Equalizing Data Science Curriculum for Computer Science Pupils

Mike, Koby; Hazan, Tamir; Hazzan, Orit

doi:10.1145/3428029.3428045

Cited by 16 publications

(8 citation statements)

References 20 publications

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“…Workshop teaching ML for object recognition that can be implemented using the knowledge that High School students attain during their normal math and IT classes. (Mike et al, 2020) Data Science and Machine Learning program.…”

Section: No Of Relevant Results (Without Duplicates)mentioning

confidence: 99%

“…(Van Brummelen, 2019;Zhu, 2019;Burgsteiner, 2016b) Coming up with ML project ideas turned out not to be a trivial step for the students, requiring several discussions and iterations. (Mike et al, 2020) Regarding the game ArtBot, some students found the game as either too monotonous, boring, easy, and slow, or too complicated and time-consuming at times. (Voulgari et al,2021) Together with the tendency to more "student-centered" approaches with peer-to-peer learning, cooperative and collaborative learning strategies are commonly adopted in which students work in groups on learning activities.…”

Section: Difficultiesmentioning

confidence: 99%

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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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Section: No Of Relevant Results (Without Duplicates)mentioning

confidence: 99%

Section: Difficultiesmentioning

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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“…A grande maioria das unidades instrucionais observadas ensinam abordagens de ML com aprendizagem supervisionada abordando as etapas de preparação de dados, treinamento, e avaliação do desempenho. Uma parte considerável também aborda redes neurais e DL [Bhatia, 2020][Mike et al 2020. A maioria das unidades instrucionais conceituam como os dados de treinamento influenciam o aprendizado e abordam limitações de ML.…”

Section: Trabalhos Correlatosunclassified

“…com carga horária que varia entre 4 e 80 horas, tipicamente voltados a iniciantes. Foram identificados tantos cursos online [Gresse von Wangenheim et al 2020][Google 2021], quanto presenciais [Mike et al 2020][Norouzi et al 2020). Observa-se que a maioria dos cursos adotam ferramentas visuais, seja para o desenvolvimento de modelos de ML ou para a implantação dos modelos treinados.…”

Section: Trabalhos Correlatosunclassified

“…Observa-se que a maioria dos cursos adotam ferramentas visuais, seja para o desenvolvimento de modelos de ML ou para a implantação dos modelos treinados. Poucos [Mobasher et al 2019][Neumann 2019] usam ambientes de linguagens de programação textual, como Python em Jupyter Notebooks, ou baseada nesta, como o Google Colab [Mike et al 2020][Norouzi et al 2020. Assim, na maioria dos cursos, mesmo no Ensino Médio, a aplicação dos conceitos de ML fica distante dos ambientes convencionais tipicamente utilizados.…”

Section: Trabalhos Correlatosunclassified

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ML4Teens - Introduzindo Machine Learning no Ensino Médio

Cardozo

Martins

Wangenheim

2022

Anais Do XXX Workshop Sobre Educação Em Computação (WEI 2022)

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Considerando a relevância do Machine Learning (ML) atualmente, tornou-se importante a popularização de conceitos de ML já no Ensino Médio. No entanto, existem poucas iniciativas com esse foco para esse estágio educacional. Portanto, este artigo apresenta um curso online desenvolvido para ensinar a compreensão básica e a aplicação de ML e redes neurais no Ensino Médio. Uma avaliação preliminar indica que o curso pode proporcionar uma experiência de aprendizagem eficaz,útil e interessante. Concebido de forma interdisciplinar, o curso pode, assim, ser uma opção para o ensino de ML com foco na classificação de imagens com Deep Learning no Ensino Médio no Brasil em conformidade com diretrizes curriculares.

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What should we teach? A human‐centered data science graduate curriculum model design for iField schools

Sun

et al. 2022

Asso for Info Science & Tech

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The information schools, also referred to as iField schools, are leaders in data science education. This study aims to develop a data science graduate curriculum model from an information science perspective to support iField schools in developing data science graduate education. In June 2020, information about 96 data science graduate programs from iField schools worldwide was collected and analyzed using a mixed research method based on inductive content analysis. A wide range of data science competencies and skills development and 12 knowledge topics covered by the curriculum were obtained. The humanistic model is further taken as the theoretical and methodological basis for course model construction, and 12 course knowledge topics are reconstructed into 4 course modules, including (a) data-driven methods and techniques; (b) domain knowledge; (c) legal, moral, and ethical aspects of data; and (d) shaping and developing personal traits, and human-centered data science graduate curriculum model is formed. At the end of the study, the wide application prospect of this model is discussed.

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Equalizing Data Science Curriculum for Computer Science Pupils

Cited by 16 publications

References 20 publications

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

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

ML4Teens - Introduzindo Machine Learning no Ensino Médio

What should we teach? A human‐centered data science graduate curriculum model design for iField schools

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