Difference between revisions of "SOCR Events SloanMerlot 2010"
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===Logistics=== | ===Logistics=== | ||
* [http://sloanconsortium.org/et4online Joint Symposium of Sloan Consortium and MERLOT with MoodleMoot] focused on the technologies that drive online learning effectiveness, will continue to highlight research, applications, and best practices of important emerging technological tools. | * [http://sloanconsortium.org/et4online Joint Symposium of Sloan Consortium and MERLOT with MoodleMoot] focused on the technologies that drive online learning effectiveness, will continue to highlight research, applications, and best practices of important emerging technological tools. | ||
| − | * | + | * [http://www.sloanconsortium.org/et4online/technology-enhanced-probability-and-statistics-education-using-statistics-online-computati Session on Technology-enhanced Probability and statistics education using SOCR]. |
| − | + | * Organizers: [http://www.stat.ucla.edu/~nchristo Nicolas Christou], [http://www.stat.ucla.edu/~ryan.rosario/ Ryan Rosario], [http://www.stat.ucla.edu/~dinov Ivo Dinov]. | |
| + | * Date/Time: July 20-23, 2010. | ||
* Place: [http://www.fairmont.com/sanjose The Fairmont, San Jose, CA]. | * Place: [http://www.fairmont.com/sanjose The Fairmont, San Jose, CA]. | ||
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The focus of this session will be on novel Cutting Edge technologies for improving the AP, undergraduate and graduate probability and statistics education. The [http://www.socr.ucla.edu Statistics Online Computational Resource (www.SOCR.ucla.edu)] provides portable online aids for probability and statistics education, technology based instruction and statistical computing. Three complementary types of resources will be discussed and demonstrated via web-based hands-on activities – Motion Charts, Statistical Inference via Confidence Intervals, and Geographic Information Systems (GIS). Outlines of each of these topics and the discussion leaders are outlined below. | The focus of this session will be on novel Cutting Edge technologies for improving the AP, undergraduate and graduate probability and statistics education. The [http://www.socr.ucla.edu Statistics Online Computational Resource (www.SOCR.ucla.edu)] provides portable online aids for probability and statistics education, technology based instruction and statistical computing. Three complementary types of resources will be discussed and demonstrated via web-based hands-on activities – Motion Charts, Statistical Inference via Confidence Intervals, and Geographic Information Systems (GIS). Outlines of each of these topics and the discussion leaders are outlined below. | ||
| − | * We will demonstrate a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We will demonstrate this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at www.socr.ucla.edu/SOCR_MotionCharts. It can be used as instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. | + | * [[SOCR_MotionCharts |Motion Charts]]: We will demonstrate a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We will demonstrate this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at [http://www.socr.ucla.edu/SOCR_MotionCharts SOCR MotionCharts]. It can be used as instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis. |
| − | * Statistical inference based on interval estimates of parameters (confidence intervals) presents significant challenges in AP statistics and undergraduate probability and statistics education. There are 2 main challenges – comprehension of the rational for the interval-based inference, and the validation that the procedural calculation protocol and heuristic definition of the meaning of these interval estimates coincide. To address these 2 issues, we developed a pair of applets – one is a data-analysis applet, and one is a simulation applet – used to illustrate the above 2 challenges. An interactive hands-on learning activity is developed, validated and disseminated as an open, portable and freely available resource (www.socr.ucla.edu/htmls/exp/CI_Experiment.html) to the entire community. | + | * [[SOCR_EduMaterials_Activities_General_CI_Experiment |CI Simulations]]: Statistical inference based on interval estimates of parameters (confidence intervals) presents significant challenges in AP statistics and undergraduate probability and statistics education. There are 2 main challenges – comprehension of the rational for the interval-based inference, and the validation that the procedural calculation protocol and heuristic definition of the meaning of these interval estimates coincide. To address these 2 issues, we developed a pair of applets – one is a data-analysis applet, and one is a simulation applet – used to illustrate the above 2 challenges. An interactive hands-on learning activity is developed, validated and disseminated as an open, portable and freely available resource ([http://www.socr.ucla.edu/htmls/exp/CI_Experiment.html SOCR Confidence Interval experiment]) to the entire community. |
| − | * GIS | + | * ''GIS'': Spatial data are encountered in many disciplines and their visualization on geo-referenced maps enhances the analysis of the data. We will discuss examples on environmental spatial data using the new SOCR applet and R packages. |
<hr> | <hr> | ||
Latest revision as of 19:28, 20 July 2010
Contents
SOCR News - 2010 Emerging Technologies for Online Learning Symposium
Technology-Enhanced Probability and Statistics Education Using the Statistics Online Computational Resource
In this 1-hour special session presenting novel approaches for integrating technology and modern pedagogical ideas in different types of probability and statistics classes. The focus of this session will be on hands-on utilization of research-generated data, tools for statistical computing and Web-based learning resources and instructional materials.
Logistics
- Joint Symposium of Sloan Consortium and MERLOT with MoodleMoot focused on the technologies that drive online learning effectiveness, will continue to highlight research, applications, and best practices of important emerging technological tools.
- Session on Technology-enhanced Probability and statistics education using SOCR.
- Organizers: Nicolas Christou, Ryan Rosario, Ivo Dinov.
- Date/Time: July 20-23, 2010.
- Place: The Fairmont, San Jose, CA.
Abstract
The focus of this session will be on novel Cutting Edge technologies for improving the AP, undergraduate and graduate probability and statistics education. The Statistics Online Computational Resource (www.SOCR.ucla.edu) provides portable online aids for probability and statistics education, technology based instruction and statistical computing. Three complementary types of resources will be discussed and demonstrated via web-based hands-on activities – Motion Charts, Statistical Inference via Confidence Intervals, and Geographic Information Systems (GIS). Outlines of each of these topics and the discussion leaders are outlined below.
- Motion Charts: We will demonstrate a new Java-based infrastructure, SOCR Motion Charts, for discovery-based exploratory analysis of multivariate data. This interactive data visualization tool enables the visualization of high-dimensional longitudinal data. SOCR Motion Charts allows mapping of ordinal, nominal and quantitative variables onto time, 2D axes, size, colors, glyphs and appearance characteristics, which facilitates the interactive display of multidimensional data. We will demonstrate this new visualization paradigm using several publicly available multivariate datasets including Ice-Thickness, Housing Prices, Consumer Price Index, and California Ozone Data. SOCR Motion Charts is designed using object oriented programming, implemented as a Java Web-applet and is available to the entire community on the web at SOCR MotionCharts. It can be used as instructional tool for rendering and interrogating high-dimensional data in the classroom, as well as a research tool for exploratory data analysis.
- CI Simulations: Statistical inference based on interval estimates of parameters (confidence intervals) presents significant challenges in AP statistics and undergraduate probability and statistics education. There are 2 main challenges – comprehension of the rational for the interval-based inference, and the validation that the procedural calculation protocol and heuristic definition of the meaning of these interval estimates coincide. To address these 2 issues, we developed a pair of applets – one is a data-analysis applet, and one is a simulation applet – used to illustrate the above 2 challenges. An interactive hands-on learning activity is developed, validated and disseminated as an open, portable and freely available resource (SOCR Confidence Interval experiment) to the entire community.
- GIS: Spatial data are encountered in many disciplines and their visualization on geo-referenced maps enhances the analysis of the data. We will discuss examples on environmental spatial data using the new SOCR applet and R packages.
- SOCR Home page: http://www.socr.ucla.edu
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