Selection of the Right Undergraduate Major by Students Using Supervised Learning Techniques
| Autor: | Nashwan Alromema, Syeda Huma Jabeen, Muhammad Lawan Jibril, Ibrahim Albidewi, Mushtaq Hussain, Mohd Shafry Mohd Rahim, Sadiq Hussain, Alhuseen Omar Alsayed |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Technology
QH301-705.5 Computer science QC1-999 Decision tree intelligent tutoring system Machine learning computer.software_genre machine learning learning analytics student field forecasting imbalanced datasets explainable machine learning Margin (machine learning) General Materials Science Biology (General) QD1-999 Instrumentation Fluid Flow and Transfer Processes business.industry Physics Process Chemistry and Technology Supervised learning General Engineering Decision rule Engineering (General). Civil engineering (General) Ensemble learning Computer Science Applications Random forest Support vector machine Chemistry Gradient boosting Artificial intelligence TA1-2040 business computer |
| Zdroj: | Applied Sciences; Volume 11; Issue 22; Pages: 10639 Applied Sciences, Vol 11, Iss 10639, p 10639 (2021) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app112210639 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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