abstract

Teaching Machine Learning in K-12 Education

Author:

Ismaila Temitayo SanusiAuthors Info & Claims

ICER 2021: Proceedings of the 17th ACM Conference on International Computing Education Research

Pages 395 - 397

https://doi.org/10.1145/3446871.3469769

Published: 17 August 2021 Publication History

Abstract

This research is interested in how to teach machine learning concepts to K-12 learners. There is limited evidence to support the teaching, learning, and usefulness of machine learning in K-12 settings, hence addressing the evident gap. This research aims to specifically identify pedagogical approaches with the underlying theories and methods in teaching K-12 machine learning as well as design, assess, and determine the impact of machine learning on student outcomes in K-12.

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Cited By

Sanusi IOmidiora JOyelere SVartiainen HSuhonen JTukiainen M(2023)Preparing Middle Schoolers for a Machine Learning-Enabled Future Through Design-Oriented PedagogyIEEE Access10.1109/ACCESS.2023.326902511(39776-39791)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269025
Sanusi ISunday KOyelere SSuhonen JVartiainen HTukiainen M(2023)Learning machine learning with young children: exploring informal settings in an African contextComputer Science Education10.1080/08993408.2023.217555934:2(161-192)Online publication date: 7-Feb-2023
https://doi.org/10.1080/08993408.2023.2175559
Jatileni CSanusi IOlaleye SAyanwale MAgbo FOyelere P(2023)Artificial intelligence in compulsory level of education: perspectives from Namibian in-service teachersEducation and Information Technologies10.1007/s10639-023-12341-z29:10(12569-12596)Online publication date: 7-Dec-2023
https://doi.org/10.1007/s10639-023-12341-z
Show More Cited By

Teaching Machine Learning in K-12 Education
1. Applied computing
2. Social and professional topics
  1. Professional topics
    1. Computing education

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Information

Published In

cover image ACM Conferences

ICER 2021: Proceedings of the 17th ACM Conference on International Computing Education Research

August 2021

451 pages

ISBN:9781450383264

DOI:10.1145/3446871

Editors:
Amy J. Ko
University of Washington, Seattle, USA
,
Jan Vahrenhold
Westfälische Wilhelms-Universität Münster, Germany
,
Renée McCauley
College of Charleston, USA
,
Matthias Hauswirth
Università della Svizzera italiana, Switzerland

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2021

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ICER 2021

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SIGCSE

ICER 2021: ACM Conference on International Computing Education Research

August 16 - 19, 2021

Virtual Event, USA

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Overall Acceptance Rate 189 of 803 submissions, 24%

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Cited By

Sanusi IOmidiora JOyelere SVartiainen HSuhonen JTukiainen M(2023)Preparing Middle Schoolers for a Machine Learning-Enabled Future Through Design-Oriented PedagogyIEEE Access10.1109/ACCESS.2023.326902511(39776-39791)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3269025
Sanusi ISunday KOyelere SSuhonen JVartiainen HTukiainen M(2023)Learning machine learning with young children: exploring informal settings in an African contextComputer Science Education10.1080/08993408.2023.217555934:2(161-192)Online publication date: 7-Feb-2023
https://doi.org/10.1080/08993408.2023.2175559
Jatileni CSanusi IOlaleye SAyanwale MAgbo FOyelere P(2023)Artificial intelligence in compulsory level of education: perspectives from Namibian in-service teachersEducation and Information Technologies10.1007/s10639-023-12341-z29:10(12569-12596)Online publication date: 7-Dec-2023
https://doi.org/10.1007/s10639-023-12341-z
Reddy TWilliams RBreazeal C(2022)LevelUp – Automatic Assessment of Block-Based Machine Learning Projects for AI Education2022 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)10.1109/VL/HCC53370.2022.9833130(1-8)Online publication date: 12-Sep-2022
https://doi.org/10.1109/VL/HCC53370.2022.9833130
Sanusi IOyelere SOmidiora J(2022)Exploring teachers' preconceptions of teaching machine learning in high school: A preliminary insight from AfricaComputers and Education Open10.1016/j.caeo.2021.1000723(100072)Online publication date: Dec-2022
https://doi.org/10.1016/j.caeo.2021.100072
Sanusi IOyelere SVartiainen HSuhonen JTukiainen M(2022)A systematic review of teaching and learning machine learning in K-12 educationEducation and Information Technologies10.1007/s10639-022-11416-728:5(5967-5997)Online publication date: 7-Nov-2022
https://dl.acm.org/doi/10.1007/s10639-022-11416-7

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