Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques

University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable mac...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Alhuseen Omar Alsayed, Mohd Shafry Mohd Rahim, Ibrahim AlBidewi, Mushtaq Hussain, Syeda Huma Jabeen, Nashwan Alromema, Sadiq Hussain, Muhammad Lawan Jibril
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	machine learning learning analytics student field forecasting imbalanced datasets explainable machine learning intelligent tutoring system 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/d8d2c63b4741407fb9431dc661d3a99e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:d8d2c63b4741407fb9431dc661d3a99e
record_format	dspace
spelling	oai:doaj.org-article:d8d2c63b4741407fb9431dc661d3a99e2021-11-25T16:33:39ZSelection of the Right Undergraduate Major by Students Using Supervised Learning Techniques10.3390/app1122106392076-3417https://doaj.org/article/d8d2c63b4741407fb9431dc661d3a99e2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10639https://doaj.org/toc/2076-3417University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable machine learning approaches (Decision Tree [DT], Extra tree classifiers [ETC], Random forest [RF] classifiers, Gradient boosting classifiers [GBC], and Support Vector Machine [SVM]) were tested to predict students’ right undergraduate major (field of specialization) before admission at the undergraduate level based on the current job markets and experience. The DT classifier predicts the target class based on simple decision rules. ETC is an ensemble learning technique that builds prediction models by using unpruned decision trees. RF is also an ensemble technique that uses many individual DTs to solve complex problems. GBC classifiers and produce strong prediction models. SVM predicts the target class with a high margin, as compared to other classifiers. The imbalanced dataset includes secondary school marks, higher secondary school marks, experience, and salary to select specialization for students in undergraduate programs. The results showed that the performances of RF and GBC predict the student field of specialization (undergraduate major) before admission, as well as the fact that these measures are as good as DT and ETC. Statistical analysis (Spearman correlation) is also applied to evaluate the relationship between a student’s major and other input variables. The statistical results show that higher student marks in higher secondary (hsc_p), university degree (Degree_p), and entry test (etest_p) play an important role in the student’s area of specialization, and we can recommend study fields according to these features. Based on these results, RF and GBC can easily be integrated into intelligent recommender systems to suggest a good field of specialization to university students, according to the current job market. This study also demonstrates that marks in higher secondary and university and entry tests are useful criteria to suggest the right undergraduate major because these input features most accurately predict the student field of specialization.Alhuseen Omar AlsayedMohd Shafry Mohd RahimIbrahim AlBidewiMushtaq HussainSyeda Huma JabeenNashwan AlromemaSadiq HussainMuhammad Lawan JibrilMDPI AGarticlemachine learninglearning analyticsstudent field forecastingimbalanced datasetsexplainable machine learningintelligent tutoring systemTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10639, p 10639 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning learning analytics student field forecasting imbalanced datasets explainable machine learning intelligent tutoring system Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	machine learning learning analytics student field forecasting imbalanced datasets explainable machine learning intelligent tutoring system Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Alhuseen Omar Alsayed Mohd Shafry Mohd Rahim Ibrahim AlBidewi Mushtaq Hussain Syeda Huma Jabeen Nashwan Alromema Sadiq Hussain Muhammad Lawan Jibril Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques
description	University education has become an integral and basic part of most people preparing for working life. However, placement of students into the appropriate university, college, or discipline is of paramount importance for university education to perform its role. In this study, various explainable machine learning approaches (Decision Tree [DT], Extra tree classifiers [ETC], Random forest [RF] classifiers, Gradient boosting classifiers [GBC], and Support Vector Machine [SVM]) were tested to predict students’ right undergraduate major (field of specialization) before admission at the undergraduate level based on the current job markets and experience. The DT classifier predicts the target class based on simple decision rules. ETC is an ensemble learning technique that builds prediction models by using unpruned decision trees. RF is also an ensemble technique that uses many individual DTs to solve complex problems. GBC classifiers and produce strong prediction models. SVM predicts the target class with a high margin, as compared to other classifiers. The imbalanced dataset includes secondary school marks, higher secondary school marks, experience, and salary to select specialization for students in undergraduate programs. The results showed that the performances of RF and GBC predict the student field of specialization (undergraduate major) before admission, as well as the fact that these measures are as good as DT and ETC. Statistical analysis (Spearman correlation) is also applied to evaluate the relationship between a student’s major and other input variables. The statistical results show that higher student marks in higher secondary (hsc_p), university degree (Degree_p), and entry test (etest_p) play an important role in the student’s area of specialization, and we can recommend study fields according to these features. Based on these results, RF and GBC can easily be integrated into intelligent recommender systems to suggest a good field of specialization to university students, according to the current job market. This study also demonstrates that marks in higher secondary and university and entry tests are useful criteria to suggest the right undergraduate major because these input features most accurately predict the student field of specialization.
format	article
author	Alhuseen Omar Alsayed Mohd Shafry Mohd Rahim Ibrahim AlBidewi Mushtaq Hussain Syeda Huma Jabeen Nashwan Alromema Sadiq Hussain Muhammad Lawan Jibril
author_facet	Alhuseen Omar Alsayed Mohd Shafry Mohd Rahim Ibrahim AlBidewi Mushtaq Hussain Syeda Huma Jabeen Nashwan Alromema Sadiq Hussain Muhammad Lawan Jibril
author_sort	Alhuseen Omar Alsayed
title	Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques
title_short	Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques
title_full	Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques
title_fullStr	Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques
title_full_unstemmed	Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques
title_sort	selection of the right undergraduate major by students using supervised learning techniques
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/d8d2c63b4741407fb9431dc661d3a99e
work_keys_str_mv	AT alhuseenomaralsayed selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques AT mohdshafrymohdrahim selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques AT ibrahimalbidewi selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques AT mushtaqhussain selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques AT syedahumajabeen selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques AT nashwanalromema selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques AT sadiqhussain selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques AT muhammadlawanjibril selectionoftherightundergraduatemajorbystudentsusingsupervisedlearningtechniques
_version_	1718413169841405952

Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques

Ejemplares similares