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

Tedre, Matti; Vartiainen, Henriikka; Kahila, Juho; Suhonen, Jarkko; Jormanainen, Ilkka; Valtonen, Teemu

doi:10.1109/fie44824.2020.9274138

Cited by 24 publications

(22 citation statements)

References 31 publications

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“…In accordance with the data literacy approach, we suggest that data agency calls for a critical attitude toward digital being, ownership, and control of one’s own data as well as informed ethical and moral decision-making grounded in understanding of how data are generated, processed, and used for different purposes (Tedre et al, 2020). While data agency can be seen as having capacity and volition to make informed decisions and personal data strategies, we also see a need to understand data agency as a relational and dynamic process that develops over time.…”

Section: Introductionmentioning

confidence: 97%

“…boyd, 2014; boyd and Marwick, 2011; Davis and James, 2013; Keen, 2020; Lapenta and Jørgensen, 2015; Marwick and boyd, 2014; Pangrazio and Selwyn, 2019; Tufekci, 2008), younger children (e.g. Livingstone et al, 2019; Tedre et al, 2020), and their parents (Stoilova et al, 2020). A recent study by Stoilova et al (2020) also provided some insights on the perspectives of teachers.…”

Section: Introductionmentioning

confidence: 99%

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Pre-service teachers’ insights on data agency

Vartiainen

Pellas

Kahila

et al. 2022

New Media & Society

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With growing concerns over children’s data agencies, researchers have begun to draw attention to children’s and young people’s privacy practices in social media environments. However, little is known about the experiences of pre-service teachers who play a key role in educating future generations. This study aimed to address this gap by exploring Finnish pre-service teachers’ conceptions and experiences of data agency in social media environments. Drawing from in-depth interviews of pre-service teachers ( N = 14), the analysis revealed that pre-service teachers construct their data agency in terms of social frames and shared social norms, and they also recognize the lack of understanding regarding wider socio-technical systems within which data agencies are situated. This research argues that without a sophisticated understanding of algorithmic governance and commercial use of data, it is unlikely that these future teachers would be prepared to facilitate children’s and youth’s agentive actions in a data-driven society.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Pre-service teachers’ insights on data agency

Vartiainen

Pellas

Kahila

et al. 2022

New Media & Society

Self Cite

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“…ACM In the next step, we analyzed the full texts and excluded irrelevant ones following the inclusion/exclusion and quality criteria. We also excluded articles describing instructional units targeting undergraduate and graduate/college level (Yu and Poger, 2020;Bennett, 2017;Kwan, 2014), other K-12 levels such as pre-school and elementary school (Tedre et al, 2020), or teachers' preparation programs (Mike and Rosenberg-Kima, 2021;Lin and Van Brummelen, 2021). We also excluded articles focusing on teaching data science targeting K-12 but not covering any ML concepts (Harvey and Kumar, 2019).…”

Section: No Of Relevant Results (Without Duplicates)mentioning

confidence: 99%

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

Martins¹,

Wangenheim²

2022

Informatics in Education

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Machine Learning (ML) is becoming increasingly present in our lives. Thus, it is important to introduce ML already in High School, enabling young people to become conscious users and creators of intelligent solutions. Yet, as typically ML is taught only in higher education, there is still a lack of knowledge on how to properly teach younger students. Therefore, in this systematic literature review, we analyze findings on teaching ML in High School with regard to content, pedagogical strategy, and technology. Results show that High School students were able to understand and apply basic ML concepts, algorithms and tasks. Pedagogical strategies focusing on active problem/project-based hands-on approaches were successful in engaging students and demonstrated positive learning effects. Visual as well as text-based programming environments supported students to build ML models in an effective way. Yet, the review also identified the need for more rigorous evaluations on how to teach ML.

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“…4, n = 13 (Sperling & Lickerman, 2012;Burgsteiner et al, 2016;Mariescu-Istodor and Jormanainen, 2019;Kahn et al, 2018;Wan et al, 2020;Rodríguez-García et al, 2020aEssinger & Rosen, 2011;Ossovski & Brinkmeier, 2019;Evangelista et al, 2018;Vachovsky et al, 2016;Estevez et al, 2019). Eight studies focused on primary schoolers (Mariescu-Istodor and Jormanainen, 2019; Lee et al, 2020;Ho & Scadding, 2019;Toivonen, et al, 2020;Chai, et al, 2020;Druga et al, 2019;Tedre, et al, 2020;Hitron, et al, 2018) while only two studies were found that targets Kindergarten (Williams et al, 2019a(Williams et al, , 2019b. Four studies focused each on elementary, middle (Sabuncuoglu, 2020;Rodríguez-García et al, 2020aSakulkueakulsuk et al, 2018), middle/high (Opel et al, 2019;Zimmermann-Niefield et al, 2019aZimmermann-Niefield, et al, 2020) and teachers ( Chiu & Chai, 2020;Kandlhofer et al, 2019;Zhou, et al, 2021) and only one that covers all levels from elementary to high school.…”

Section: Articles Distributed Based On Educational Levelsmentioning

confidence: 99%

“…To introduce the learning aims to the students, several approaches were utilized. The approaches most adopted include project-based learning which include co-creation of ML-based solutions with the students (e.g., Tedre et al, 2020;Toivonen et al, 2020). Almost all the articles included in this review utilized group work activities to foster students to learn ML basics (e.g., Ossovski & Brinkmeier, 2019;Sakulkueakulsuk et al, 2018).…”

Section: Pedagogical Developmentmentioning

confidence: 99%

A systematic review of teaching and learning machine learning in K-12 education

et al. 2022

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The increasing attention to Machine Learning (ML) in K-12 levels and studies exploring a different aspect of research on K-12 ML has necessitated the need to synthesize this existing research. This study systematically reviewed how research on ML teaching and learning in K-12 has fared, including the current area of focus, and the gaps that need to be addressed in the literature in future studies. We reviewed 43 conference and journal articles to analyze specific focus areas of ML learning and teaching in K-12 from four perspectives as derived from the data: curriculum development, technology development, pedagogical development, and teacher training/professional development. The findings of our study reveal that (a) additional ML resources are needed for kindergarten to middle school and informal settings, (b) further studies need to be conducted on how ML can be integrated into subject domains other than computing, (c) most of the studies focus on pedagogical development with a dearth of teacher professional development programs, and (d) more evidence of societal and ethical implications of ML should be considered in future research. While this study recognizes the present gaps and direction for future research, these findings provide insight for educators, practitioners, instructional designers, and researchers into K-12 ML research trends to advance the quality of the emerging field.

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

Cited by 24 publications

References 31 publications

Pre-service teachers’ insights on data agency

Pre-service teachers’ insights on data agency

Findings on Teaching Machine Learning in High School: A Ten - Year Systematic Literature Review

A systematic review of teaching and learning machine learning in K-12 education

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