Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review
Accepted manuscript, 893 KB, PDF document
Final published version
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 language | English |
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Title of host publication | Proceedings of 5th FabLearn Europe / MakeEd Conference 2021 |
Publisher | Association for Computing Machinery |
Publication year | 2021 |
Article number | 3466728 |
ISBN (print) | 978-1-4503-8989-1 |
ISBN (Electronic) | 9781450389891 |
DOIs | |
Publication status | Published - 2021 |
Event | 5th International Conference on Computing, Design and Making in Education, FabLearn Europe / MakeEd 2021 - Virtual, Online, Switzerland Duration: 2 Jun 2021 → 3 Jun 2021 |
Conference | 5th International Conference on Computing, Design and Making in Education, FabLearn Europe / MakeEd 2021 |
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Land | Switzerland |
By | Virtual, Online |
Periode | 02/06/2021 → 03/06/2021 |
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ID: 216079862