An Electroencephalography Study on Cognitive Load in Visual and Textual Programming
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Author / Producer
Date
2024-08-13
Publication Type
Conference Paper
ETH Bibliography
yes
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Abstract
This paper presents a comparative study of Algot, a visual programming language designed to bridge the syntax-semantics gap via liveness and programming by demonstration, and the textual programming language Python. We conducted an experimental, within-subjects study with 24 undergraduate computer science students who performed recursion-based tasks in each language while their cognitive load was measured using an electroencephalogram and a validated survey instrument. The students received a brief introduction to Algot, but were all familiar with Python. The students performed significantly better when programming in Algot, but the cognitive load levels were similar according to both instruments. Our results provide evidence that within the domain that was tested, Algot can be quickly learned, and that students do not find it more cognitively demanding than working in a familiar language.
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Publication status
published
External links
Book title
ICER '24: Proceedings of the 2024 ACM Conference on International Computing Education Research
Journal / series
Volume
1
Pages / Article No.
280 - 292
Publisher
Association for Computing Machinery
Event
20th Annual ACM Conference on International Computing Education Research (ICER 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
electroencephalography; undergraduate education; CS1; recursion; cognitive load; visual programming; programming by demonstration; direct manipulation; live programming
Organisational unit
09628 - Su, Zhendong / Su, Zhendong
Notes
Conference Presentation held on August 14, 2024.