Modeling E-Behaviour, Personality and Academic Performance with Machine Learning

The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. T...

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Autores principales:	Serepu Bill-William Seota, Richard Klein, Terence van Zyl
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	e-behaviour big five personality student performance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/64355bc4eab74c7ab1cacb315ceb7599
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spelling	oai:doaj.org-article:64355bc4eab74c7ab1cacb315ceb75992021-11-25T16:31:12ZModeling E-Behaviour, Personality and Academic Performance with Machine Learning10.3390/app1122105462076-3417https://doaj.org/article/64355bc4eab74c7ab1cacb315ceb75992021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10546https://doaj.org/toc/2076-3417The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.05</mn></mrow></semantics></math></inline-formula>) population correlation coefficients for traits against grade—<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.846</mn></mrow></semantics></math></inline-formula> for Extraversion and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.319</mn></mrow></semantics></math></inline-formula> for Conscientiousness. Furthermore, we demonstrate that a student’s e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a classification accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula>) of students at risk of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.51</mn></mrow></semantics></math></inline-formula>. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education.Serepu Bill-William SeotaRichard KleinTerence van ZylMDPI AGarticlee-behaviourbig five personalitystudent performanceTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10546, p 10546 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	e-behaviour big five personality student performance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	e-behaviour big five personality student performance Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Serepu Bill-William Seota Richard Klein Terence van Zyl Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
description	The analysis of student performance involves data modelling that enables the formulation of hypotheses and insights about student behaviour and personality. We extract online behaviours as proxies to Extraversion and Conscientiousness, which have been proven to correlate with academic performance. The proxies of personalities we obtain yield significant (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>p</mi><mo><</mo><mn>0.05</mn></mrow></semantics></math></inline-formula>) population correlation coefficients for traits against grade—<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.846</mn></mrow></semantics></math></inline-formula> for Extraversion and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.319</mn></mrow></semantics></math></inline-formula> for Conscientiousness. Furthermore, we demonstrate that a student’s e-behaviour and personality can be used with deep learning (LSTM) to predict and forecast whether a student is at risk of failing the year. Machine learning procedures followed in this report provide a methodology to timeously identify students who are likely to become at risk of poor academic performance. Using engineered online behaviour and personality features, we obtain a classification accuracy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula>) of students at risk of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>0.51</mn></mrow></semantics></math></inline-formula>. Lastly, we show that we can design an intervention process using machine learning that supplements the existing performance analysis and intervention methods. The methodology presented in this article provides metrics that measure the factors that affect student performance and complement the existing performance evaluation and intervention systems in education.
format	article
author	Serepu Bill-William Seota Richard Klein Terence van Zyl
author_facet	Serepu Bill-William Seota Richard Klein Terence van Zyl
author_sort	Serepu Bill-William Seota
title	Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
title_short	Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
title_full	Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
title_fullStr	Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
title_full_unstemmed	Modeling E-Behaviour, Personality and Academic Performance with Machine Learning
title_sort	modeling e-behaviour, personality and academic performance with machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/64355bc4eab74c7ab1cacb315ceb7599
work_keys_str_mv	AT serepubillwilliamseota modelingebehaviourpersonalityandacademicperformancewithmachinelearning AT richardklein modelingebehaviourpersonalityandacademicperformancewithmachinelearning AT terencevanzyl modelingebehaviourpersonalityandacademicperformancewithmachinelearning
_version_	1718413139104497664

Modeling E-Behaviour, Personality and Academic Performance with Machine Learning

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