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

Abstract

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.

A Recommendation System for Selecting the Appropriate Undergraduate Program at Higher Education Institutions Using Graduate Student Data

“…Many research studies have been conducted support students in their decision to select the appropriate major at university [3]. This is achieved by introducing recommendations and decision support systems based on different supervised ML techniques and based on student data, such as academic history, absences, and tendencies.…”

“…Students use internet searches and friends' recommendations in their decisions. Moreover, the socio-economic background of the family is a great influence [3]. Typically, people seek guidance and support from teachers, relatives, and coworkers.…”

“…Sci. 2022, 12, 12525 2 of 14 United States do not return after their first year due to the wrong major choice, costing taxpayers over USD 9 billion per year [3]. Statistical evidence has revealed that many students continue to fail in their university courses despite receiving family support and having good work ethic; this failure is linked failure to select a suitable faculty/major that is appropriate for their abilities and skills [4].…”

CNN-LSTM deep learning based forecasting model for COVID-19 infection cases in Nigeria, South Africa and Botswana

“…1 Methodology stacked on top of each other [21]. The convolutional layer shares a lot of weight while the composite layers soften the convolutional layer release and reduce the amount of data rate from the lower layer [12]. The sharing of weight in the convolutional layer together with appropriate chosen pooling schemes, endows the CNN with some invariance properties [7,[22][23][24].CNN has been found to be one of the most effective DL vision algorithms used for image recognition and computer vision [25,26].…”

“…Hence, many different artificial intelligence, statistical and mathematical approaches have been developed for predicting and forecasting the severity and spread of COVID-19 pandemic in many countries [10][11][12]. In the work of [1] LSTM was designed for predicting either 1 or 7 days, or both of COVID-19 cases.…”