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Quantifying patterns and programming strategies in block-based programming environments

Published: 25 May 2019 Publication History

Abstract

Pupils are often first exposed to programming in block-based programming environments like Scratch. Identifying and measuring the previous experience of students learning to program is a key to improve the teaching of programming. In this contribution, we outline an approach to measure and evaluate programming interactions with the block-based programming environment Scratch. First results, obtained with eight upper secondary school students, show that programming skills and patterns can be quantified with interaction metrics measured during program construction. The aim is a more fine-grained identification and assessment of programming skills.

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C. Watson and F. W. Li, "Failure rates in introductory programming revisited," in ITiCSE '14. New York, USA: ACM, 2014, pp. 39--44.
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MIT Media Lab, "Scratch," https://scratch.mit.edu/, 2019, {Online; accessed 01/2019}.
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M. Armoni, O. Meerbaum-Salant, and M. Ben-Ari, "From Scratch to "real" programming," ACM TOCE, vol. 14, no. 4, pp. 1--15, 2015.
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F. Hermans and E. Aivaloglou, "Do code smells hamper novice programming? A controlled experiment on Scratch programs," IEEE ICPC, vol. July, pp. 1--10, 2016.
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Cited By

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  • (2023)Using Sensor-Based Programming to Improve Self-Efficacy and Outcome Expectancy for Students from Underrepresented GroupsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588854(187-193)Online publication date: 29-Jun-2023

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Published In

cover image ACM Conferences
ICSE '19: Proceedings of the 41st International Conference on Software Engineering: Companion Proceedings
May 2019
369 pages

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IEEE Press

Publication History

Published: 25 May 2019

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Author Tags

  1. block-based programming
  2. learning analytics
  3. programming patterns
  4. programming skills

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ICSE '19
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Overall Acceptance Rate 276 of 1,856 submissions, 15%

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ICSE 2025

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  • (2023)Using Sensor-Based Programming to Improve Self-Efficacy and Outcome Expectancy for Students from Underrepresented GroupsProceedings of the 2023 Conference on Innovation and Technology in Computer Science Education V. 110.1145/3587102.3588854(187-193)Online publication date: 29-Jun-2023

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