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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10546 |
| _version_ | 1827677518658797568 |
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| 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 |
| collection | DOAJ |
| 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 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 display="inline"><semantics><mrow><mn>0.846</mn></mrow></semantics></math></inline-formula> for Extraversion and <inline-formula><math 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 Cohen's Kappa Coefficient (<inline-formula><math display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula>) of students at risk of <inline-formula><math 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. |
| first_indexed | 2024-03-10T05:44:09Z |
| format | Article |
| id | doaj.art-64355bc4eab74c7ab1cacb315ceb7599 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:44:09Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-64355bc4eab74c7ab1cacb315ceb75992023-11-22T22:15:19ZengMDPI AGApplied Sciences2076-34172021-11-0111221054610.3390/app112210546Modeling E-Behaviour, Personality and Academic Performance with Machine LearningSerepu Bill-William Seota0Richard Klein1Terence van Zyl2School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2001, South AfricaSchool of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg 2001, South AfricaInstitute for Intelligent Systems, University of Johannesburg, Johannesburg 2092, South AfricaThe 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 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 display="inline"><semantics><mrow><mn>0.846</mn></mrow></semantics></math></inline-formula> for Extraversion and <inline-formula><math 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 Cohen's Kappa Coefficient (<inline-formula><math display="inline"><semantics><mi>κ</mi></semantics></math></inline-formula>) of students at risk of <inline-formula><math 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.https://www.mdpi.com/2076-3417/11/22/10546e-behaviourbig five personalitystudent performance |
| spellingShingle | Serepu Bill-William Seota Richard Klein Terence van Zyl Modeling E-Behaviour, Personality and Academic Performance with Machine Learning Applied Sciences e-behaviour big five personality student performance |
| title | 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_short | Modeling E-Behaviour, Personality and Academic Performance with Machine Learning |
| title_sort | modeling e behaviour personality and academic performance with machine learning |
| topic | e-behaviour big five personality student performance |
| url | https://www.mdpi.com/2076-3417/11/22/10546 |
| work_keys_str_mv | AT serepubillwilliamseota modelingebehaviourpersonalityandacademicperformancewithmachinelearning AT richardklein modelingebehaviourpersonalityandacademicperformancewithmachinelearning AT terencevanzyl modelingebehaviourpersonalityandacademicperformancewithmachinelearning |