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VotestratesML: A High School Learning Tool for Exploring Machine Learning and its Societal Implications

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

The increased use of Artificial Intelligence, and in particular Machine Learning (ML) raises the need for widespread AI literacy, in three particular areas related to ML; understanding how ML works, the process behind creating ML models, and the ability to reflect on its personal and societal implications. Existing ML learning tools focus primarily on the first two areas, and to a lesser degree the third. In order to address this, we designed VotestratesML; a tool allowing K-12 students to build models and make predictions based on real world voting data. Based on in-situ deployments of VotestratesML, we reflect on opportunities and challenges for engaging K-12 students in understanding and reflecting on ML. We find that the design of VotestratesML supports students' engagement in all three areas of ML, through grounding ML in a known subject area and allowing for collaboration and competition.

Original languageEnglish
Title of host publicationProceedings of 5th FabLearn Europe / MakeEd Conference 2021
PublisherAssociation for Computing Machinery
Publication year2021
Article number3466728
ISBN (print)978-1-4503-8989-1
ISBN (Electronic)9781450389891
DOIs
Publication statusPublished - 2021
Event5th International Conference on Computing, Design and Making in Education, FabLearn Europe / MakeEd 2021 - Virtual, Online, Switzerland
Duration: 2 Jun 20213 Jun 2021

Conference

Conference5th International Conference on Computing, Design and Making in Education, FabLearn Europe / MakeEd 2021
LandSwitzerland
ByVirtual, Online
Periode02/06/202103/06/2021

    Research areas

  • #CCTD, CCTD, CEED, K-12 Education, Machine Learning, Societal Implications, Technology Education

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