# Fair and Transparent Student Admission Prediction Using Machine Learning Models
## 2 Literature Review
### Student Admissions Through Machine Learning Models
(2018) presents a comprehensive literature review on the application of predictive analytics in university admissions. The authors identified key research areas, including admission outcomes, academic performance, admission yield, admission likelihood, and appropriate field of study selection. Their findings emphasize the potential of predictive analytics to improve university admissions processes and highlight the need for further research at the intersection of predictive techniques and student retention strategies within higher education. [PERSON] et al. (2018) introduced a novel two-stage ensemble model that combines Probabilistic Neural Networks (PNNs) and Support Vector Machines (SVMs) to predict admissions in higher education. The proposed model leverages PNNs to expand the dataset by estimating class probabilities and employs a stacking ensemble of SVMs with various kernel functions, using Logistic Regression as the meta-algorithm. The results indicate that this model outperforms existing machine learning methods in admission prediction accuracy. In their study, [PERSON] and [PERSON] (2018) applied machine learning models to predict student admissions outcomes at a large urban research university. Their findings indicate that these predictive models disproportionately affected under-represented groups.This research contributes critical insights into the ethical and practical challenges of implementing predictive analytics in admissions, emphasizing the need for bias mitigation to ensure fairness and equity across diverse student demographics. [PERSON] and his colleagues ([PERSON] and [PERSON], 2018) aimed to develop machine learning models to improve admissions predictions for two STEM graduate programs, with a particular focus on the role of letters of recommendation (LORs). Their methodology involved analyzing 717 applications and included data from structured application forms, resumes, and LORs. Feature extraction incorporated demographic data, academic credentials, GRE scores, and natural language processing (NLP) to evaluate LORs' sentiment and tone. The study's findings indicate that while LORs alone have limited predictive power, integrating LOR data with other applicant information significantly enhances model accuracy, underscoring the value of combining qualitative and quantitative data in admissions predictions. From this brief literature review, study ([PERSON] and [PERSON], 2018) is among the first to reveal significant biases related to gender, race, and first-generation status when machine learning models are applied to student admissions processes. [PERSON] et al. (2018) presented a pivotal study emphasizing the necessity of addressing fairness concerns in algorithmic decision-making (ADM) within higher education. Their research examined perceptions of fairness in ADM as compared to human decision-making (HDM) in university admissions, using survey data collected from students at a major German university. Their findings indicate that ADM is perceived as fairer than HDM in terms of both procedural and distributive fairness. This study highlights the critical role of fairness in ADM, particularly in maintaining student trust and protecting institutional reputation. The preceding literature review, while not conducted as a systematic one, reveals a significant gap in current research: there is a marked shortage of studies that rigorously investigate the integration of fairness-aware machine learning algorithms within student admissions processes. This absence of research suggests that the critical intersection of algorithmic fairness and admissions decisions remains underexplored, potentially leaving biases and inequities unaddressed in the deployment of machine learning systems in educational settings.