Data Mining Framework to Analyze the Evolution of Computational Thinking Skills in Game Building Workshops

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dc.contributor.authorDe Souza A.A.
dc.contributor.authorBarcelos T.S.
dc.contributor.authorMunoz R.
dc.contributor.authorVillarroel R.
dc.contributor.authorSilva L.A.
dc.date.accessioned2024-03-12T23:55:09Z
dc.date.available2024-03-12T23:55:09Z
dc.date.issued2019
dc.description.abstract© 2013 IEEE.Computational thinking has become a required capability in the student learning process, and digital games as a teaching approach have presented promising educational results in the development of this competence. However, properly evaluating the effectiveness and, consequently, student progress in a course using games is still a challenge. One of the most widely implemented ways of evaluation is with an automated analysis of the code developed in the classes during the construction of digital games. Nevertheless, this topic has not yet been explored in aspects such as incremental learning, the model and teaching environment and the influences of acquiring skills and competencies of computational thinking. Motivated by this knowledge gap, this paper introduces a framework proposal to analyze the evolution of computational thinking skills in digital games classes. The framework is based on a data mining technique that aims to facilitate the discovery process of the patterns and behaviors that lead to the acquisition of computational thinking skills, by analyzing clusters with an unsupervised neural network of self-organizing maps (SOM) for this purpose. The framework is composed of a collection of processes and practices structured in data collection, data preprocessing, data analysis, and data visualization. A case study, using Scratch, was executed to validate this approach. The results point to the viability of the framework, highlighting the use of the visual exploratory data analysis, through the SOM maps, as an efficient tool to observe the acquisition of computational thinking skills by the student in an incremental course.
dc.description.firstpage82848
dc.description.lastpage82866
dc.description.volume7
dc.identifier.doi10.1109/ACCESS.2019.2924343
dc.identifier.issn2169-3536
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/35375
dc.relation.ispartofIEEE Access
dc.rightsAcesso Aberto
dc.subject.otherlanguageAnalytical models
dc.subject.otherlanguagecomputer science education
dc.subject.otherlanguagedata mining
dc.subject.otherlanguagedata models
dc.subject.otherlanguageeducation
dc.subject.otherlanguagegames
dc.subject.otherlanguageself-organizing feature maps
dc.titleData Mining Framework to Analyze the Evolution of Computational Thinking Skills in Game Building Workshops
dc.typeArtigo
local.scopus.citations20
local.scopus.eid2-s2.0-85069776412
local.scopus.subjectComputational thinkings
local.scopus.subjectComputer Science Education
local.scopus.subjectData mining frameworks
local.scopus.subjectExploratory data analysis
local.scopus.subjectgames
local.scopus.subjectIncremental learning
local.scopus.subjectStudent learning process
local.scopus.subjectUnsupervised neural networks
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85069776412&origin=inward
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