Dr. Scratch: a Web Tool to Automatically Evaluate Scratch Projects
Abstract
This poster paper presents the operation and the new features of Dr. Scratch, an award-winning gamified web application that allows to perform automatic analysis of Scratch projects to assess the development of computational thinking and detect some bad programming habits that are common in students learning to program in this environment.
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High scores in the data dimension require children to master the use of complex data structures like lists.
“According to Dr. Scratch's rubrics, high scores in the data dimension require children to master the use of complex data structures like lists.”
From ChatScratch: An AI-Augmented System Toward Autonomous Visual Programming Learning for Children Aged 6-12 (2024) · cites this paper as [40]
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- Authors
- Moreno-León, J., & Robles, G.
- Affiliations
- Programamos.es, Sevilla, Spain
- Universidad Rey Juan Carlos, Madrid, Spain
- ORCID
- Moreno-León, Jesús
- Venue
- Proceedings of the Workshop in Primary and Secondary Computing Education
- Publisher
- ACM
- Published
- 2015-11-09
- Pages
- 132-133
- OpenAlex
- W2294905309
- Cited by
- 183
- References
- 5
- Citations by year
- 2026: 10 · 2025: 11 · 2024: 13 · 2023: 16 · 2022: 22 · 2021: 22 · 2020: 18 · 2019: 45 · 2018: 8 · 2017: 10 · 2016: 8
- FWCI
- 6.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Workshop in Primary and Secondary Computing Education (publishedVersion)
- Funders
- eMadrid - Investigación y Desarrollo de tecnologías para el e-learning en la Comunidad de Madrid (S2013/ICE-2715)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Games and Gamification
- Physical Sciences › Computer Science › Artificial Intelligence › Educational Robotics and Engineering
- Keywords
- Scratch, gamification, computational thinking, programming education, web application, code analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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