A College Major Recommendation System

Stein, Samuel A.; Weiss, Gary M.; Chen, Yi Wen; Leeds, Daniel

doi:10.1145/3383313.3418488

Cited by 8 publications

(10 citation statements)

References 4 publications

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“…Step 4 Model Deployment: In Phase four, an intelligent field of specialization system was developed with the help of a high-performance ML model (RF, GBC, and SVM) because RF, GBC, and SVM predict field specialization with high accuracy. Consequently, intelligent detection of the student field of specialization provides decision-makers, academic advisors, students, and other individuals with knowledge and person-specific information, which is intelligently filtered or presented at the appropriate time, to improve education and the student's best-fit specialization field [3,14,35,66,67]. Additionally, the proposed intelligent field of specialization system will help university admission offices in daily activities.…”

Section: Proposed Frameworkmentioning

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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Section: Proposed Frameworkmentioning

confidence: 99%

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

et al. 2021

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“…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. Some research has used the K-Nearest-Neighbor (KNN) algorithm as the highest accuracy algorithm for this classification problem [12]. In particular, the authors in [13] developed the King Abdelaziz University Recommendation System (KAURS), which is a recommendation system to predict and suggest a suitable major for students based on their abilities and marks in their preparatory year.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The validation for the system was performed using the k-fold cross-validation, which resulted in 74.79% accuracy. In addition, the researchers in [12] proposed a recommendation system that aims to improve student outcomes by suggesting a number of appropriate majors (n) utilizing the KNN approach; the researchers measured the percentage of students who had their major as the n recommended major based on students with similar courses and performance using adjusted cosine distance. However, this could not determine whether the major was suitable for the student; to confirm this, another measurement was used to check if the student's performance was at or above the average performance in this specialty.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For instance, in some research [15], the major prediction is decided based on the student's score at high school, without considering other factors such as the job market, academic history, etc. Many existing studies achieved only a low classification rate, e.g., [1,12,13,26]. Other research works have achieved high classification results; however, they depend on a complex workflow of preprocessing the data.…”

Section: Literature Reviewmentioning

confidence: 99%

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

2022

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Selecting the appropriate undergraduate program is a critical decision for students. Many elements influence this choice for secondary students, including financial, social, demographic, and cultural factors. If a student makes a poor choice, it will have implications for their academic life as well as their professional life. These implications may include having to change their major, which will cause a delay in their graduation, having a low grade-point average (GPA) in their chosen major, which will cause difficulties in finding a job, or even dropping out of university. In this paper, various supervised machine learning techniques, including Decision Tree, Random Forest, and Support Vector Machine, were investigated to predict undergraduate majors. The input features were related to the student’s academic history and the job market. We were able to recommend the program that guarantees both a high academic degree and employment, depending on previous data and experience, for Master of Business Administration (MBA) students. This research was conducted based on a published research and using the same dataset and aimed to improve the results by applying hyper-tuning, which was absent in previous research. The obtained results showed that our work outperformed the work of the published research, where the random forest exceeded the other classification techniques and reached an accuracy of 97.70% compared to 75.00% on the published research. The importance of features was also investigated, and it was found that the degree percentage, MBA percentage, and entry test result were the top contributing features to the model.

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“…This workload leaves them little time to address students' needs, let alone provide personalized advice on college major decisions to large numbers of students. An AI-based recommender system has the potential to mine a large amount of student data to gain deep insight into a student's interests and personality to provide more accurate and personalized college major recommendations [Stein et al 2020;Yadalam et al 2020].…”

Section: Introductionmentioning

confidence: 99%

When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation

Wang,

Bian

et al. 2023

ACM Trans. Interact. Intell. Syst.

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Currently, there is a surge of interest in fair Artificial Intelligence (AI) and Machine Learning (ML) research which aims to mitigate discriminatory bias in AI algorithms, e.g. along lines of gender, age, and race. While most research in this domain focuses on developing fair AI algorithms, in this work, we examine the challenges which arise when humans and fair AI interact. Our results show that due to an apparent conflict between human preferences and fairness, a fair AI algorithm on its own may be insufficient to achieve its intended results in the real world. Using college major recommendation as a case study, we build a fair AI recommender by employing gender debiasing machine learning techniques. Our offline evaluation showed that the debiased recommender makes fairer career recommendations without sacrificing its accuracy in prediction. Nevertheless, an online user study of more than 200 college students revealed that participants on average prefer the original biased system over the debiased system. Specifically, we found that perceived gender disparity is a determining factor for the acceptance of a recommendation. In other words, we cannot fully address the gender bias issue in AI recommendations without addressing the gender bias in humans. We conducted a follow-up survey to gain additional insights into the effectiveness of various design options that can help participants to overcome their own biases. Our results suggest that making fair AI explainable is crucial for increasing its adoption in the real world.

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A College Major Recommendation System

Cited by 8 publications

References 4 publications

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

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

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

When Biased Humans Meet Debiased AI: A Case Study in College Major Recommendation

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