Machine Learning for All!—Introducing Machine Learning in Middle and High School

Martins, Ramon Mayor; Wangenheim, Christiane Gresse von; Rauber, Marcelo Fernando; Hauck, Jean Carlo Rossa

doi:10.1007/s40593-022-00325-y

Cited by 18 publications

(16 citation statements)

References 43 publications

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“…In , children aged between 3 and 9 autonomously explored the input-output relationship with Google Teachable Machine, which fostered their intellectual curiosity, developed their computational thinking, and enhanced their understanding of machine learning. In both primary (Toivonen et al, 2020;Melsión et al, 2021) and secondary schools (Kilhoffer et al, 2023;Martins et al, 2023), Google Teachable Machine has been employed, allowing students to use their webcams, images, or sounds without coding to develop their own machine learning classification models.…”

Section: Intelligent Agentsmentioning

confidence: 99%

“…For example, Scratch, a block-based programming software, is frequently used in both primary (Dai et al, 2023;Li & Song, 2019;Shamir & Levin, 2021) and secondary schools (Estevez et al, 2019;Fernández-Martínez et al, 2021). Other software is used for visualizing and scaffolding abstract AI concepts through online games and experiences, such as Quick and Draw (Martins et al, 2023) and Music Box (Han et al, 2018). In primary schools, Kitten is used to teach block-based programming (Li & Song, 2019), whereas C++ and JavaScript are used for logical thinking and simulation (Gong et al, 2020).…”

Section: Software Devicesmentioning

confidence: 99%

“…In secondary schools, researchers have also reported an increase in students' knowledge of AI algorithms (Yoder et al, 2020) and machine learning concepts (Sakulkueakulsuk et al, 2018), as well as their recognition of AI patterns (Wan et al, 2020). For example, students understood the fundamental neural networks of machine learning concepts by developing a classification model of recycling images (Martins et al, 2023).…”

Section: Cognitive Outcomesmentioning

confidence: 99%

“…Affective and behavioral outcomes have been identified in AI learning tool studies within K-12 contexts. In general, students' motivation to learn AI (Han et al, 2018;Shamir & Levin, 2021 and their interest in the course (Mariescu-Istodor & Jormanainen, 2019;Martins et al, 2023) were enhanced as a result of AI learning activities. Students' perceptions of the relevance of AI to their life also increased (Kajiwara et al, 2023;Lin et al, 2021).…”

Section: Affective and Behavioral Outcomesmentioning

confidence: 99%

“…Students scored high on self-efficacy (Kajiwara et al, 2023;Shamir & Levin, 2022) and confidence (Shamir & Levin, 2021) in training and validating an AI system. In Martins et al (2023), over 45% of 108 secondary school student participants in the introductory course "Machine Learning for all" reported that they perceived AI learning as an enjoyable experience, and 63% of them hoped to learn more about machine learning in the future.…”

Section: Affective and Behavioral Outcomesmentioning

confidence: 99%

See 4 more Smart Citations

Artificial intelligence (AI) learning tools in K-12 education: A scoping review

Yim,

2024

J. Comput. Educ.

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Artificial intelligence (AI) literacy is a global strategic objective in education. However, little is known about how AI should be taught. In this paper, 46 studies in academic conferences and journals are reviewed to investigate pedagogical strategies, learning tools, assessment methods in AI literacy education in K-12 contexts, and students’ learning outcomes. The investigation reveals that the promotion of AI literacy education has seen significant progress in the past two decades. This highlights that intelligent agents, including Google’s Teachable Machine, Learning ML, and Machine Learning for Kids, are age-appropriate tools for AI literacy education in K-12 contexts. Kindergarten students can benefit from learning tools such as PopBots, while software devices, such as Scratch and Python, which help to develop the computational thinking of AI algorithms, can be introduced to both primary and secondary schools. The research shows that project-based, human–computer collaborative learning and play- and game-based approaches, with constructivist methodologies, have been applied frequently in AI literacy education. Cognitive, affective, and behavioral learning outcomes, course satisfaction and soft skills acquisition have been reported. The paper informs educators of appropriate learning tools, pedagogical strategies, assessment methodologies in AI literacy education, and students’ learning outcomes. Research implications and future research directions within the K-12 context are also discussed.

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Section: Intelligent Agentsmentioning

confidence: 99%