Enhancing Collaboration and Infrastructure for Data-Intensive Research in CS Education
This workshop focuses on SPLICE initiative, funded by NSF, which aims to support collaborative research and community-building in computer science education. The project emphasizes developing standards, protocols, and learning infrastructure to enhance computing education. Accomplishments include the establishment of a website, tutorials, workshops, and working groups to promote interoperability and data sharing among interested parties. Challenges such as tool integration and data compatibility are being addressed to improve analytics dissemination. The workshop also explores use cases like integrating LTI in an LMS for improved connectivity.
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Presentation Transcript
Welcome to SPLICE Workshop 4.0 February 2019
What is SPLICE? NSF support to project: Collaborative Research: Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education Ken Koedinger, CMU Peter Brusilovsky, UPitt Cliff Shaffer and Steve Edwards, Virginia Tech Grant period: September 1, 2017 February 29, 2020
Standards, Protocols, and Learning Infrastructure for Computing Education Mission: support the CS Education community by supplying documentation and infrastructure to help with adopting shared standards, protocols, and tools. We promote: Development and broader re-use of innovative learning content that is instrumented for rich data collection; Formats and tools for analysis of learner data; and Best practices to make large collections of learner data and associated analytics available to researchers in the CSE, data science, and learner science communities.
What Have We Accomplished So Far? (1) Some infrastructure Website: https://cssplice.github.io Tutorials: LTI, Caliper GitHub project Google Group Workshops 1.0: June 2017 in Pittsburg 2.0: February 2018 in Baltimore 3.0: August 2018 in Espoo, Finland
What Have We Accomplished So Far? (2) Working Groups Small Code Snapshots Small Coding Exercise Representation Packaging Curricular Materials Collaborations!
Interoperability Issues: Vision Lots of software artifacts available for use by instructors in a rich network The artifacts generate learner analytics The learner analytics data are shared to the various interested parties
Interoperability Issues: Problems Tools are hard to integrate. (LTI?) Incompatible learner analytics data (Caliper? Working groups?) Hard to get analytics to interested parties (Caliper?)
Use Case 1 An LMS and tools Hub-and-spokes LTI pretty well solves basic connection issues, everyone just needs to support Still have issues with learner analytics (limited data transfer)
Use Case 2 eTextbook with tools talking to LMS Examples: OpenDSA, MasteryGrid eTextbook marshals resources, primes the LMS for access These resources are other smart content, primarily exercises, like Code Workout, OpenDSA exercises, ACOS exercises, on and on Can support access to 3rdparty tools via LTI But no delivery of data analytics to any interested members of the network How to get the data from the exercise (invoked by Canvas) back to the eTextbook? For analysis, and for value-added processing like late policy
PART II: Data Formats and Analysis All of our working groups are more-or-less working on formats Only a few instances of shared analysis tools?
Access to Data: Vision Various tool providers are generating rich data sets Example: Web-CAT has been collecting data for years Learning scientists can use data collected by others to test hypotheses
Access to Data: Problems Even if we solve all issues of data formats and analysis tools, there are issues related to sharing data, due to privacy issues Clearing with institutions charged with protecting privacy Sanitizing data while preserving key relationships Think about sanitizing program source code with comments! We have a few collaborations sharing data and basic tools for things like anonymization.
