Table of Contents

1. About DataShop
2. Getting Data In
   1. Data Logging
   2. Data Import
3. Accessing DataShop
4. Glossary
5. Reports
   1. Dataset Info
      1. Overview
      2. Papers and Files
      3. KC Models
      4. Problem Breakdown
   2. Learning Curve
      1. Step Rollup
      2. LFA Values
   3. Error Report
   4. Performance Profiler
6. Filtering Data
   1. By student, KC, and problem
   2. Sample Selector
7. Export: Getting Data Out
   1. Excel PivotTables
8. Examples
9. Contact Us

About DataShop

The Pittsburgh Science of Learning Center (PSLC) DataShop is a data repository and web application for learning science researchers. It provides secure data storage as well as an array of analysis and visualization tools available through a web-based interface. Primarily, DataShop stores learner interactions from online course materials that include intelligent tutors. Data is collected from the seven PSLC courses: Algebra, Chemistry, Chinese, English, French, Geometry and Physics. There are also sources external to the PSLC that contribute to DataShop, such as middle school math data from the Assistments project (http://www.assistment.org).

DataShop can store any type of data associated with a course or study. This includes intelligent tutor data (which is capable of being analyzed through the analysis and visualization tools) as well as any related publications, files, presentations, or electronic artifacts a researcher would like to store. Courses and studies are represented as datasets, which are organized by project. An example of this is the “Algebra 1 2005-2006” dataset, which is grouped with similar datasets under the “Algebra Course” project. Semantic information can be stored for each dataset, providing additional context.

The amount of data in DataShop is constantly growing. The majority of datasets contain tutor interaction data, while others are files-only datasets, used solely for central and secure file storage. As of March 2008 there are about 8,700,000 tutor transactions, representing about 45,000 student hours available for analysis. Researchers have utilized DataShop to explore learning issues in a variety of educational domains. These include, but are not limited to, collaborative problem solving in Algebra (Rummel, Spada, Diziol, 2007), self-explanation in Physics (Hausmann & VanLehn , 2007), the effectiveness of worked examples and polite language in a Stoichiometry tutor (McLaren, Lim, Yaron, & Koedinger, 2007) and the optimization of knowledge component learning in Chinese (Pavlik, Presson, & Koedinger , 2007).

DataShop is designed to enable learning researchers to get off to a solid start analyzing their data.

To learn more about DataShop, explore the help pages here, start using the web application, or see our section on the PSLC website.