Using video analysis and learning analytics to understand programming trajectories in data science activities with Scratch-Reference-Cited by-同舟云学术

Using video analysis and learning analytics to understand programming trajectories in data science activities with Scratch

Published:2022-06-27 Issue: Volume: Page:
ISSN:
Container-title:Interaction Design and Children
language:
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Author:

Fernandez Cassia¹,Freitas João Adriano²,Lopes Roseli de Deus³,Blikstein Paulo⁴

Affiliation:

1. Electronic Systems Dept., University of São Paulo, Brazil and TLTLab, Teachers College, Columbia University, United States

2. University of São Paulo, Brazil

3. Electronic Systems Dept., University of Sao Paulo, Brazil

4. TLTLab, Teachers College, Columbia University, United States

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) in Brazil

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3501712.3529742

Reference32 articles.

1. Australian Curriculum Assessment and Reporting Authority [ACARA]. 2015. Foundation–Year 10 Australian Curriculum: Science. Retrieved January 4, 2022 from https://www.australiancurriculum.edu.au/f-10-curriculum/science/ Australian Curriculum Assessment and Reporting Authority [ACARA]. 2015. Foundation–Year 10 Australian Curriculum: Science. Retrieved January 4, 2022 from https://www.australiancurriculum.edu.au/f-10-curriculum/science/

2. ACM , Code .org, CSTA , Cyber Innovation Center, and National Math and Science Initiative . 2016 . K-12 Computer Science Framework. Retrieved from http://www.k12cs.org ACM, Code.org, CSTA, Cyber Innovation Center, and National Math and Science Initiative. 2016. K-12 Computer Science Framework. Retrieved from http://www.k12cs.org

3. Modeling and Measuring High School Students’ Computational Thinking Practices in Science

4. A Principled Approach to Designing a Computational Thinking Practices Assessment for Early Grades

5. Educational Data Mining and Learning Analytics: Applications to Constructionist Research

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