gal, I. (2002). Adults’ Statistical Literacy: Meanings, Components, Responsibilities. International Statistical Review, 70(1), 1–25. https://doi.org/10.1111/j.1751-5823.2002.tb00336.x
Hogg, R. V. (1991). Statistical Education: Improvements are Badly Needed. The American Statistician, 45(4), 342–343. https://doi.org/10.1080/00031305.1991.10475832
Huang, F. L. (2019). MANOVA: A Procedure Whose Time Has Passed? Gifted Child Quarterly, 64(1), 56–60. https://doi.org/10.1177/0016986219887200
Muñiz-Rodríguez, L., Rodríguez-Muñiz, L. J., & Alsina, Á. (2020). Deficits in the Statistical and Probabilistic Literacy of Citizens: Effects in a World in Crisis. Mathematics, 8(11), 1872. https://doi.org/10.3390/math8111872
Qian, M., & Clark, K. R. (2016). Game-based Learning and 21st century skills: A review of recent research. Computers in Human Behavior, 63(63), 50–58. https://doi.org/10.1016/j.chb.2016.05.023
Conference theme: Digital Collaboration