Predicting academic performance: executive functions in university environments

This study aims to investigate the relationship between academic performance (measured by students’ grades) and executive function (EF) skills, a set of high-level cognitive skills that enable individuals to regulate their thoughts, emotions, and actions in goal-directed behavior. Executive function...

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Detalles Bibliográficos
Autor: Borreguero Ruiz, Albert
Tipo de recurso: tesis de maestría
Fecha de publicación:2025
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/445085
Acceso en línea:https://hdl.handle.net/2117/445085
Access Level:acceso abierto
Palabra clave:Academic achievement
Executive functions (Neuropsychology)
Cognitive psychology
Rendiment escolar
Funció executiva (Neuropsicologia)
Psicologia de la cognició
Àrees temàtiques de la UPC::Matemàtiques i estadística
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spelling Predicting academic performance: executive functions in university environmentsBorreguero Ruiz, AlbertAcademic achievementExecutive functions (Neuropsychology)Cognitive psychologyRendiment escolarFunció executiva (Neuropsicologia)Psicologia de la cognicióÀrees temàtiques de la UPC::Matemàtiques i estadísticaThis study aims to investigate the relationship between academic performance (measured by students’ grades) and executive function (EF) skills, a set of high-level cognitive skills that enable individuals to regulate their thoughts, emotions, and actions in goal-directed behavior. Executive functions include cognitive domains such as inhibition (the ability to suppress impulsive responses), attentional control (the capacity to maintain focus), working memory (the temporary storage and manipulation of information), cognitive flexibility (the ability to shift between tasks or strategies), and sustained attention (the capacity to maintain focus over time). These components were assessed through standardized and computerized cognitive tasks (programmed in PsychoPy and deployed via Pavlovia) administered to students of the Escola Tecnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB, UPC). The collected data consisted of behavioral performance metrics (e.g., reaction time and accuracy on the tasks) and academic grades, and were analyzed through three types of predictive models: Linear Regression, Random Forest, and Artificial Neural Networks (ANNs). Principal Component Analysis (PCA) was used to reduce dimensionality and improve model interpretability. Results showed that linear models, especially when combined with PCA, outperformed more complex approaches. Variables from the Wisconsin Card Sorting Task and the Stroop Task, measuring cognitive flexibility and attentional control, were the most predictive of academic grades. In contrast, working memory measures showed weaker associations. Despite initial setbacks, the research confirms that EF components are meaningful predictors of academic outcomes in analytical subjects such as statistics and data science. The findings emphasize the importance of aligning model complexity with data quality and offer insights for future studies in educational data mining.Universitat Politècnica de CatalunyaCastellano Palomino, Marta Janira20252025-06-0120252025-10-31master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/445085reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4450852026-05-27T15:37:01Z
dc.title.none.fl_str_mv Predicting academic performance: executive functions in university environments
title Predicting academic performance: executive functions in university environments
spellingShingle Predicting academic performance: executive functions in university environments
Borreguero Ruiz, Albert
Academic achievement
Executive functions (Neuropsychology)
Cognitive psychology
Rendiment escolar
Funció executiva (Neuropsicologia)
Psicologia de la cognició
Àrees temàtiques de la UPC::Matemàtiques i estadística
title_short Predicting academic performance: executive functions in university environments
title_full Predicting academic performance: executive functions in university environments
title_fullStr Predicting academic performance: executive functions in university environments
title_full_unstemmed Predicting academic performance: executive functions in university environments
title_sort Predicting academic performance: executive functions in university environments
dc.creator.none.fl_str_mv Borreguero Ruiz, Albert
author Borreguero Ruiz, Albert
author_facet Borreguero Ruiz, Albert
author_role author
dc.contributor.none.fl_str_mv Castellano Palomino, Marta Janira
dc.subject.none.fl_str_mv Academic achievement
Executive functions (Neuropsychology)
Cognitive psychology
Rendiment escolar
Funció executiva (Neuropsicologia)
Psicologia de la cognició
Àrees temàtiques de la UPC::Matemàtiques i estadística
topic Academic achievement
Executive functions (Neuropsychology)
Cognitive psychology
Rendiment escolar
Funció executiva (Neuropsicologia)
Psicologia de la cognició
Àrees temàtiques de la UPC::Matemàtiques i estadística
description This study aims to investigate the relationship between academic performance (measured by students’ grades) and executive function (EF) skills, a set of high-level cognitive skills that enable individuals to regulate their thoughts, emotions, and actions in goal-directed behavior. Executive functions include cognitive domains such as inhibition (the ability to suppress impulsive responses), attentional control (the capacity to maintain focus), working memory (the temporary storage and manipulation of information), cognitive flexibility (the ability to shift between tasks or strategies), and sustained attention (the capacity to maintain focus over time). These components were assessed through standardized and computerized cognitive tasks (programmed in PsychoPy and deployed via Pavlovia) administered to students of the Escola Tecnica Superior d’Enginyeria Industrial de Barcelona (ETSEIB, UPC). The collected data consisted of behavioral performance metrics (e.g., reaction time and accuracy on the tasks) and academic grades, and were analyzed through three types of predictive models: Linear Regression, Random Forest, and Artificial Neural Networks (ANNs). Principal Component Analysis (PCA) was used to reduce dimensionality and improve model interpretability. Results showed that linear models, especially when combined with PCA, outperformed more complex approaches. Variables from the Wisconsin Card Sorting Task and the Stroop Task, measuring cognitive flexibility and attentional control, were the most predictive of academic grades. In contrast, working memory measures showed weaker associations. Despite initial setbacks, the research confirms that EF components are meaningful predictors of academic outcomes in analytical subjects such as statistics and data science. The findings emphasize the importance of aligning model complexity with data quality and offer insights for future studies in educational data mining.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-06-01
2025
2025-10-31
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/445085
url https://hdl.handle.net/2117/445085
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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