A Cognitive-Load-Informed Decomposition of Debugging Subskills with Targeted Exercises

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Bibliographic Details
Title: A Cognitive-Load-Informed Decomposition of Debugging Subskills with Targeted Exercises
Language: English
Authors: Gabriele Pozzan, Andreas Bollin, Tullio Vardanega
Source: Informatics in Education. 2026 25(2):135-165.
Availability: Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
Peer Reviewed: Y
Page Count: 31
Publication Date: 2026
Intended Audience: Teachers
Document Type: Journal Articles
Reports - Research
Descriptors: Cognitive Processes, Troubleshooting, Difficulty Level, Programming, Skill Development, Classification, Problem Solving, Learning Activities, Learning Trajectories
ISSN: 1648-5831
2335-8971
Abstract: Debugging is integral to programming. It comes into play as soon as novices make their first mistakes in creating programming artifacts. It is also consistently reported to be a skill that is difficult to learn as well as to teach effectively. Research in Informatics Education has often focused on the "process" of debugging, by breaking it down in steps connected by temporal and causal dependencies. In this work, we focus instead on debugging "as a skill," from the standpoint of Cognitive Load Theory, and break it down into a tree-shaped model of subskills that enable one another. Debugging may thus be seen as a "meta"-skill that requires the coordination of multiple others. From the standpoint of Cognitive Load Theory, such a skill is cognitively expensive, which may explain the learning-related difficulties tied to debugging. Using the framework of the four-component instructional design, we hypothesize a categorization of each debugging subskill as either recurrent or nonrecurrent, dividing those that are applied consistently to different contexts from those that require problem solving. All subskills may be practised and potentially assessed with targeted exercises, whose design depends on their recurrent/nonrecurrent nature. We provide extensive examples of such exercises. Our decomposition of debugging into subskills is a novel way to address debugging in educational contexts and complements the work done on debugging processes. Although it is currently a theoretically grounded conjecture, the model provides concrete guidance for instructors on analyzing existing materials and planning cognitive-load-informed learning trajectories.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1516136
Database: ERIC
Description
Abstract:Debugging is integral to programming. It comes into play as soon as novices make their first mistakes in creating programming artifacts. It is also consistently reported to be a skill that is difficult to learn as well as to teach effectively. Research in Informatics Education has often focused on the "process" of debugging, by breaking it down in steps connected by temporal and causal dependencies. In this work, we focus instead on debugging "as a skill," from the standpoint of Cognitive Load Theory, and break it down into a tree-shaped model of subskills that enable one another. Debugging may thus be seen as a "meta"-skill that requires the coordination of multiple others. From the standpoint of Cognitive Load Theory, such a skill is cognitively expensive, which may explain the learning-related difficulties tied to debugging. Using the framework of the four-component instructional design, we hypothesize a categorization of each debugging subskill as either recurrent or nonrecurrent, dividing those that are applied consistently to different contexts from those that require problem solving. All subskills may be practised and potentially assessed with targeted exercises, whose design depends on their recurrent/nonrecurrent nature. We provide extensive examples of such exercises. Our decomposition of debugging into subskills is a novel way to address debugging in educational contexts and complements the work done on debugging processes. Although it is currently a theoretically grounded conjecture, the model provides concrete guidance for instructors on analyzing existing materials and planning cognitive-load-informed learning trajectories.
ISSN:1648-5831
2335-8971