Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability?
A machine learning approach to predict university attributes that influence graduate employability is presented in this work. The machine learning technique used here is the hyperbox model, which is based on the principle of generating if / then rules to predict outcomes. The rule-based hyperbox mod...
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| Format: | Article |
| Language: | English |
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AIDIC Servizi S.r.l.
2020-08-01
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| Series: | Chemical Engineering Transactions |
| Online Access: | https://www.cetjournal.it/index.php/cet/article/view/11055 |
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doaj-652fdbb0b26c4d6e840cf9d4733d4d4e2021-02-16T11:27:56ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162020-08-018110.3303/CET2081114Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability?Kathleen B. AvisoJose Isagani B. JanairoRochelle Irene G. LucasMichael Angelo B. PromentillaDerrick Ethelbhert C. YuRaymond R. TanA machine learning approach to predict university attributes that influence graduate employability is presented in this work. The machine learning technique used here is the hyperbox model, which is based on the principle of generating if / then rules to predict outcomes. The rule-based hyperbox model can be generated from empirical data using a mixed integer linear programming model. This machine learning approach is applied to the problem of predicting employability of chemical engineering graduates based on institutional attributes. The analysis shows that research intensity and quality do not necessarily result in high employability.https://www.cetjournal.it/index.php/cet/article/view/11055 |
| collection |
DOAJ |
| language |
English |
| format |
Article |
| sources |
DOAJ |
| author |
Kathleen B. Aviso Jose Isagani B. Janairo Rochelle Irene G. Lucas Michael Angelo B. Promentilla Derrick Ethelbhert C. Yu Raymond R. Tan |
| spellingShingle |
Kathleen B. Aviso Jose Isagani B. Janairo Rochelle Irene G. Lucas Michael Angelo B. Promentilla Derrick Ethelbhert C. Yu Raymond R. Tan Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability? Chemical Engineering Transactions |
| author_facet |
Kathleen B. Aviso Jose Isagani B. Janairo Rochelle Irene G. Lucas Michael Angelo B. Promentilla Derrick Ethelbhert C. Yu Raymond R. Tan |
| author_sort |
Kathleen B. Aviso |
| title |
Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability? |
| title_short |
Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability? |
| title_full |
Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability? |
| title_fullStr |
Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability? |
| title_full_unstemmed |
Predicting Higher Education Outcomes with Hyperbox Machine Learning: What Factors Influence Graduate Employability? |
| title_sort |
predicting higher education outcomes with hyperbox machine learning: what factors influence graduate employability? |
| publisher |
AIDIC Servizi S.r.l. |
| series |
Chemical Engineering Transactions |
| issn |
2283-9216 |
| publishDate |
2020-08-01 |
| description |
A machine learning approach to predict university attributes that influence graduate employability is presented in this work. The machine learning technique used here is the hyperbox model, which is based on the principle of generating if / then rules to predict outcomes. The rule-based hyperbox model can be generated from empirical data using a mixed integer linear programming model. This machine learning approach is applied to the problem of predicting employability of chemical engineering graduates based on institutional attributes. The analysis shows that research intensity and quality do not necessarily result in high employability. |
| url |
https://www.cetjournal.it/index.php/cet/article/view/11055 |
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