Student Graduation Rates Research Articles

Participation in Undergraduate Research Reduces Equity Gaps in STEM Graduation Rates.

Many students who enroll in a public U.S. 4-y college will not graduate. The odds of completing a college degree are even lower for students who have been marginalized in higher education, especially in Science, Technology, Engineering, and Math (STEM) fields. Can undergraduate research increase a student's likelihood of graduating college and close educational equity gaps in college completion? To answer this question, we use data from six public U.S. universities (N = 120,308 students) and use Propensity Score Matching to generate a comparison group for analyses. We conducted logistic regressions on graduation rates and equity gaps in 4 and 6 y using the matched comparison group and undergraduate researchers in STEM (n = 2727). When being compared with like-peers and controlling for background characteristics and prior academic performance, students who participated in undergraduate research were twice as likely to graduate in 4 y and over 10 times as likely to graduate in 6 y. We also found that equity gaps in 4-y graduation rates for students of color, low-income, and first-generation students were cut in half for undergraduate researchers. At 6 y, these gaps were completely closed for undergraduate researchers. As we seek ways to close education gaps and increase graduation rates, undergraduate research can be a meaningful practice to improve student success.

Inequalities in Girls’ High School Sports Participation: How Social Class, Race/Ethnicity, and Gender Route Opportunities to Play and Persist in Athletics

ABSTRACT High school athletics are understudied sites for social and educational stratification. Participation can offer mental and physical health benefits, improved student retention and graduation rates, the encouragement of pro-social behaviors, resume building, and enhanced social statuses. Despite legal prohibitions against race and gender discrimination in schools, opportunities to play and persist in interscholastic athletics may reflect and amplify existing social and educational stratification processes. Using an exploratory sequential mixed-methods research design, this study centers on girls’ high school sports and considers how gender, race/ethnicity, and social class operate at the individual, interactional, cultural, and institutional levels and encourage proclivities, commitments, and support for participation. We combine qualitative (N = 28 women and 47 total college athletes) and quantitative (N = 4,271 high school students) studies to inquire about how, and to what extent, racial/ethnic and social-class dynamics affect girls playing any and specific high school sports and whether they play persistently. Findings suggest that schools co-construct unequal athletic opportunity structures by nurturing and rewarding a cultivated athletic habitus associated with masculinity, whiteness, and affluent dispositions. These processes disguise athletic advantages and successes as well-earned merit and restrict who is most likely to receive the individual and social benefits of high school sports participation.

SUNY Westchester CC receives $3 million NSF grant

As part of its celebration of Hispanic Heritage Month, State University of New York (SUNY) Westchester Community College (WCC) announced it has received a $3 million, five‐year grant from the National Science Foundation (NSF) to increase the recruitment, retention and graduation rates of students pursuing STEM (science, technology, engineering and mathematics) at Hispanic‐Serving Institutions (HSIs). HSI is a federal designation given to schools that meet various requirements, including at least 25% of enrolled students being of Hispanic backgrounds.

SUNY Westchester CC receives $3 million NSF grant

As part of its celebration of Hispanic Heritage Month, State University of New York (SUNY) Westchester Community College (WCC) announced it has received a $3 million, five‐year grant from the National Science Foundation (NSF) to increase the recruitment, retention, and graduation rates of students pursuing STEM (science, technology, engineering, and mathematics) fields at Hispanic‐Serving Institutions (HSIs). HSI is a federal designation given to schools that meet various requirements, including at least 25% of enrolled students being of Hispanic background.

A Transcendental Phenomenological Exploration of Undergraduate University Students’ Lived Experiences with Spiritual Leadership

This article presents findings from a recent study conducted at a Christian university exploring the spiritual leadership encounters undergraduate students described having with faculty and staff members. University students today face a myriad of challenges ranging from aspects of mental health to finding purpose and meaning in life. As a result, many university decision-makers are seeking ways to improve campus experiences that help meet the needs of their students and increase student engagement, retention, and graduation rates. Spiritual leadership theory is a relatively new theory in leadership studies that has been validated in several studies in various fields. However, there is limited research exploring spiritual leadership in a university context. The purpose of this transcendental phenomenological research study was to explore the lived experience of undergraduate university students with spiritual leadership encounters while attending a university. The data revealed that the spiritual leadership encounters students in this study had with faculty and staff members resulted in several positive experiences, including feelings of being cared for, feeling supported, developing a sense of community, and increased academic motivation. Recommendations for the future include encouraging university decision-makers to explore the potential benefits of integrating the spiritual leadership framework into current pedagogical practices. The information presented here may help practitioners of the spiritual leadership model in classrooms, residential halls, and other university settings to potentially experience similar benefits with the undergraduate students they teach and lead.

Academic Success of Collegiate Athletes and Social and Emotional Learning

College student-athletes represent a unique sub-population of college students with several competing responsibilities to fulfill. While institutions of higher education have historically used standardized test scores and GPA as predictors of students’ academic success, there have been shifts to include non-cognitive, social and emotional factors in this predictive model. Social and emotional skills can be developed. Research has shown that implementing social and emotional learning curriculum has positive impacts on academic achievement, student attitudes about learning, and the creation of prosocial behaviors. These benefits contribute to increased student persistence, retention, and graduation rates. While the connection between social and emotional learning and academic achievement is well documented, there is a lack of research and practical application of social and emotional learning curriculum in higher education with collegiate student-athletes. Advisors, academic support personnel, coaches, and athletic administrators who understand student-athletes’ social and emotional capabilities can use this understanding to help holistically develop student-athletes. Breaking down siloed, departmental operations within higher education and sharing information to work collaboratively for the good of student-athletes can lead to higher academic achievement, better on-field performance, and graduates with the necessary soft skills to succeed in a competitive job market.

Prediksi Tingkat Kelulusan Mahasiswa menggunakan Algoritma Naïve Bayes, Decision Tree, ANN, KNN, dan SVM

The student graduation rate in all universities can be measured by looking at their study duration, both on time and delayed. Thus, by observing the study duration, it can affect the quality of study programs in universities. The purpose of this research is to apply and compare the Naïve Bayes, Decision Tree, Artificial Neural Network, K-Nearest Neighbor (K-NN), and Support Vector Machine (SVM) algorithms in predicting the graduation rate of students. The dataset in this research consisted of 807 student data from the Faculty of Engineering, Universitas Hamzanwadi. The data analysis technique used was descriptive statistics by applying the knowledge discovery in a database (KDD) method. The testing of the five algorithms was done by optimizing the data using the SMOTEENN technique, with a data split of 80% for training and 20% for testing, using a random state of 42. Our findings show that the Naïve Bayes algorithm had an accuracy of 92.37%, Decision Tree 91.60%, K-NN 96.95%, SVM 93.13%, and ANN 90.84%. Among the five algorithms tested, the K-NN algorithm had the highest accuracy rate of 96.95%. The predicted results tended to indicate delayed graduation influenced by GPA. Therefore, the institution needs to pay more attention to students predicted to be delayed to improve their GPA each semester, thus promoting timely graduation within the expected time frame.

Ikhtibār Mi’yār al-Kafā’ah fī al-Lugah al-ʽArabiyyah bi-Mutașaffiḥ al-Imtiḥān al-Āmin fī Jāmiʽah Walisongo al-Islāmiyyah al-Ḥukūmiyyah Semarang

The objectives of the study include: 1. Describe the IMKA test with Safe Exam Browser (SEB), 2. Know the success rate achieved in the test. The research method used is qualitative descriptive analysis, data was collected in the form of observation, interviews, documentation, and triangulation combinations. Spradley model data analysis consists of domain analysis (categorization), taxonomic analysis (category elaboration), and componential analysis (looking for specific differences). The research time is divided into pre, implementation, and post. Pre-research in September 2021. The implementation is from September 2021 until the end of the online exam on August 8, 2022. Post is the time after the exam is over until the collection of the Language certificate. The primary sample/data source in this study is third and fifth-semester students from all departments and faculties within UIN Walisongo totaling 8,755 students with a schedule of 4 periods in July and 6 periods in August. The results showed that the IMKA Test with CBT type SEB has not shown the success of Arabic language skills in UIN Walisongo students, marked by a student graduation rate of 17.63% with the number of students graduating as many as 1,544 students.

Comparison of Different Classification Techniques to Predict Student Graduation

Every year, the number of students accepted at the Maulana Malik Ibrahim State Islamic University of Malang continues to increase. Still, not all students can graduate on time according to the specified study period, resulting in a buildup of students who have not graduated according to their graduation period. One of the aspects evaluated in the Study Program accreditation process is the student graduation rate. Apart from that, for each semester, Study Programs are also required to report educational data to DIKTI, and student graduation is one of the factors considered in the report. There is an imbalance between the number of students graduating each year and the number of new students accepted. To overcome this problem, it is necessary to predict student graduation to determine whether they will graduate on time. In science and data analysis, predictions are often used to make predictions based on existing data and information. Classification models in predicting student graduation include the Nave Bayes method, Support Vector Machine SVM, and Random Forest, as well as the level of accuracy of these three methods. From the results of experiments and model evaluations carried out, with data from 458 Informatics Engineering Study Program students with details of 366 training data and 92 testing data, it was obtained that the SVM model had the highest accuracy, reaching around 87% and Random Forest also had good accuracy, around 82%. At the same time, the Naïve Bayes model has lower accuracy, around 76%.

Coaching program sets struggling students up for success

BOSTON — When student affairs staff at Texas State University wanted to boost student retention and graduation rates, they decided to focus on students on academic probation and in developmental education courses. According to Victoria Black, Associate Dean for Student Services at Texas State University, 20% of first‐year students find themselves on academic probation and 75% of those students don’t get into good academic standing, lose their financial aid, and thus have to leave the institution. To set those struggling students on a path to success, staff developed a success coaching program, resulting in improved GPAs for at least 50% of students who received success coaching at least three times. Also, about 75% of students who received coaching at least two times described the program as helpful, Black said during a presentation at the annual conference for NASPA — Student Affairs Administrators in Higher Education.

Fairness of Academic Performance Prediction for the Distribution of Support Measures for Students: Differences in Perceived Fairness of Distributive Justice Norms

Artificial intelligence in higher education is becoming more prevalent as it promises improvements and acceleration of administrative processes concerning student support, aiming for increasing student success and graduation rates. For instance, Academic Performance Prediction (APP) provides individual feedback and serves as the foundation for distributing student support measures. However, the use of APP with all its challenges (e.g., inherent biases) significantly impacts the future prospects of young adults. Therefore, it is important to weigh the opportunities and risks of such systems carefully and involve affected students in the development phase. This study addresses students’ fairness perceptions of the distribution of support measures based on an APP system. First, we examine how students evaluate three different distributive justice norms, namely, equality, equity, and need. Second, we investigate whether fairness perceptions differ between APP based on human or algorithmic decision-making, and third, we address whether evaluations differ between students studying science, technology, engineering, and math (STEM) or social sciences, humanities, and the arts for people and the economy (SHAPE), respectively. To this end, we conducted a cross-sectional survey with a 2 ×\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ imes$$\\end{document} 3 factorial design among n = 1378 German students, in which we utilized the distinct distribution norms and decision-making agents as design factors. Our findings suggest that students prefer an equality-based distribution of support measures, and this preference is not influenced by whether APP is based on human or algorithmic decision-making. Moreover, the field of study does not influence the fairness perception, except that students of STEM subjects evaluate a distribution based on the need norm as more fair than students of SHAPE subjects. Based on these findings, higher education institutions should prioritize student-centric decisions when considering APP, weigh the actual need against potential risks, and establish continuous feedback through ongoing consultation with all stakeholders.

Early Prediction for Graduation of Private High School Students with Machine Learning Approach

Graduation rates indicate school success. Predicting student graduation helps schools identify students in danger of dropping out and intervene early to enhance academic performance. It can also assist policymakers create graduation and dropout prevention initiatives. However, based on a literature search, predicting student graduation rates from admission test scores is difficult. School grades are a better predictor of timely tertiary graduation than acceptance test scores because college success requires cognitive abilities and self-regulation competencies, which are better indexed by school grades. Self-efficacy, school academic culture, and future expectations can also affect student graduation rates. Finally, the selective admissions modality needs to be refined. This study aims to (1) predict private high school graduation with eight algorithms: Random tree, Naïve Bayes Multinomial, Support Vector Machine (SVM), Random forest (RF), K-Nearest Neighbor, Ada Boost, Multilayer perceptron, Logistic regression, and (2) compare the performance of the eight algorithms. According to research, the Random tree, Naïve Bayes Multinomial, Random forest (RF), and Ada boost algorithms all perform at 99.49% for the first aim. For the second objective, the Random Tree approach outperforms other algorithms in Accuracy (99.49%), Precision (100%), F-Measure (99.74%), and consumption time (0 seconds). Therefore, the Random tree algorithm outperforms others. This research contributes in two ways: scientifically by testing eight algorithms—Random tree, Naïve Bayes Multinomial, Support Vector Machine (SVM), Random forest (RF), K-Nearest Neighbor, Ada Boost, Multilayer perceptron, and Logistic regression—to predict private high school graduation, and secondly by recommending school administrators to develop a selective enrollment model.

Penerapan K-Nearest Neighbor Untuk Klasifikasi Tingkat Kelulusan Pada Siswa Sma Negeri 11 Kota Bengkulu

Abstract-Bengkulu City 11 Public High School is one of the State Senior High Schools in Bengkulu City. The student's pass rate refers to the final score obtained for each subject, then calculates the average score to get the student's pass rating. So far, the school does not have an application that can help provide a predictive picture of student graduation. Where this needs to be done in order to evaluate the teaching and learning system so that students are able to face the final exam given The K-Nearest Neighbor application for classifying graduation rates for students at State Senior High School 11 Bengkulu City can help provide an informational description of the graduation rate for students at State High School 11 Bengkulu City and can provide information on the results of the classification results for high school graduation rates. State 11 City of Bengkulu. Based on the results of the tests that have been carried out, the functional application of the graduation rate classification of students at the Bengkulu City 11 State Senior High School is going well as expected.

Prediction of Student Graduation with the Neural Network Method Based on Particle Swarm Optimization

In private universities in Indonesia, student graduation is something that is worth paying attention to, because it will be an aspect of the success of the university. Universities certainly have data on students who graduated, where student graduation data is very important to be taken into consideration by private universities, however with a lot of data it will make it difficult for private universities to find information from this data. Other researchers have previously carried out student graduation data with the same data by examining student graduation data using other methods. So we need a data mining algorithm that has never been tested on student graduation data. The method used is the neural network method with an optimization method, namely the particle swarm optimization method, to test the data, which will later produce information that is very useful for universities. After testing the student graduation data and getting accuracy results using the neural network method of 84.55% and after being optimized using the particle swarm optimization method, the accuracy results were optimal with a value of 86.94%. These results can be used by private universities to predict that students will graduate on time before they take their final semester so that the student graduation rate will be high.

Teacher Quality Affects On Graduation Of Study Programming Data Approach There With CRISP-DM Method

Each student's graduation is influential to the teacher in every subject that can be predicted based on the pattern of habits of the teacher who presents the subject. Web Proggramming is the subject of study that must be completed by every student. If this course is not completed, it is not allowed for the student to take other courses related to it. The custom patterns of teachers in this study were taken from 300 student respondents. An analysis is done to compare the results of questionnaire scores with the assessment of college admissions teachers. From the results of the comparison, it is possible to predict the graduation rate of students to the web programming course. The results of the experiment were that 72% of the students received highly influential predictions, 12% Influential, 7% Sufficient, 5% Influential and 4% Highly Influential.

The Impact of Academic Library Expenditure on College Graduation Rates

Some higher education institutions have announced plans for cutting funding for academic libraries in response to fiscal difficulties. However, academic libraries provide essential resources and experiences for college students, and a number of studies have investigated how institutional expenditure for academic libraries is associated with student graduation. Yet, these studies are limited in that they only examine cross-sectional correlations, do not control for other factors that can influence student success, or only examine a relatively small number of institutions. To address these limitations, we use longitudinal data from the Integrated Postsecondary Education Data Systems (IPEDS) to analyze the relationship between institutional expenditure on academic libraries and student graduation rates. In particular, we examine how library expenditure is associated with not just overall graduation rates, but the graduation rates of low-income students (i.e., recipients of the federal Pell Grant). Preliminary results indicate that institutional expenditure for academic libraries is positively associated with both overall student graduation rates and the graduation rates of low-income students, net of multiple institutional characteristics linked to graduation rates. Our findings support the importance of investing in academic libraries for student success and evidence against the budget cut of academic libraries. Our study also sheds light on academic libraries’ relevance for the performance of low-income students in universities.

Coaching program sets struggling students up for success

BOSTON — When student affairs staff at Texas State University wanted to boost student retention and graduation rates, they decided to focus on students on academic probation and in developmental education courses. Knowing that 20% of first‐year students find themselves on academic probation, and 75% of those students don’t get into good academic standing, so they lose their financial aid and have to leave the institution, according to Victoria Black, Associate Dean for Student Services at Texas State University. To set those struggling students on a path to success, staff developed a success coaching program, resulting in improved GPAs for at least 50% of students who received success coaching at least three times. Also, about 75% of students who received coaching at least two times described the program as helpful, Black said during a presentation at the annual conference for NASPA — Student Affairs Administrators in Higher Education.

Integrating learning support: the relationship between first-year seminar enrollment and student success in developmental mathematics

ABSTRACT Nearly half of all students in the United States are placed into at least one developmental course in college. Many of these students fail to complete developmental course sequences and persist to degree completion. First-Year Seminars are one of several high-impact practices associated with increased persistence, retention, and graduation rates of college students. Inverse probability of treatment weighting (IPTW) was used to explore the effect of concurrent first-year seminar enrolment on the academic achievement of students placed into developmental mathematics. Study results indicated that students who self-selected into a first-year seminar were more likely to earn a passing grade in their developmental mathematics course and have higher overall GPA. These results may inform strategies for integrating learning support with instruction for academically underprepared college students.

THE IMPACT OF OWNERSHIP TYPE ON THE QUALITY OF STUDY PROGRAMS IN THE HIGHER EDUCATION SECTOR OF KOSOVO: A COMPARATIVE ANALYSIS

The sector of higher education in Kosovo has witnessed significant during the past two decades, with the establishment of private and public institutions leading to increased participation. However, concerns about the quality and the relevance of the study programs offered by these institutions have emerged. To address these concerns, a research study was conducted to evaluate the impact of ownership type on program quality. Based on the analysis of accreditation data, the study finds no significant differences in quality between private and public institutions. Nonetheless, further research is recommended to examine specific accreditation fields and other factors, including student quality, graduation rates, employment rates, student satisfaction, and academic staff quality, to understand the differences in program quality of private and public higher education institutions in Kosovo.

A Comparative Study of Gender Disparities in Geoscience and Mining in Mongolia

Mongolian women enjoy equal rights and actively participate in various sectors of the national economy, including the mineral and mining industry. The Mongolian University of Science and Technology (MUST), the largest university in Mongolia, plays a crucial role in preparing engineers for the Mongolian industry. Within MUST, the School of Geology and Mining Engineering (SGME) stands out as one of the largest schools, boasting a dedicated team of 136 staff members. Impressively, 92 of these staff members are female, constituting a remarkable 67.65% of the total staff. The directorial board of SGME, consisting of 12 members, also demonstrates a noteworthy level of gender diversity, with 5 of its members being female. This represents a proportion of 41.67% and highlights the inclusion of women in decision-making positions. Additionally, it is worth noting that the Geology and Hydrogeology department, one of the five departments within the School, is led by a capable female leader. However, despite the encouraging representation of women among staff and in leadership roles, there is a noticeable disparity in the enrollment and graduation rates of students at SGME. Currently, these rates stand at only about 20–24 percent, indicating the need for further efforts to encourage and support female students in pursuing geology and mining engineering studies. Outside of academia, within the mining industry, the Oyu Tolgoi large-scale mine, which in 2022 employed 20,328 workers, faces a significant gender imbalance. Out of this workforce, only 3577 are women, comprising a mere 18% of the total employees, while the remaining 82% are men. Among the 2997 total employees in the open pit mine, 737 women are employed in various roles, including 66 engineers and technicians, with the remaining 671 in other positions. In the newly opened underground mine, the total number of women employees stands at 2840, including 248 engineers and technicians and 2592 in other roles. Furthermore, on the Board of Directors, there are only 2 women out of a total of 23 managers, and a mere 104 women hold positions as senior staff and superintendents. A comparative analysis between Asia and other global regions reveals that female employment in Mongolia’s mining sector in general, at 18%, closely aligns with Oceania’s rates (17%) and surpasses those of both the broader Asian region (13%) and South America (11%). Addressing these statistical imbalances is crucial to improving gender equality in geoscience and mining. Historically, the mining industry has been male-dominated, but women-led professional geoscience and mining organizations in Mongolia play a vital role in promoting the recruitment, retention, and advancement of women in these industries. Recognizing the significance of gender diversity, these organizations strive to increase the representation of women in leadership positions. Women in leadership bring unique perspectives that contribute to well-rounded decision-making processes within organizations. By acknowledging the importance of gender dynamics, promoting inclusivity, and supporting the professional growth of Mongolian women in geoscience and mining, the overall development and sustainability of these sectors in the country will be greatly enhanced.