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Modern societies rely extensively on computing technologies. As such, there is a need to identify and develop strategies for addressing fairness, ethics, accountability, and transparency (FEAT) in computing-based research, practice, and educational efforts. To achieve this aim, a workshop, funded by the National Science Foundation, convened a working group of experts to document best practices and integrate disparate approaches to FEAT. The working group included different disciplines, demographics, and institutional types, including large research-intensive universities, Historically Black Colleges and Universities, Hispanic-Serving Institutions, teaching institutions, and liberal arts colleges. The workshop brought academics and members of industry together along with government representatives, which is vitally important given the role and impact that each sector can have on the future of computing. Relevant insights were gained by drawing on the experience of policy scholars, lawyers, statisticians, sociologists, and philosophers along with the more traditional sources of expertise in the computing realm (such as computer scientists and engineers). The working group examined best practices and sought to articulate strategies for addressing FEAT in computing-based research and education. This included identifying methodological approaches that researchers could employ to facilitate FEAT, instituting guidelines on what problem definition practices work best, and highlighting best practices for data access and data inclusion. The resulting report is the culmination of the working group activities in identifying systematic methods and effective approaches to incorporate FEAT considerations into the design and implementation of computing artifacts.
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Where's the Data? Exploring Datasets in Computing Education
This working group aims to identify available datasets within the context of computing education research. One particular area of interest is programming education, and the data in question may include students' steps, progress, or submissions in the form of program code. To achieve this goal, the working group will review well-known data resources and repositories (e.g., DataShop, GitHub, NSF Public Access Repository, and IEEE DataPort) and recent papers published within the SIGCSE community. As a result of the review process, the working group will create an overview of available datasets and characterize them while reflecting on current data practices, challenges, and the consequences of limited access to research data. Additionally, the group intends to propose a path for the community to become more open and move toward open data practices. This proposal highlights the importance of sharing research data within the computing education research community to make it stronger and more productive.
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- PAR ID:
- 10518569
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the ACM Conference on Global Computing Education (CompEd 2023)
- ISBN:
- 9798400703744
- Page Range / eLocation ID:
- 209 to 210
- Subject(s) / Keyword(s):
- Open Data Open Science datasets reusing data computing education secondary research educational data mining
- Format(s):
- Medium: X
- Location:
- Hyderabad India
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3617650.3624951
