SOCR: Statistics Online Computational Resource.

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.

[1]  Paul M. Thompson,et al.  Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage , 2002, IEEE Transactions on Information Technology in Biomedicine.

[2]  D M Lane.  The Rice Virtual Lab in Statistics , 1999, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[3]  Elise Whitley,et al.  Statistics review 1: Presenting and summarising data , 2001, Critical care.

[4]  Paul F. Velleman.  Design principles for technology-based statistics education , 2000 .

[5]  R. Felder,et al.  Learning and Teaching Styles in Engineering Education. , 1988 .

[6]  Ross Taplin,et al.  Teaching Statistical Consulting Before Statistical Methodology , 2003 .

[7]  Nancy Berman,et al.  Comparison of means. , 2007, Methods in molecular biology.

[8]  Jonathan Ball,et al.  Statistics review 5: Comparison of means , 2002, Critical care.

[9]  Joel B. Greenhouse,et al.  Applying Cognitive Theory to Statistics Instruction , 2000 .

[10]  Ivo D. Dinov,et al.  Pedagogical utilization and assessment of the statistic online computational resource in introductory probability and statistics courses , 2008, Comput. Educ..

[11]  David R. Cox.  Some remarks on statistical education , 1998 .

[12]  J. Ball,et al.  Statistics review 6: Nonparametric methods , 2002, Critical care.

[13]  Yumi Asahi,et al.  A Statistical Data Representation System on the Web , 2002, Comput. Stat..

[14]  Andrew K. Chan,et al.  Encoding with frames in MRI and analysis of the signal-to-noise ratio , 2002, IEEE Transactions on Medical Imaging.

[15]  J Belcher,et al.  Nonparametric methods. , 2001, Nurse researcher.

[16]  Anne Hawkins,et al.  Discussion: Forward to Basics! A Personal View of Developments in Statistical Education , 1997 .

[17]  Jonathan Ball,et al.  Statistics review 2: Samples and populations , 2002, Critical care.

[18]  Elise Whitley,et al.  Statistics review 3: Hypothesis testing and P values , 2002, Critical care.

[19]  Joaquim Marques de Sá.  Presenting and Summarising the Data , 2003 .

[20]  Gerard Hanley,et al.  MERLOT: Multimedia Educational Resource for Learning and Online Teaching , 2003 .

[21]  M. Kramer,et al.  Hypothesis Testing and P Values , 1988 .