Co-Designing Machine Learning Apps in K–12 With Primary School Children

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

doi:10.1109/icalt49669.2020.00099

Cited by 53 publications

(35 citation statements)

References 12 publications

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“…Indeed, while entire generations of people are growing up in the middle of machine learning (ML) systems, this development seems to have been given only minor attention in CER. A small but growing body of research shows concrete examples of teaching ML to beginners [119], [120], [127], [128]. New social and ethical dilemmas created by new AIsystems also call for reshaping of related training in AI ethics [129], [130].…”

Section: A Keyword Trends (Rq1)mentioning

confidence: 99%

Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination

et al. 2022

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The need for organized computing education efforts dates back to the 1950s. Since then, computing education research (CER) has evolved and matured from its early initiatives and separation from mathematics education into a respectable research specialization of its own. In recent years, a number of meta-research papers, reviews, and scientometric studies have built overviews of CER from various perspectives. This paper continues that approach by offering new perspectives on the past and present state of CER: analyses of influential papers throughout the years, of the theoretical backgrounds of CER, of the institutions and authors who create CER, and finally of the top publication venues and their citation practices. The results reveal influential contributions from early curriculum guidelines to rigorous empirical research of today, the prominence of computer programming as a topic of research, evolving patterns of learning-theory usage, the dominance of high-income countries and a cluster of 52 elite institutions, and issues regarding citation practices within the central venues of dissemination.

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Section: A Keyword Trends (Rq1)mentioning

confidence: 99%

Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination

et al. 2022

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“…Recent research has emerged that focuses on introducing AI/ML knowledge to young learners. Several major trends include 1) Structured series courses teaching basic ML concepts and utilizing existing AI education tools [6,17,41,63]; 2) short workshops using interactive data visualization and toy problems to teach students basic algorithms [13,15,48]; 3) learning environments enabling students to develop basic AI applications with block-based programming [22,34,51,74]; 4) Accessible and engaging GUI/TUI/VUIs enabling students to train and test ML models with much of the programming complexity hidden behind the interface [18,45,53,64]. In addition to competencies being infrequently addressed, certain design considerations were also overlooked.…”

Section: Overview Of Existing Trendsmentioning

confidence: 99%

“…The fifth guideline addresses this barrier. For example, several strategies to overcome this challenge in existing works include 1) using block-based visual programming to train and test ML models [2,9,21,22]; 2) unveil the complex ML concepts gradually through GUI/TUI/VUIs and visualizations for students to engage with and develop AI systems without programming [13,18,29,64] (Future Opportunity 5); 3) facilitating the progression of AI learning with starter codes, worked examples [16] or following the "Use-Modify-Create" cycle [53,57,63,75]; and 4) providing detailed workbook or guided tutorials [34,50,73,74].…”

Section: Gamification and Embodimentmentioning

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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“…Using the standalone TensorFlow library requires scripting skills in computer language like Python or JavaScript, which is eliminated in GTM and easy to access with GUI. Recently, GTM is being used for teaching purposes (Toivonen et al 2020) and even has been used to design an educational program (Yu et al 2019). ML algorisms have been applied for insect and crop pest classification (Tuda and Luna-Maldonado 2020;Ayan et al 2020), but GTM has never been evaluated to develop ML model for insect classification purposes.…”

Section: Introductionmentioning

confidence: 99%

Short communication: Insect detection using a machine learning model

Gupta

Homchan

2021

Nusantara Biosci

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Abstract. Homchan S, Gupta YM. 2020. Short communication: Insect detection using a machine learning model. Nusantara Bioscience 13: 69-73. The key step in characterizing any organisms and their gender highly relies on correct identification of specimens. Here we aim to classify insect and their sex by supervised machine learning (ML) model. In the present preliminary study, we used a newly developed graphical user interface (GUI) based platform to create a machine learning model for classifying two economically important cricket species. This study aims to develop ML model for Acheta domesticus and Gryllus bimaculatus species classification and sexing. An experimental investigation was conducted to use Google teachable machine GTM for preliminary cricket species detection and sexing using pre-processed 2646 still images. An alternative method for image processing is used to extract still images from high-resolution video for optimum accuracy. Out of the 2646 images, 2247 were used for training ML model and 399 were used for testing the trained model. The prediction accuracy of trained model had 100 % accuracy to identify both species and their sex. The developed trained model can be integrated into the mobile application for cricket species classification and sexing. The present study may guide professionals in the field of life science to develop ML models based on image classification, and serve as an example for researchers and taxonomists to employ machine learning for species classification and sexing in the preliminary analysis. Apart from our main goals, the paper also intends to provide the possibility of ML models in biological studies and to conduct the preliminary assessment of biodiversity.

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Co-Designing Machine Learning Apps in K–12 With Primary School Children

Cited by 53 publications

References 12 publications

Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination

Computing Education Research Compiled: Keyword Trends, Building Blocks, Creators, and Dissemination

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

Short communication: Insect detection using a machine learning model

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