Difference between revisions of "An Infrastructure for Sustainable Innovation and Research in Computer Science Education"
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The Hub provides a pioneering example of infrastructure to support data-enabled research on education in Computing and beyond. It advances Computing Education Research through: (1) building a community and website that helps instructors adopt evidence-based practices and innovative learning technologies, (2) creating data standards and large, high-quality datasets, (3) developing advanced algorithms using artificial intelligence, statistics, and analytics methods that leverage data to optimize student learning effectiveness, efficiency, and engagement, (4) developing rigorous evaluation methods to demonstrate large, lasting, and replicable impacts on student achievement. Cross-disciplinary scientific advances are disseminated through the Hub infrastructure community and scientific publications. | The Hub provides a pioneering example of infrastructure to support data-enabled research on education in Computing and beyond. It advances Computing Education Research through: (1) building a community and website that helps instructors adopt evidence-based practices and innovative learning technologies, (2) creating data standards and large, high-quality datasets, (3) developing advanced algorithms using artificial intelligence, statistics, and analytics methods that leverage data to optimize student learning effectiveness, efficiency, and engagement, (4) developing rigorous evaluation methods to demonstrate large, lasting, and replicable impacts on student achievement. Cross-disciplinary scientific advances are disseminated through the Hub infrastructure community and scientific publications. | ||
| − | This project follows our earlier infrastructure community-development project "[[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education]]". It is | + | This project follows our earlier infrastructure community-development project "[[Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education]]". It is supported by NSF award [https://www.nsf.gov/awardsearch/showAward?AWD_ID=2213789&HistoricalAwards=false CNS 2213789 (2017-2021)]. |
== Project Web Site == | == Project Web Site == | ||
| − | To distribute project-related information, we maintain the project website | + | To distribute project-related information, we maintain the project website [http://cssplice.org/ http://cssplice.org/], where all project information including standards, best practices, and resources are be shared. It informs the community about events and opportunities to contribute. The site provides links to project publications, code, data, and learning content hosted in archival repositories such as GitHub and DataShop. |
== Call for Collaborators: What we can offer == | == Call for Collaborators: What we can offer == | ||
Latest revision as of 02:04, 4 April 2024
Contents
Overview
Researchers from the University of Pittsburgh, Carnegie Mellon University, North Carolina State University, and Virginia Tech collaborate to support the Computer Science Education Hub, social and technical infrastructure to accelerate research on teaching and learning of computing disciplines. The Hub will host community events and provide a web repository for computing education datasets, tools, and common analysis methods. The Hub facilitates the creation and adoption of new tools to support computing educators and students, and new standards to support data collection and data-enabled research. Hub community events help researchers and educators from many learning contexts to develop and improve computing education resources.
The Hub provides a pioneering example of infrastructure to support data-enabled research on education in Computing and beyond. It advances Computing Education Research through: (1) building a community and website that helps instructors adopt evidence-based practices and innovative learning technologies, (2) creating data standards and large, high-quality datasets, (3) developing advanced algorithms using artificial intelligence, statistics, and analytics methods that leverage data to optimize student learning effectiveness, efficiency, and engagement, (4) developing rigorous evaluation methods to demonstrate large, lasting, and replicable impacts on student achievement. Cross-disciplinary scientific advances are disseminated through the Hub infrastructure community and scientific publications.
This project follows our earlier infrastructure community-development project "Community-Building and Infrastructure Design for Data-Intensive Research in Computer Science Education". It is supported by NSF award CNS 2213789 (2017-2021).
Project Web Site
To distribute project-related information, we maintain the project website http://cssplice.org/, where all project information including standards, best practices, and resources are be shared. It informs the community about events and opportunities to contribute. The site provides links to project publications, code, data, and learning content hosted in archival repositories such as GitHub and DataShop.
Call for Collaborators: What we can offer
Reusable Smart Learning Content
We have many types of “smart learning content” (various interactive problems and examples) for Java, Python, SQL. Content could be re-used in other classes individually, or as a package through personalized Mastery Grids interface.
- Read more about it in the Smart Content section of the Wiki
- See demos of our “old” content for
Adaptive practice system
We have an infrastructure that allows building flexible practice systems (which students could use on their own) for Java, Python, SQL. The system allows to create support for any course in the field - you define a sequence of topics, assign content to topics, and receive a fully-ready system with an adaptive interface. The system supports our own smart content and several types of interoperable content created by our collaborators. In total, there are 7 types of content for Python, 6 types for Java and 6 types for SQL. We can help you to create a custom support exactly for your course. The system could be used to run extensive classroom studies and collect data. Read more about it here http://kt1.exp.sis.pitt.edu/wiki/Adaptive_Navigation_Support_and_Open_Social_Learner_Modeling_for_PAL Also, watch (not the most recent) demo at https://www.youtube.com/watch?v=76YLR2VY2QE
Call for Collaborators: What we are looking for
More Smart Learning Content!
We are interested in collaborating with other developers of smart learning content to make it more interoperable and to feature in our personalized systems for Python, Java, and SQL.
More Studies with Data Collection
Our ultimate goal is to learn more about how people learn programming and better support them with adaptive educational systems. To get there we need to run more studies of different type of smart learning content and different personalization approaches. We want to partner with instructors in Java, Python and Database classes who are interested to engage their student in practicing computer science concepts with different types of smart learning content. See what we have in “When we can offer” section above and get in touch! It is a great chance to make interesting discoveries and publish good papers. In particular, we just developed PCLab system with several new types of learning content to support learning program construction skills - for which we have the least data. We are especially interested to run studies of this new content. See PCLab demos for Python (http://bit.ly/pclab-python) and Java (http://bit.ly/pclab-java)
Systems
Project partners developed a number of systems and tools for Computer Science Education. Here we list a few tools being developed by PAWS Lab
