A Data Mining Model for Students’ Choice of College Major Based on Rough Set Theory

Al-Shalabi, Luai

doi:10.3844/jcssp.2019.1150.1160

Cited by 2 publications

(2 citation statements)

References 25 publications

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“…37 Conversely, Al-Shalabi emphasized personal academic prowess in program selection. 38 Our findings diverge from Khoi and Cuong's results, suggesting no correlation between students' abilities and university choices. 39 However, our study aligns with Tandrayen-Ragoobur and Gokulsing, highlighting high school academic performance as a consideration.…”

Section: Figure 1 the Results Of The Proposed Frameworkcontrasting

confidence: 99%

The Perceived Utility and University Enrolment Intention in Indonesia: Students Perspective

Juhaidi,

Ma'ruf,

Tajudin

et al. 2023

Cendekia J. Education and Society

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This study investigates the difference between the utility of general higher education (GHE) and Islamic higher education (IHE), students' enrolment intention, and their correlation. Data were collected using questionnaires. The participants of this study were 753 students in South Kalimantan, Indonesia. Researchers used convenience and snowballing sampling techniques. Data analysis used the Wilcoxon Signed Rank Test and Spearman's correlation. This study proves that the level of utility and all its factors (economic and non-economic benefit, probability of success, and cost) differ significantly between GHE and IHE (Sig<0.05). Moreover, it was found that all factors correlate with enrolment intention (Sig<0.05). However, correlation coefficients indicate that the cost of GHE and IHE is the factor with the weakest correlation (p=0,139 and p=0.084) with enrolment intention. The strongest correlation with enrolment intention is the utility of GHE and IHE (p=0,597 and p=0.579) and the possibility of success in GHE and IHE(p=0,614 and p=0.530). This study offers practical recommendations for higher education institutions and suggests future research.

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Section: Figure 1 the Results Of The Proposed Frameworkcontrasting

confidence: 99%

The Perceived Utility and University Enrolment Intention in Indonesia: Students Perspective

Juhaidi,

Ma'ruf,

Tajudin

et al. 2023

Cendekia J. Education and Society

View full text Add to dashboard Cite

show abstract

“…One essential goal of universities is to aid student admission into their desired college specialization. What student admission means varies depending on the context of the university requirements, students' academic results, and other related factors [11,12]. Universities provide student admission centers and student counselors or advisors to help students meet their educational needs.…”

Section: Student Field Specialization (Undergraduate Major Course)mentioning

confidence: 99%

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

et al. 2021

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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.

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A Data Mining Model for Students’ Choice of College Major Based on Rough Set Theory

Cited by 2 publications

References 25 publications

The Perceived Utility and University Enrolment Intention in Indonesia: Students Perspective

The Perceived Utility and University Enrolment Intention in Indonesia: Students Perspective

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

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