research-article

Machine Learning Introduces New Perspectives to Data Agency in K—12 Computing Education

Authors:

Henriikka Vartiainen,

Tapani Toivonen,

Ilkka Jormanainen,

Teemu ValtonenAuthors Info & Claims

2020 IEEE Frontiers in Education Conference (FIE)

Pages 1 - 8

https://doi.org/10.1109/FIE44824.2020.9274138

Published: 21 October 2020 Publication History

Abstract

This innovative practice full paper is grounded in the societal developments of computing in the 2000s, which have brought the concept of information literacy and its many variants into limelight. Widespread tracking, profiling, and behavior engineering have set the alarms off, and there are increasing calls for education that can prepare citizens to cope with the latest technological changes. We describe an active concept, data agency, that refers to people's volition and capacity for informed actions that make a difference in their digital world. Data agency extends the concept of data literacy by emphasizing people’s ability to not only understand data, but also to actively control and manipulate information flows and to use them wisely and ethically.This article describes the theoretical underpinnings of the data agency concept. It discusses the epistemological and methodological changes driven by data-intensive analysis and machine learning. Epistemologically the many new modalities of automation are non-reductionist, non-deterministic, and statistical; the models they rely on are soft and brittle. This article also presents results from a pilot study on how to teach central machine learning concepts and workflows in K-12 through co-creation of machine learning-based solutions.

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Cited By

Kahila JVartiainen HArkko ELin APope NTedre M(2024)Enhancing Understanding of Data Traces and Profiling Among K--9 Students Through an Interactive Classroom GameProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3677635(1-9)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3677635
Höper LSchulte CMühling AMonga MLonati VBarendsen ESheard JPaterson J(2024)Students' Motivation and Intention to Engage with Data-Driven Technologies from a CS Perspective in Everyday LifeProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653625(646-652)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653625
Höper LSchulte CMühling A(2024)Learning an Explanatory Model of Data-Driven Technologies can Lead to Empowered Behavior: A Mixed-Methods Study in K-12 Computing EducationProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671118(326-342)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671118
Show More Cited By

Index Terms

Machine Learning Introduces New Perspectives to Data Agency in K—12 Computing Education
1. Applied computing
  1. Education
2. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Computing education programs
        Computer science education

Index terms have been assigned to the content through auto-classification.

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2020 IEEE Frontiers in Education Conference (FIE)

Oct 2020

2717 pages

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IEEE Press

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Published: 21 October 2020

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Cited By

Kahila JVartiainen HArkko ELin APope NTedre M(2024)Enhancing Understanding of Data Traces and Profiling Among K--9 Students Through an Interactive Classroom GameProceedings of the 19th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3677619.3677635(1-9)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1145/3677619.3677635
Höper LSchulte CMühling AMonga MLonati VBarendsen ESheard JPaterson J(2024)Students' Motivation and Intention to Engage with Data-Driven Technologies from a CS Perspective in Everyday LifeProceedings of the 2024 on Innovation and Technology in Computer Science Education V. 110.1145/3649217.3653625(646-652)Online publication date: 3-Jul-2024
https://dl.acm.org/doi/10.1145/3649217.3653625
Höper LSchulte CMühling A(2024)Learning an Explanatory Model of Data-Driven Technologies can Lead to Empowered Behavior: A Mixed-Methods Study in K-12 Computing EducationProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671118(326-342)Online publication date: 12-Aug-2024
https://dl.acm.org/doi/10.1145/3632620.3671118
Grover SStephenson BStone JBattestilli LRebelsky SShoop L(2024)Teaching AI to K-12 Learners: Lessons, Issues, and GuidanceProceedings of the 55th ACM Technical Symposium on Computer Science Education V. 110.1145/3626252.3630937(422-428)Online publication date: 7-Mar-2024
https://dl.acm.org/doi/10.1145/3626252.3630937
Höper L(2021)Developing and evaluating the concept data awareness for K12 computing educationProceedings of the 21st Koli Calling International Conference on Computing Education Research10.1145/3488042.3490509(1-3)Online publication date: 17-Nov-2021
https://dl.acm.org/doi/10.1145/3488042.3490509

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