Abstract
The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student's intuition and enhance their learning.
Highlights
Statistical analyses commonly involve a problem identification and description, data design and acquisition, theoretical model development, manual or automated data analysis and results interpretation (Whitley and Ball 2002a,b,c,d,e)
Most of the statistical pedagogical approaches follow a similar design with an emphasis on statistical thinking and practical as pects of data analysis (Lovett and Greenhouse 2000; Taplin 2003)
The latest recommendations of many international pedagogical resources in probability and statistics (e.g., Dear et al 2005; Snell et al 2004, American Statistical Association, http://www.amstat.org/) suggest that undergraduate students taking probability and statistics courses should be exposed to real-world problems and be given hands-on experiences in generating, collecting and displaying data, as well as trained in model-design, analysis and result interpretation (Hawkins 1997; Teugels 1997; Cox 1998; Taplin 2003)
Summary
Statistical analyses commonly involve a problem identification and description, data design and acquisition, theoretical model development, manual or automated data analysis and results interpretation (Whitley and Ball 2002a,b,c,d,e). The latest recommendations of many international pedagogical resources in probability and statistics (e.g., Dear et al 2005; Snell et al 2004, American Statistical Association, http://www.amstat.org/) suggest that undergraduate students taking probability and statistics courses should be exposed to real-world problems and be given hands-on experiences in generating, collecting and displaying data, as well as trained in model-design, analysis and result interpretation (Hawkins 1997; Teugels 1997; Cox 1998; Taplin 2003) To address these necessities and improve content delivery in undergraduate statistics and probability courses we have built a dynamic collection of interactive online displays, simulations, games, tutorials, presentations, datasets and other resources
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