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.

References in zbMATH (referenced in 12 articles , 1 standard article )

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  1. Baj, Pawel; Buxton, Oliver R. H.: Passive scalar dispersion in the near wake of a multi-scale array of rectangular cylinders (2019)
  2. Dinov, Ivo D.; Siegrist, Kyle; Pearl, Dennis K.; Kalinin, Alexandr; Christou, Nicolas: Probability \textitDistributome: a web computational infrastructure for exploring the properties, interrelations, and applications of probability distributions (2016)
  3. Boyle, Elizabeth A.; MacArthur, Ewan W.; Connolly, Thomas M.; Hainey, Thomas; Manea, Madalina; Kärki, Anne; van Rosmalen, Peter: A narrative literature review of games, animations and simulations to teach research methods and statistics (2014) MathEduc
  4. Visaya, Maria Vivien; Sherwell, David: Dynamics from multivariable longitudinal data (2014)
  5. Dinov, Ivo D.; Christou, Nicolas: Web-based tools for modelling and analysis of multivariate data: California ozone pollution activity (2011) MathEduc
  6. Al-Aziz, Jameel; Christou, Nicolas; Dinov, Ivo D.: SOCR ”motion charts”: an efficient, open-source, interactive and dynamic applet for visualizing longitudinal multivariate data (2010) MathEduc
  7. González, Jose A.; Jover, Lluis; Cobo, Erik; Muñoz, Pilar: A web-based learning tool improves student performance in statistics: A randomized masked trial (2010) MathEduc
  8. Annie Chu; Jenny Cui; Ivo Dinov: SOCR Analyses: Implementation and Demonstration of a New Graphical Statistics Educational Toolkit (2009) not zbMATH
  9. Dinov, Ivo D.; Christou, Nicolas: Statistics Online Computational Resource for education (2009) MathEduc
  10. Dinov, Ivo D.; Christou, Nicholas; Sanchez, Juana: Central limit theorem: New SOCR applet and demonstration activity (2008) MathEduc
  11. Dinov, Ivo D.; Sanchez, Juana; Christou, Nicolas: Pedagogical utilization and assessment of the statistic online computational resource in introductory probability and statistics courses (2008) MathEduc
  12. Ivo Dinov: SOCR: Statistics Online Computational Resource (2006) not zbMATH