Machine Learning Unplugged - Development and Evaluation of a Workshop About Machine Learning

Ossovski, Elisaweta; Brinkmeier, Michael

doi:10.1007/978-3-030-33759-9_11

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…Of the articles analyzed, the studies were majorly conducted with high schoolers as shown in Fig. 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.…”

Section: Articles Distributed Based On Educational Levelsmentioning

confidence: 99%

“…Such tools include Google's Teachable Machine (GTM), robots, scratch, RapidMiner, bounding box, data cards, set of toy cookies and picture cards. Few articles (Essinger & Rosen, 2011;Ossovski & Brinkmeier, 2019) adopted an unplugged activities approach to convey the central idea of AI to students. Some of the learning aims as deduced from the papers are to teach the concept of facial recognition (Ho & Scadding, 2019), signal processing (Essinger & Rosen, 2011), linear classifier (Ossovski & Brinkmeier, 2019), Decision tree and K-mean and ANN (Estevez et al, 2019).…”

Section: Pedagogical Developmentmentioning

confidence: 99%

“…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). Active-based learning activities were also introduced to the students through class exercises and facilitated discussions (e.g., Estevez et al, 2019).…”

Section: Pedagogical Developmentmentioning

confidence: 99%

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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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