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Goliath and the cognitive load theory

2025Artigo científico Acesso aberto
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datacite.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática
datacite.subject.fosCiências Sociais::Ciências da Educação
datacite.subject.sdg04:Educação de Qualidade
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
dc.contributor.authorFreitas, Tiago Carvalho
dc.contributor.authorCosta Neto, Alvaro
dc.contributor.authorPereira, Maria João
dc.contributor.authorHenriques, Pedro Rangel
dc.date.accessioned2026-03-19T10:06:07Z
dc.date.available2026-03-19T10:06:07Z
dc.date.issued2025
dc.description.abstractA successful teaching effort is usually dependant on several factors. From the right environment, to a precisely worded exercise statement, it rests on the teacher's shoulders the concoction of the most effective learning assets to their students. A significant part of this process lies on practise: students commonly solidify their knowledge by solving exercises. Creating new programming exercises, specially in high-demand environments such as large classrooms, is a repetitive and error-prone process, specially when stacked with other typical affairs that educators are required to attend to. Goliath, one of the two main contributions of this article, is a template-based, Artificial Intelligence (AI) supported exercise generator, that aims to facilitate the creation of exercise repositories. By using a Domain-Specific Language (DSL) to define exercise templates, combined with the automatic generation of different exercise types, educators can use Goliath's features to improve their exercise repositories, both in size and variety. This systematic approach allows for greater control and automatisation than using a Large Language Model (LLM) directly, as the exercises’ main components can be pre-defined and pre-configured via their templates. Goliath, which is available online for free access, has been tested and its usability assessed. Combined with these functionalities, the content of the exercises themselves, the manner in which they are presented, and how they are rated for difficulty should also be considered in high regard when designing programming exercises. The Cognitive Load Theory (CLT) provides a conceptual foundation to understand problem-solving mechanisms that are commonly found in several aspects and situations of daily life, such as solving programming exercises. This foundation has been explored and systematically structured to construct the second main contribution of this article: guides to create exercise templates in Goliath founded on the Cognitive Load Theory, aiming to improve both teaching and learning computer programming.eng
dc.description.sponsorshipThis work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UID/00319/2023. The work of Maria João and Alvaro was supported by national funds: UID/05757 - Research Centre in Digitalization and Intelligent Robotics (CeDRI); and SusTEC, LA/P/0007/2020 (DOI:10.54499/LA/P/0007/2020).
dc.identifier.citationFreitas, Tiago Carvalho; Costa Neto, Alvaro; Pereira, Maria João; Henriques, Pedro Rangel (2025). Goliath and the Cognitive Load Theory. Journal of Computer Languages. ISSN 2590-1184. 85, p. 1-15. DOI: 10.1016/2025.101372
dc.identifier.doi10.1016/2025.101372
dc.identifier.issn2590-1184
dc.identifier.urihttp://hdl.handle.net/10198/36145
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationResearch Centre in Digitalization and Intelligent Robotics
dc.relationAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
dc.relation.ispartofJournal of Computer Languages
dc.rights.urihttp://creativecommons.org/licenses/by-nd/4.0/
dc.subjectComputer programming education
dc.subjectArtificial intelligence
dc.subjectDomain-Specific Languages
dc.subjectProgramming exercises
dc.subjectCognitive load theory
dc.titleGoliath and the cognitive load theoryeng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDP/05757/2020
oaire.awardNumberLA/P/0007/2020
oaire.awardTitleResearch Centre in Digitalization and Intelligent Robotics
oaire.awardTitleAssociate Laboratory for Sustainability and Tecnology in Mountain Regions
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F05757%2F2020/PT
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/LA%2FP%2F0007%2F2020/PT
oaire.citation.endPage15
oaire.citation.startPage1
oaire.citation.titleJournal of Computer Languages
oaire.citation.volume85
oaire.fundingStream6817 - DCRRNI ID
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamePereira
person.givenNameMaria João
person.identifier.ciencia-idC912-4A49-A3B3
person.identifier.orcid0000-0001-6323-0071
person.identifier.ridG-5999-2011
person.identifier.scopus-author-id13907870300
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublicationa20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2
relation.isAuthorOfPublication.latestForDiscoverya20ccfa6-4e84-4c25-ab0d-8d6ba196ffc2
relation.isProjectOfPublicationd0a17270-80a8-4985-9644-a04c2a9f2dff
relation.isProjectOfPublication6255046e-bc79-4b82-8884-8b52074b4384
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