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STATS@UIUC: Web Interface for Analytical Environments

 

Purpose

The rstudio subdomain for stat.illinois.edu is presently a testbed for providing online analytical environments. The current active project that the domain fosters is related to autograding technologies.

Active Services

There are currently two outside services that are active within this subdomain:

These services are currently restricted to students within STAT 385: Statistics Programming methods taught by James Joseph Balamuta and STAT 430: Basics of Statistical Learning taught by Dr. David Dalpiaz or faculty who wish to evaluate the use of the above services for teaching purposes.

Potential and Impact

The testbed serves as a means to not only explore online options for learning, but also represents a shift way from having students rely on personal computers. In the case of the autograde technology, the modeling building aspect is shifted from the student's machine to a remote server. This enables the ability for students to work with larger data sets than what is traditionally possible on consumer-grade hardware. Furthermore, the impact of enabling online platforms that provide autograding technology is particularly important as the number of students seeking to learn statistics is increasing exponentially. Not only does autograding technology decrease the amount of time necessary for a teaching assistant (TA) to grade homework, but also provides students with instant feedback and multiple attempts.

History

In Fall 2015, James Balamuta, a PhD Student within the Departments of Informatics and Statistics, deployed an autograding solution within STAT 429: Time Series Analysis taught by Prof. St├ęphane Guerrier. The autograding solution was used to evaluate the effectiveness of student models on a hidden test data set with results being displayed on an anonymized leaderboard. For more information, please see the autograde project page on GitHub. Since then there are been a few different courses that have inhabited the platform.

  • Fall 2015 STAT 429: Time Series Analysis taught by Prof. Stephane Guerrier
  • Spring 2016 STAT 430: Basics of Statistical Learning taught by Dr. David Dalpiaz
  • Summer 2016 STAT 385: Statistics Programming Methods taught by James Joseph Balamuta
  • Fall 2016 STAT 429: Time Series Analysis taught by Prof. Stephane Guerrier
  • Fall 2016 STAT 578: Time Series Forecasting taught by Prof. Stephane Guerrier
  • Fall 2016 Instructional Staff for STAT 200 (Statistical Analysis), STAT 212 (Biostatistics), and STAT 420 (Methods of Applied Statistics)
  • Spring 2017 STAT 430: Basics of Statistical Learning taught by Dr. David Dalpiaz
  • Spring 2017 Instructional Staff for STAT 200 (Statistical Analysis), STAT 385 (Statistics Programming Methods), STAT 420 (Methods of Applied Statistics), and STAT 428 (Statistical Computing).
  • Summer 2017 STAT 385: Statistics Programming Methods taught by James Joseph Balamuta
  • Fall 2017 STAT 385: Statistics Programming Methods taught by James Joseph Balamuta
  • Fall 2017 STAT 430: Statistics of Basic Learning taught by Dr. David Dalpiaz
  • Fall 2017 Instructional Staff for STAT 200 (Statistical Analysis)

Thanks

The existence of this testbed is in part due to the support of the following individuals:

  • Prof. Jeff Douglas
  • Prof. Steven Culpepper
  • Dr. David Dalpiaz
  • Prof. Douglas Simpson
  • Rami Dass
  • ATLAS - Applied Technologies for Learning in the Arts and Sciences
  • RStudio Inc.

Contact

To receive additional information about the autograde project or the deployment of RStudio services, please contact:
balamut2 "at" illinois [dot] edu.

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