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 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.

College Majors, Occupations, and the Gender Wage Gap

The paper assesses gender differences in pre-labor market specialization among the college-educated and highlights how those differences have evolved over time. Women choose majors with lower potential earnings (based on male wages associated with those majors) and subsequently sort into occupations with lower potential earnings given their major choice. These differences have narrowed over time, but recent cohorts of women still choose majors and occupations with lower potential earnings. Differences in undergraduate major choice explain a substantive portion of gender wage gaps for the college-educated above and beyond simply controlling for occupation. Collectively, our results highlight the importance of understanding gender differences in the mapping between college major and occupational sorting when studying the evolution of gender differences in labor market outcomes over time.

Student-run free clinic volunteers: who they are and what we can learn from them

Background Initiatives employing medical students’ volunteerism and idealism, such as the Student-Run Free Clinics (SRFC) program, are prevalent in US medical schools. Many studies evaluated various aspects of volunteering, sometimes resulting in conflicting evidence. This study simultaneously sought to identify the characteristics of volunteers vs. non-volunteers, and to characterize the volunteers’ perception of the SRFC. Methods We administered a survey to the Long School of Medicine (LSOM) Class of 2018 before their third year of medical school. The authors compared and contrasted the findings of the SRFC volunteers with their non-volunteering counterparts by analyzing their demographics, volunteering history, academic performance, and clinical skills. The volunteers were also asked about their SRFC experiences. Results While most volunteers were female (62 %) and non-traditional students (67 %), the difference was not statistically significant (p = 0.15 and p = 0.38, respectively). Additionally, there were no statistically significant differences between the two groups in measures of academic performance (p = 0.25). Most of the volunteers learned about the SRFC program prior to starting medical school. Further, while SRFC volunteers were more likely to engage in additional local volunteering initiatives, the difference was not statistically significant (p = 0.03, prespecified α= 0.006). Importantly, volunteers agreed/strongly agreed that SRFC volunteering emphasized aspects that were missing or underemphasized in the formal medical school curriculum. Conclusions Medical students’ age, gender, undergraduate major, and non-traditional status were not statistically different between volunteers vs. non-volunteers. However, there may be tendencies for volunteers to be female, non-traditional, and locally engaged. Further, the timing of knowledge of the SRFC program may not affect student involvement in the SRFC, either. Most importantly, however, while volunteering does not affect the students’ academic performance, it may provide improvements in clinical competencies.

Essays on machine learning applications in economics : causal inference and prediction

This study includes three chapters related to machine learning applications with focus on different empirical topics. The first chapter talks about a new method and its application. The second chapter focuses on young economics professors salary issues. While the third chapter discusses scientific paper publication values based on text analysis and gender bias. In the first Chapter, I give a discussion of Double/Debiased Machine Learning (DML) which is a causal estimation method recently created by Chernozhukov, Chetverikov, Demirer, Duo, Hansen, Newey, and Robins (2018) and apply it to an education empirical analysis. I explain why DML is practically useful and what it does; I also take a bootstrap procedure to improve the built-in DML standard errors in the curriculum adoption application. As an extension to the existing studies on how curriculum materials affect student achievement, my work compares the results of DML, kernel matching, and ordinary least squares (OLS). In my study, the DML estimators avoid the possible misspecification bias of linear models and obtain statistically significant results that improve upon the kernel matching results. In the second chapter, we analyze the effects of gender, PhD graduation school rank, and undergraduate major on young economics professors' salaries. The dataset used is novel, containing detailed and time-varying research productivity measures and other demographic information of young economics professors from 28 of the top 50 public research universities in the United States. We apply double/debiased machine learning (DML) to obtain consistent estimators under the high-dimensional control variable set. By tracking the first 10 years of their professional work experience, we find that there barely exist effects on young faculties' salaries from the above three factors in most of the experience years. However, the gender effect on salary in experience year 7 is both statistically significant and economically significant (large enough in magnitude to have a practical meaning). In experience years 5 to 7, which are also near most faculties' promotion years, the gender effects are obvious. For both PhD graduation school rank and undergraduate major, the estimates for experience years 7 to 9 are large in magnitude; however they do not possess statistical significance. Overall, the effects tend to expand with years of experience. We also discuss possible economic mechanisms and reasons. In the third chapter, we build machine learning and simple linear models to predict academic paper publication outcomes as measured by journal H-indices, and we discuss the gender bias associated with these outcomes. We use a novel dataset with paper text content and each paper's associated H-index, authors' genders, and other information, collected from recently published economics journals. We apply term frequency-inverse document frequency vectorization and other Natural Language Processing (NLP) tools to transfer text content into numerical values as model inputs. We find that when using paper text content to predict an H-index, the prediction power is around 60 [percent] in our classification model (4 tiers) and the root mean squared error is around 44 in our regression model. Moreover, when controlling for paper text, the gender causal effect hardly exists. As long as the paper contains similar text, gender does not influence the change in H-index. Additionally, we give real-world meanings associated with the models.