Dinov Ivo D
The Resource, Department of Statistics, 8125 Mathematical Science Bldg., University of California, Los Angeles, Los Angeles, CA 90095-1554, United States of America, Tel. +1/31/825-8430, /31/206-5658,
J Stat Softw. 2006 Oct 1;16(11). doi: 10.18637/jss.v016.i11.
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.
在过去十年中，本科和研究生阶段的统计学教育对实践计算机实验室经验的需求已得到明确确立。因此，人们进行了多次尝试，以开发问题驱动的统计思维、数据分析和结果解释的新方法。在本文中，我们描述了一个基于网络的综合教育框架，用于：交互式分布建模、虚拟在线概率实验、统计数据分析、可视化和集成。在使用如STATA、S-PLUS、R、SPSS、SAS、Systat等已获许可的统计软件包在各级学院进行多年统计教学的经验基础上，我们尝试设计一种新的统计教育环境，即统计在线计算资源（SOCR）。该资源执行许多标准类型的统计分析，与其他经典工具非常相似。此外，它采用插件式面向对象架构设计，完全独立于平台，基于网络，具有交互性、可扩展性和安全性。在过去四年中，我们在许多本科和研究生概率与统计课程中对SOCR框架进行了测试、微调并重新分析，有证据表明SOCR资源能够培养学生的直觉并增强他们的学习效果。
