Dear Colleagues/Members:
According to Kolb's learning theory, instructors' learning styles (including experiences and material that they perceive as having been instrumental in their understanding of statistics) are likely to shape their teaching of this course. About a month ago, I surveyed members of this forum to ascertain which course(s) during their training contributed mostly to their understanding and use of statistics. There were 15 (fifteen) responses (some of them quite detailed and informative). All courses reported were described as advanced or graduate level, and based on thematic analysis, were labeled as: application-based, math, multivariate, probability, and research. Six (40%) respondents reported strong negative feelings about their earlier and introductory statistics courses at the undergraduate level, and also noted that application-based courses were pivotal to their understanding of statistics. Most of the respondents identified themselves as college/university instructors, and a few as statistician/data analyst/research analyst.
Here is the distribution of courses which contributed mostly to respondents’ understanding and application of statistics.
N= 15
Application-based (6)
Math (3)
Multivariate (2)
Probability (2)
Research (2)
Application-based + Research = 8/15
Application-Based = (actuarial science, biometry/ecology, economics, psychology, thesis/dissertation, and SPSS)
I would hesitate to readily group (math, probability and multivariate) as they may be stimuli for different types of reasoning. In particular, multivariate statistics, albeit mathematically intensive at times, is more representative (than bivariate analysis) of real-life phenomena and situations, and may therefore be more effective in facilitating conceptual understanding and deep learning.
It would be interesting to find out what “type” of understanding and reasoning was engendered by each classification of courses, and the resultant pedagogical approach, and student performance.
Most importantly, the delayed understanding of statistics is cause for concern. For all respondents, this understanding came with an advanced or graduate level course.
Notwithstanding the methodological limitations, these findings are plausible and should be further explored.
I want to thank you all for your participation and support.
Your feedback will be greatly appreciated.
Regards,
R. Hassad
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