The CTSkills App -- Measuring Problem Decomposition Skills of Students in Computational Thinking
November 22, 2024 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Dorit Assaf, Giorgia Adorni, Elia Lutz, Lucio Negrini, Alberto Piatti, Francesco Mondada, Francesca Mangili, Luca Maria Gambardella
arXiv ID
2411.14945
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
cs.ET
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
This paper addresses the incorporation of problem decomposition skills as an important component of computational thinking (CT) in K-12 computer science (CS) education. Despite the growing integration of CS in schools, there is a lack of consensus on the precise definition of CT in general and decomposition in particular. While decomposition is commonly referred to as the starting point of (computational) problem-solving, algorithmic solution formulation often receives more attention in the classroom, while decomposition remains rather unexplored. This study presents "CTSKills", a web-based skill assessment tool developed to measure students' problem decomposition skills. With the data collected from 75 students in grades 4-9, this research aims to contribute to a baseline of students' decomposition proficiency in compulsory education. Furthermore, a thorough understanding of a given problem is becoming increasingly important with the advancement of generative artificial intelligence (AI) tools that can effectively support the process of formulating algorithms. This study highlights the importance of problem decomposition as a key skill in K-12 CS education to foster more adept problem solvers.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted