Developing Teaching Materials on Artificial Intelligence by Using a Simulation Game (Work in Progress)

Opel, Simone; Schlichtig, Michael; Schulte, Carsten

doi:10.1145/3361721.3362109

Cited by 10 publications

(9 citation statements)

References 7 publications

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“…For instance, one work included an activity in which students physically acted out feature extraction and the nearest-neighbor algorithm [19]; in another, students acted as rule-based conversational agents [59]. Only six works engaged students in embodying full algorithms or processes such as these [17,19,24,37,59,61] (Future Opportunity 7). Nonetheless, some works involved shallow embodiment in which students embodied data collection through gestures [2,18,75,76].…”

Section: Engagementmentioning

confidence: 99%

“…In-time feedback loops, as discussed in 6.3.2, data labeling and evaluation [18], and peer sharing and remixing [10] can stimulate student reflection on underlying ML mechanisms. Other methods include after-lab discussion [13,13,15,28,34], reflective worksheets, journals or blogs [7,8,28,37,48,63,76], and collaborative group work [5,28,42].…”

Section: Promote Reflectionmentioning

confidence: 99%

“…Non-programming activities can also be designed to practice social studies, science, and math skills (e.g. [37,66]). As AI becomes more relevant to different disciplines, curricula that can enable more touch points for interdisciplinary learning will emerge.…”

Section: Integrated Ai In Core Curriculamentioning

confidence: 99%

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Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework

Zhou¹,

Lin²

2020

Preprint

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Artificial intelligence (AI) literacy is a rapidly growing research area and a critical addition to K-12 education. However, support for designing tools and curriculum to teach K-12 AI literacy is still limited. There is a need for additional interdisciplinary human-computer interaction and education research investigating (1) how general AI literacy is currently implemented in learning experiences and ( 2) what additional guidelines are required to teach AI literacy in specifically K-12 learning contexts. In this paper, we analyze a collection of K-12 AI and education literature to show how core competencies of AI literacy are applied successfully and organize them into an educator-friendly chart to enable educators to efficiently find appropriate resources for their classrooms. We also identify future opportunities and K-12 specific design guidelines, which we synthesized into a conceptual framework to support researchers, designers, and educators in creating K-12 AI learning experiences. CCS Concepts: • Social and professional topics → K-12 education; • Computing methodologies → Artificial intelligence; • Applied computing → Interactive learning environments; • General and reference → Surveys and overviews.

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Section: Engagementmentioning

confidence: 99%

Section: Promote Reflectionmentioning

confidence: 99%

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Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework

Zhou¹,

Lin²

2020

Preprint

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“…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%

“…Several teaching and learning activities were utilized to introduce the tools to students such as embodied models of gesture (e.g., Zimmermann-Niefield et al, 2020) and the use of mind maps and visualization (e.g., Lin et al, 2020). Other activities include the use of simulation game (e.g., Opel et al, 2019), modelling (Lee et al, 2020), and engaging students in scientific inquiry behaviors such as question asking, and explanation (Wan et al, 2020).…”

Section: Technology 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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KI-Labor: Online-Lernumgebungen zur künstlichen Intelligenz

Mühling

Bastian

2023

Die Zukunft Des MINT-Lernens – Band 2

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ZusammenfassungKünstliche Intelligenz und speziell auch maschinelles Lernen prägen als Technologie vermehrt unseren Alltag und werfen auch Fragen von gesamtgesellschaftlicher Tragweite auf. Die Thematisierung der Grundlagen dieser Verfahren ist damit auch eine schulische Aufgabe. Eine problemlose Einbettung in den existierenden Fachunterricht erscheint aber aus im Beitrag dargelegten Gründen nicht ohne Weiteres möglich, sodass zunächst eine fachdidaktische Aufbereitung der Themen – speziell auch in Form von Unterrichtsmaterial – erfolgen muss. Der Beitrag stellt dazu drei digitale Lernumgebungen vor, die basierend auf gemeinsamen theoretischen Überlegungen verschiedene Ausgestaltungen für Unterricht zu typischen Themen der künstlichen Intelligenz bzw. des maschinellen Lernens darstellen: Perceptren, künstliche neuronale Netze und Verstärkungslernen. Zentral für alle Umgebungen ist ein Element der interaktiven Exploration von Systemen, diese werden durch stärker oder weniger stark geleitete Bearbeitungswege und Aufgaben ergänzt.

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Developing Teaching Materials on Artificial Intelligence by Using a Simulation Game (Work in Progress)

Cited by 10 publications

References 7 publications

Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework

Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework

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

KI-Labor: Online-Lernumgebungen zur künstlichen Intelligenz

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