Search Results (51)

Students’ profiles are influenced by a variety of contextual variables. Over a period of eleven years, a variety of these variables was selected to determine why some students perform better than others. The main purpose of this paper is to record and report on the results of the investigation. The main research task was to document combinations of variables and admission requirements according to students’ profiles (N = 9035), and to identify trends and possible patterns contributing to the success of educational studies. The integrated theoretical lens aligns with South Africa’s national focus on equity, access, and success, providing insight into how institutional practices and student diversity intersect. A quantitative research method was used. The study concluded that unique combinations of variables contribute to first-year accounting students’ success. The value of the study contributes to student profiles, in particular, gender, race, marks and subject choices, pre-university knowledge and admission requirements that could predict student success. Full article

University admission exams can be understood as information systems in which subject areas act as components intended to convey predictive signals about students’ future academic performance. However, the informational effectiveness of these subject areas is rarely evaluated using data-driven approaches. This study proposes a data-driven framework for assessing the informational effectiveness of admission exam subject areas by analyzing their empirical relationships with subsequent academic performance. Institutional data of 2197 students across 33 undergraduate programs from two cohorts after four semesters of study are used. Each academic program is represented as a vector of correlations linking performance in admission subject areas to long-term academic outcomes. The importance of each subject area in the admission exam is contrasted with empirically observed correlations to identify mismatches in informational effectiveness. Additionally, similarity analysis is applied to uncover affinities among academic programs. The results reveal substantial heterogeneity in the informational effectiveness of admission exam subject areas, indicating that predefined subject-area weightings do not consistently reflect their empirical contribution. Similarity patterns further identify groups of programs, suggesting opportunities for program-specific optimization of admission exam design. The proposed framework provides a replicable approach for evaluating and refining admission exams as information systems, contributing to data-driven decision-making in educational assessment design. Full article

Veterinary education is academically demanding and emotionally intensive, affecting student performance, well-being, and long-term professional development. This narrative review synthesizes current evidence on academic stressors, admissions predictors, coping mechanisms, and institutional responses in veterinary training. Cognitive indicators such as Grade Point Average (GPA) and standardized test scores reliably predict early performance in pre-clinical biomedical courses. However, these measures do not adequately capture essential non-cognitive attributes, including resilience, adaptability, motivation, and communication skills, which are critical for sustained success in clinical environments. Holistic admission approaches show promise but remain inconsistently validated across institutions. Academic stress in veterinary programs arises from heavy curricular loads, frequent high-stakes assessments, financial pressures, and transitions into clinical training. Persistent stress exposure is associated with anxiety, depressive symptoms, and burnout risk. Evidence suggests that structured wellness initiatives, peer mentoring, and resilience-building programs can mitigate these effects when embedded systematically within the curriculum. Current literature is largely cross-sectional and geographically concentrated in Western educational contexts, limiting causal inference and generalizability. Longitudinal, multi-institutional research linking admissions profiles to academic trajectories and psychological outcomes is needed. Integrating cognitive and non-cognitive evaluation with sustained institutional support may enhance retention, academic performance, and professional preparedness in veterinary education. Full article

This dataset contains detailed information on student trajectories and dropout factors at a Spanish technological university offering Science, Technology, Engineering, Arts, and Mathematics programs. The data comprise demographic, socioeconomic, and academic variables for all enrolled students, including those in bachelor’s, master’s, doctoral, and lifelong learning programs, across three complete academic years, excluding periods affected by the SARS-CoV-2 pandemic. The data were collected and standardized from disjointed internal data sources, and fully anonymized. The dataset contains information about 39,364 students, 4989 courses in 163 degrees, and 77 variables related to admission pathways, academic performance indicators, socio-demographic background, digital activity in the Learning Management System, and Wi-Fi access records. Each of the 464,739 records corresponds to a course enrolment per student per year, enabling longitudinal analyses of academic progression and dropout. This data has the potential to be reused to support research on factors influencing student retention, allow for the development of predictive models to identify students at risk of leaving their studies, and offer a resource for comparative studies in higher education. Full article

For decades, the U.S. college admissions process has utilized standardized exams as critical indicators of college readiness. With the onset of the COVID pandemic, the majority of 4-year universities implemented the Test-Optional policy to improve college access and enrollment. The Test-Optional policy allows prospective high school students to apply to institutions that have implemented this policy without a SAT or ACT score. This study examined the use of the Test-Optional policy and its relationship with early college success. Forward multiple regression examined which variables of High School GPA, Students of Color, First-Generation Status, Test-Optional, Pell Eligible, and Pre-College Credits best predict undergraduate first-year GPA. The results generated a five-variable model that accounted for 31% of the variability in first-year college GPA. High School GPA was the strongest predictor, while Test-Optional was not entered into the model. Binary logistic regression examined predictors of first-year college completion. Our results revealed the model including High School GPA, which tripled the odds of first-year completion. Again, Test-Optional was not included in the model. Although Students of Color and Pell Eligibility utilized Test-Optional significantly more than their peers, Test-Optional was not a significant predictor of first-year College GPA or first-year completion. Full article

Background and Objectives: Immunodeficiency associated with aging comorbidities increases the vulnerability of geriatric patients to sepsis. Early recognition and management of sepsis are essential in this population. This study evaluated the relationships between RDW, MPV, and MPV/PLT ratios and mortality in geriatric sepsis patients. Materials and Methods: This retrospective study was conducted between 2020 and 2024 in the Intensive Care Unit of the Department of Anesthesiology and Reanimation at a university hospital. Patients aged ≥ 65 years with a SOFA score of ≥2 were included. Demographic data (sex, age, height, weight, and BMI), hemogram parameters (RDW, MPV, and PLT), blood gas, and biochemical values were analyzed. Furthermore, their comorbidities; site of infection; ICU length of stay; vital signs; and SOFA, APACHE II, and SAPS II scores, recorded within the first 24 h following ICU admission, were evaluated. Statistical analysis was performed using the chi-square test, Student’s t-test, the Mann–Whitney U test, the Monte Carlo exact test, and ROC analysis. A p-value of <0.05 was considered statistically significant. Results: A total of 247 patients were included, with 46.2% (n = 114) classified as non-survivors during the 28-day follow-up period. Among them, 64.9% (n = 74) were male, with a mean age of 78.22 ± 8.53 years. Significant differences were also found in SOFA, APACHE-II, and SAPS-II scores between non-survivors and survivors (SOFA: 7.64 ± 3.16 vs. 6.78 ± 2.78, p = 0.023; APACHE-II: 21.31 ± 6.36 vs. 19.27 ± 5.88, p = 0.009; SAPS-II: 53.15 ± 16.04 vs. 46.93 ± 14.64, p = 0.002). On days 1, 3, and 5, the MPV/PLT ratio demonstrated a statistically significant predictive value for 28-day mortality. The optimal cut-off values were >0.03 on day 1 (AUC: 0.580, 95% CI: 0.516–0.642, sensitivity: 72.81%, specificity: 65.91%, p = 0.027), >0.04 on day 3 (AUC: 0.602, 95% CI: 0.538–0.663, sensitivity: 60.53%, specificity: 60.61%, p = 0.005), and >0.04 on day 5 (AUC: 0.618, 95% CI: 0.554–0.790, sensitivity: 66.14%, specificity: 62.88%, p = 0.001). Conclusions: The MPV and MPV/PLT ratios demonstrated statistically significant but limited predictive value for 28-day mortality in geriatric patients with sepsis. In contrast, the limited prognostic value of RDW may be related to variability in the inflammatory response and other underlying conditions. The correlations found between SOFA, APACHE II, and SAPS II scores highlight their importance in mortality risk prediction. Full article

Bias and fairness issues in artificial intelligence (AI) algorithms are major concerns, as people do not want to use software they cannot trust. Because these issues are intrinsically subjective and context-dependent, creating trustworthy software requires human input and feedback. (1) Introduction: This work introduces an interactive method for mitigating the bias introduced by machine learning models by allowing the user to adjust bias and fairness metrics iteratively to make the model more fair in the context of undergraduate student admissions. (2) Related Work: The social implications of bias in AI systems used in education are nuanced and can affect university reputation and student retention rates motivating a need for the development of fair AI systems. (3) Methods and Dataset: Admissions data over six years from a large urban research university was used to create AI models to predict admissions decisions. These AI models were analyzed to detect biases they may carry with respect to three variables chosen to represent sensitive populations: gender, race, and first-generation college students. We then describe a method for bias mitigation that uses a combination of machine learning and user interaction. (4) Results and Discussion: We use three scenarios to demonstrate that this interactive bias mitigation approach can successfully decrease the biases towards sensitive populations. (5) Conclusion: Our approach allows the user to examine a model and then iteratively and incrementally adjust bias and fairness metrics to change the training dataset and generate a modified AI model that is more fair, according to the user’s own determination of fairness. Full article

The medical profession is a prestigious position that requires very extensive higher education, to which only a small proportion of applicants are accepted. Changes in selection criteria can profoundly impact applicants’ pre-educational choices, early medical studies, and the characteristics of future medical professionals. This study assesses the impact of changing the admission requirements of medical schools in Finland. We examined two cohorts of students admitted to the University of Oulu’s medical school: 2009–2011 (n = 316) and 2013–2015 (n = 339). The first cohort prepared for the entrance exam with a field-specific book, while the second cohort focused on secondary school subjects such as biology, chemistry, and physics. We analysed the effects of the changes on accepted students’ profiles and the relationship between their prior performance, entrance exam success, and performance in medical studies. Changing the entrance exam content did not significantly alter accepted students’ profiles or ease access for recent matriculants. However, minor changes in correlations between prior performance, entrance exam performance, and medical study success were observed. The entrance exam’s predictive power for academic success was weak in both admission periods. This comparative study found that changing the entrance exam material did not notably influence the characteristics of accepted students. The changes to the selection criteria appear to have a minor impact on the actual success of students studying medicine. Regardless of the selection criteria, those who are accepted typically demonstrate strong learning capabilities. Despite modifications in the required entry-level knowledge, students with strong skills are admitted. Full article

University resources are limited, and strategic admission management is required in certain fields that have high application volumes but limited available study places. Student admission processes need to select an appropriate number of applicants to ensure the optimal enrollment while avoiding over- or underenrollment. The traditional approach often relies on the enrollment yields from previous years, assuming fixed admission probabilities for all applicants and ignoring statistical uncertainty, which can lead to suboptimal decisions. In this study, we propose a novel machine-learning-based approach to improving student admission decisions. Trained on historical application data, this approach predicts the number of enrolled applicants conditionally based on the number of admitted applicants, incorporates the statistical uncertainty of these predictions, and derives the probability of the number of enrolled applicants being larger or smaller than the available study places. The application of this approach is illustrated using empirical application data from a German university. In this illustration, first, several machine learning models were trained and compared. The best model was selected. This was then applied to applicant data for the next year to estimate the individual enrollment probabilities, which were aggregated to predict the number of applicants enrolled and the probability of this number being larger or smaller than the available study places. When this approach was compared with the traditional approach using fixed enrollment yields, the results showed that the proposed approach enables data-driven adjustments to the number of admitted applicants, ensuring controlled risk of over- and underenrollment. Full article

This study presents an innovative approach to support educational administration, focusing on the optimization of university admission processes using feature selection algorithms. The research addresses the challenges of concept drift, outlier treatment, and the weighting of key factors in admission criteria. The proposed methodology identifies the optimal set of features and assigns weights to the selection criteria that demonstrate the strongest correlation with academic performance, thereby contributing to improved educational management by optimizing decision-making processes. The approach incorporates concept change management and outlier detection in the preprocessing stage while employing multivariate feature selection techniques in the processing stage. Applied to the admission process of engineering students at a public Chilean university, the methodology considers socioeconomic, academic, and demographic variables, with curricular advancement as the objective. The process generated a subset of attributes and an application score with predictive capabilities of 83% and 84%, respectively. The results show a significantly greater association between the application score and academic performance when the methodology’s weights are used, compared to the actual weights. This highlights the increased predictive power by accounting for concept drift, outliers, and shared information between variables. Full article

The traditional graduate admission method is to evaluate students’ performance and interview results, but this method relies heavily on the subjective feelings of the evaluators, and these methods may not be able to comprehensively and objectively evaluate the qualifications and potential of the applicants. At present, artificial intelligence has played a key role in the reform of the education system, and the data processing function of artificial intelligence has greatly reduced the workload of screening work. Therefore, this study aims to optimize the graduate enrollment evaluation process by applying a new composite model, the random forest–improved sparrow search algorithm (RF–ISSA). The research used seven data sets including research, cumulative grade point average (CGPA), letter of recommendation (LOR), statement of purpose (SOP), university rating, TOEFL score, and graduate record examination (GRE) score, and carried out the necessary data pre-processing before the model construction. The experimental results show that the RMSE and R values of the composite model are 0.0543 and 0.9281, respectively. The predicted results of the model are very close to the actual data. In addition, the study found that the importance score of CGPA was significantly higher than other characteristics, and that this value has the most significant impact on the outcome of the graduate admissions assessment. Overall, this study shows that combining the integrated strategy sparrow search algorithm (ISSA) with hyperparameter optimization and focusing on the most influential features can significantly improve the predictive performance and applicability of graduate admissions models, providing a more scientific decision support tool for school admissions professionals. Full article

Student admission prediction is a crucial aspect of academic planning, offering insights into enrollment trends, resource allocation, and institutional growth. However, traditional methods often lack the ability to address fairness and transparency, leading to potential biases and inequities in the decision-making process. This paper explores the development and evaluation of machine learning models designed to predict student admissions while prioritizing fairness and interpretability. We employ a diverse set of algorithms, including Logistic Regression, Decision Trees, and ensemble methods, to forecast admission outcomes based on academic, demographic, and extracurricular features. Experimental results on real-world datasets highlight the effectiveness of the proposed models in achieving competitive predictive performance while adhering to fairness metrics such as demographic parity and equalized odds. Our findings demonstrate that machine learning can not only enhance the accuracy of admission predictions but also support equitable access to education by promoting transparency and accountability in automated systems. Full article

Academic performance plays a key role in assessing the quality and equity of a country’s educational system. Studying the aspects or factors that influence university academic performance is an important research opportunity. This article synthesizes research that employs machine learning techniques to identify the determinants of academic performance in first-year university students. A total of 8700 records from the Complutense University of Madrid corresponding to all incoming students in the academic year 2022–2023 have been analyzed, for which information was available on 28 variables related to university access, academic performance corresponding to the first year, and socioeconomic characteristics. The methodology included feature selection using Random Forest and Extreme Gradient Boosting (XGBoost) to identify the main predictors of academic performance and avoid overfitting in the models, followed by analysis with four different machine learning techniques: Linear Regression, Support Vector Regression, Random Forest, and XGBoost. The models showed similar predictive performance, also highlighting the coincidence in the predictors of academic performance both at the end of the first semester and at the end of the first academic year. Our analysis detects the influence of variables that had not appeared in the literature before, the admission option and the number of enrolled credits. This study contributes to understanding the factors that impact academic performance, providing key information for implementing educational policies aimed at achieving excellence in university education. This includes, for example, peer tutoring and mentoring where high- and low-performing students could participate. Full article

Background: Experiential learning (EL) experiences are an important component of a university education, positively impacting career-related attitudes, knowledge, and skills. Students also require EL opportunities to gain experiences required for admission to competitive graduate and professional programs. Students with disabilities face barriers accessing and benefiting from such opportunities. Purpose: This study examined the degree to which demographic factors, type and severity of disability, dispositional factors, and overall adjustment and well-being are predictive of the quality of EL experiences among university students with disabilities. Methodology/approach: A survey was distributed to undergraduate students with disabilities who have participated in EL courses. The results were analyzed using multiple linear regression. Findings/conclusions: Dispositional and adjustment and well-being variables, including the environmental mastery dimension of their psychological well-being rather than demographic factors, such as gender or type and severity of disability, are significant predictors of the quality of EL experiences among students with disabilities. Implications: For students with disabilities to have academically and professionally successful EL experiences, post-secondary institutions must continue to provide appropriate accommodations and educate instructors about the diverse and complex needs of this student group. This must include an understanding of the uniqueness of each individual student’s needs. Full article

Prior to enrolling in an English as a medium of instruction (EMI) institution, students must show an English proficiency level through meeting a benchmark on a standard English proficiency test, which is typically aligned with the Common European Framework of Reference for Languages (CEFR). Along with overall English proficiency, aural/written vocabulary level mastery could also predict students’ success at EMI institutions, as students need adequate English vocabulary knowledge to comprehend lectures and course readings. However, aural/written vocabulary level mastery has yet to be clearly benchmarked to CEFR levels. Therefore, this study aimed to investigate the correlations between students’ aural/written vocabulary level mastery and their CEFR levels. Forty undergraduate students in a Macau EMI university were recruited to take one English proficiency test and two vocabulary level tests (i.e., Listening Vocabulary Levels Test (LVLT) and the Updated Vocabulary Levels Test (UVLT)). Correlation analyses were conducted to explore the relationship between students’ CEFR levels and their mastery of listening and reading vocabulary levels. A positive correlation was found between students’ CEFR levels and their mastery of receptive aural vocabulary levels (ρ = 0.409, p = 0.009). Furthermore, a statistically significant positive correlation was found between students’ CEFR levels and their mastery of receptive written vocabulary levels (ρ = 0.559, p < 0.001). Although positive correlations were observed, no clear pattern was identified regarding the relationship between students’ CEFR levels and their mastery of aural/written vocabulary levels. Regression analyses were further conducted to determine the extent to which the combination of receptive aural and written vocabulary knowledge predicts the CEFR levels. The results indicated that the regression model that included only UVLT scores better predicted the CEFR levels. Given the positive correlations observed between students’ CEFR levels and their mastery of vocabulary levels, this study’s findings suggest the inclusion of aural/written vocabulary levels as additional indicators for ensuring student academic success in EMI institutions. Implications for EMI universities on student admissions, classroom teaching, and provision of additional English courses were provided. Full article