Student Graduation Rates Research Articles

Service Learning in the First Year: Enhanced Retention and Graduation Rates for First-Year and Transfer Students from Underrepresented Groups

This study constitutes an assessment of the service-learning program at a minority-serving institution, using data spanning nine years. It focuses on two student populations: those who enrolled as first-time students, and the larger population of students who transferred from other institutions, primarily two-year colleges. Within these groups, we examine the effects of service-learning in the first year on Latinx/Hispanic students, low-income students, and other underrepresented student subpopulations on first-year retention and on graduation rates. Using propensity score analysis with full matching techniques means this study may be considered a quasi-experimental design in the cases for which strong balance is obtained. Results indicate that service-learning increases first-year retention rates, and increases two measures of graduation rates in every case demonstrating strong balance and significance. Results for Hispanic students are particularly notable.

Systems analysis of the national performance management system for TVET lecturers in South Africa

A quality performance appraisal (PA) and continuing professional development (CPD) system is an essential component of a national performance management system for TVET lecturers. It is intended to support an improvement in lecturer quality and accountability, strengthens student graduation rates and feeds national human resource development for sustainable employment. The aim of this research study was to contribute to improvements in the functional integration and synergies between PA and the CPD system as applied in TVET colleges. The study involved the collection of qualitative data at six South African TVET colleges in order to develop a general description that captures the key features and interactions commonly shared across the case studies in a general analytic model. Data collection, coding and analysis informed the iterative development of a systems model based on causal loop analysis (CLA). This application of the CLA tool provided the means to represent multiple relationships and the feedback loops between them visually. We found that four main causal loops strongly influence the dynamics of PA and the CPD system. The findings offer an analytic platform for further research towards strengthening and reviewing the overall performance management system for TVET lecturers.

Application of Data Mining for Prediction of High School Student Graduation Rates

The implementation of Data Mining in the education sector aims to develop methods that are able to discover valuable knowledge from data generated in the educational environment. This can be used to increase learning efficiency by paying more attention to students who are predicted to have low grades. However, in its application, each algorithm shows different performance depending on the attributes and dataset used. In this study, a dataset of semester grades and final school exam scores was used. Some of the prediction techniques used are decision trees, support vector machines, and neural networks. Of the four scenarios for the science major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as predictions of students' final grades, namely the decision tree model and support vector machine. For the Social Sciences major at SMAN 2 and SMAN 3 Pangkalpinang with 3 different models, the Mean Squared Error value shows that the test results are in accordance with the testing dataset and can be used as a prediction of students' final grades, namely the support vector machine model.

Transforming Undergraduate STEM Education: The Learning Assistant Model and Student Retention and Graduation Rates

BackgroundEvidence-based and student-centered instructional methods hold the promise of transforming undergraduate STEM education and simultaneously solving the dual challenge of STEM workforce needs and inequities within STEM. The Learning Assistant (LA) Model was created to reform curriculum, recruit teachers, and inform discipline-based education research. Numerous studies have shown positive impacts on students and Learning Assistants (LAs) in terms of retention and pass rates, learning outcomes, attitudinal shifts, and emotional support. Several studies have explored demographic differences and found that LAs help close the gap in retention rates for women and first-generation students. While short-term effects have been well documented, the long-term effects on students’ college retention and completion rates have not been widely studied. We gathered information about LA-adopting institutions identified on the Learning Assistant Alliance website and from individual higher education institutions’ websites. Combining this information with the College Scorecard Database for four-year U.S. institutions allows us to examine student retention and graduation rates after these institutions have adopted the LA model and look for differences in both the student and university characteristics included in the dataset. We are interested in determining if there is a difference between LA-adopting and non-LA-adopting institutions in terms of their student outcomes.ResultsThese LA-adopting institutions have larger enrollments and are more likely to award graduate degrees. Students in LA-adopting institutions are more likely to be younger with lower percentages of females, first generation, and Black students. No significant differences in the percentages of Hispanic students or in the average costs per student were observed in our study. Using regression analysis, we find LA-adopting institutions have significantly higher retention and completion rates.ConclusionsSince LA-adopting institutions have different characteristics than non-LA institutions, these differences should be considered in future studies examining the impact of the LA model. After controlling for these differences in our study, we find that institutions with the LA model have 4 to 15 percentage points higher college completion rate and they have better outcomes for Hispanic and Black students. These findings suggest that increasing the adoption of the LA model may contribute to reducing inequity in STEM.

The longitudinal retention of STEM students through a multifaceted mentoring and tutoring program

ABSTRACT This 4-year longitudinal study identified the impact of a multifaceted mentoring and tutoring program on the retention and graduation rates of a diverse body of undergraduate college students enrolled in high-risk science courses. Findings revealed that the program was successful in increasing the retention and graduation rates of students who participated in the program. All student cohorts tutored through the program demonstrated significantly higher survival rates when they increased their usage of tutoring, threby demonstrating greater survival to retention. Notably, cohorts with high as well as those with low academic competencies achieved the highest 4-year retention survival probabilities when they participated in six or more tutoring sessions. Additionally, through further analysis, a predictive model derived from this research offers a valuable tool that can be strategically utilized to improve retention and/or graducation outcomes of at-risk populations and underrepresented populations in STEM education.

Mission Impact Levers (MIL) Framework: A Value Lens Approach to Leadership and Resource Management

This study evaluates the effectiveness of the Mission Impact Levers (MIL) Framework in aligning institutional actions and resource allocations with core missions within higher education institutions (HEIs). By employing a dataset that includes retention and graduation status, and integrating machine learning techniques, particularly the Random Forest model, the research quantitatively analyzes the framework’s impact on key institutional metrics. The MIL Framework is designed to foster strategic coherence and long-term sustainability by ensuring mission-centric decision-making. Preliminary findings indicate that institutions implementing the MIL Framework experience enhanced institutional effectiveness, demonstrating notable improvements in student retention and graduation rates. This data-driven approach not only confirms the framework's potential to significantly benefit HEIs in a competitive educational environment but also enhances strategic decision-making. This validation study aims to equip institutional leaders with robust tools to foster resilience and success, thereby positioning their institutions to thrive amidst increasing competition and dynamic educational demands.

Implementasi Algoritma Naïve Bayes dalam Memprediksi Tingkat Kelulusan Siswa pada Sertifikasi Mikrotik Certified Network Associate (MTCNA)

The development of technology has increased the importance of certification in computer networking, such as the MikroTik Certified Network Associate (MTCNA). This certification is crucial for students who want to validate their skills and enhance their career prospects. However, predicting student graduation rates for the MTCNA certification presents various challenges, such as prior knowledge, study habits, and the complexity of the exam content. These factors can significantly affect the accuracy of predictions regarding student graduation rates. This study aims to address these issues by implementing the Naïve Bayes algorithm to predict student graduation rates for the MTCNA certification. The Naïve Bayes algorithm, known for its simplicity and effectiveness in classification problems, is expected to provide a more accurate predictive model. This study uses historical data on student performance and other relevant attributes. The results show that the Naïve Bayes model has an adequate prediction accuracy, with an accuracy rate of 91.70%. These findings are expected to provide valuable insights for educators and students, thereby improving preparation strategies and success rates in the MTCNA certification exam.

Implementation Of The Support Vector Machine Method In Predicting Student Graduation

Student graduation is an important indicator of the quality of education at a higher education institution. A high graduation rate not only indicates success in the learning process but also has a positive impact on the reputation of the institution. Conversely, a low graduation rate can be a signal of problems that require special attention. Various factors, both academic and non-academic, influence higher education institutions in ensuring timely student graduation. Therefore, we need a method that can accurately predict student graduation to carry out early intervention. This study aims to apply the support vector machine method in predicting student graduation. We chose this method due to its capacity to classify complex data. We use historical student data, such as Semester Achievement Index scores, as input variables to build a prediction model. We evaluate the model using precision, recall, and f1-score metrics. According to the study's findings, the support vector machine model's accuracy level is 71.20%. This method is good at predicting students who graduate with a precision of 95%, recall of 72%, and f1-score of 82%. However, the model's performance in predicting students who failed was less than satisfactory, with a precision of only 17%, a recall of 62%, and an f1-score of 26%. The imbalance in data between passed and failed students contributed to this result. The Support Vector Machine method effectively predicts student graduation for the majority class (passed), but requires special handling of the data imbalance to enhance the accuracy of predictions for the minority class (failed). Universities expect to use the results of this study to carry out early intervention and increase student graduation rates.

Upward Bound Program Supports Success of Low-Income and/or First-Generation College Students at a STEM-Focused HSI

A deeper understanding of low-income and first-generation college student experiences is essential to advancing diversity and equity in higher education. Due to a significant gap in college enrollment and graduation rates for low-income and first-generation college students, specifically after COVID-19, it is important to identify factors affecting their success. The Upward Bound Math and Science (UBMS) Program at New Mexico Tech (NMT) supports high school students who are either first-generation college students and/or from low-income families to prepare them with the skills and motivation necessary to complete high school and enter and succeed in a program of postsecondary education. These students face unique challenges during their time at college. To identify the factors that affect the success of these students, we surveyed several of our UBMS program participants who chose to attend NMT after completing high school. All participants said support from UBMS staff was critical when starting college at NMT. Participants also mention building a support system with their fellow UBMS alumni to deal with the struggles that being at STEM-focused institutions brings. This study also resulted in several suggestions for faculty and staff members to improve academic outcomes for all students.

Academic library expenditures and graduation rates: evidence from four-year colleges and universities in the US

ABSTRACT In the midst of financial challenges, colleges and universities in the United States have significantly cut funding for their academic libraries in the name of enhancing efficiency. Using longitudinal data from the Integrated Postsecondary Education Data System (IPEDS) on 1,325 four-year colleges and universities, this study finds that total academic library expenditure is positively associated with student graduation rates, net of a wide range of institutional and student-related characteristics. Such findings are evident for total graduation rates as well as graduation rates for students of different racial/ethnic backgrounds. In particular, the positive association between academic library expenditures and graduation rate is most consistently evident for Black students who attend predominantly White institutions (PWIs). The findings reinforce the important role that academic libraries can play for students and shed light on the need for institutions to consider the educational consequences of reducing funding for academic libraries.

The Relationship between Principal Managerial Skills and Teacher Teaching Competence with Student Graduation Rates for State Universities in SMA Negeri in Tarutung District

The Relationship between Principal Managerial Skills and Teacher Teaching Competence with Student Graduation Rates for State Universities in SMA Negeri in Tarutung District

APPLICATION OF RANDOM FOREST METHOD TO PREDICTION OF STUDENT CANDIDATES ACCEPTED IN THE SNMPTN PATHWAY (CASE STUDY AT UNIVERSITAS LAMBUNG MANGKURAT)

Academic planning is an essential aspect that needs to be carried out to plan the teaching and learning process in a campus, such as the admission of new students through the SNMPTN. At Lambung Mangkurat University, it is known that the partici- pants who took the SNMPTN during the 2021 admission period amounted to 7,703. It is recognized that selecting candidates is not easy due to many prospective students passing the selection but choosing to withdraw. Therefore, a system is needed to predict the graduation of prospective students through the SNMPTN. This research utilizes the Random Forest method to predict the graduation of prospective students through the SNMPTN. The data will be divided into 90% for training data and 10% for testing data, then using classification parameters with 300 n-estimators. The research yielded a precision value of 0.72, recall value of 0.46, and system accuracy of 89.3% For further research recommendations, other prediction methods can be explored to forecast the graduation rate of students through the SNMPTN, or a comparison of methods can be con- ducted to determine which method is more effective.

Faculty diversity and graduation rates: a zero-sum effect

ABSTRACT Texas higher education institutions serve some of the most diverse student populations in U.S. institutions. However, graduation rates remain among the lowest in the United States, particularly for underrepresented minority students. Diverse faculty members who act as mentors have the potential to increase underrepresented minority students’ graduation rates. Yet few studies have examined these relationships. Using data from the 2021 Integrated Post-Secondary Data System (IPEDS), this quantitative study contributes to this line of research by examining the correlations of overall faculty diversity and faculty race/ethnic match and mismatch with graduation rates of students by race/ethnicity in Texas. Similar to past research, findings suggested a zero-sum effect: a significant positive relationship between faculty and students of the same racial/ethnic background and a negative correlation between faculty diversity and the graduation rates of students from different racial/ethnic backgrounds than faculty. These results suggest that further research is needed to determine what other variables might influence such findings and how faculty can support students from different racial/ethnic backgrounds.

Analisis Faktor-Faktor Keterlambatan Mahasiswa Tingkat Akhir Di Program Studi Pendidikan Ekonomi FKIP ULM Dalam Menyelesaikan Skripsi

This research aims to determine the factors inhibiting the completion of a thesis in the FKIP ULM economics education study program in the 2016-2019 class. This research uses quantitative research with descriptive quantitative research, using a questionnaire which will be distributed using a Likert Scale with a sample size of 92 participants. With the validity research instrument test used in this research, the validity test itself uses the Product Moment (Pearson) correlation statistical calculation formula. And testing the reliability test was measured using the Cronbach'Alpha method. The data analysis technique used was Descriptive Analysis. Based on research that has been carried out, it is stated that the factors of delay in completing the thesis affect the graduation rate of FKIP ULM economic education students. is in the very high category with a percentage of 97.9%. The factors that cause delays in completing a student's thesis are internal factors and external factors consisting of internal factors, namely motivation, intelligence, health, attitudes and study habits, psychology. External factors consist of family factors, availability of learning resources, peer environment, lecturers, and other factors.

Prediction of Student Graduation Rates using the Artificial Neural Network Backpropagation Method

This student graduation rate research focuses on analyzing academic performance with the main aim of identifying and distinguishing between students who graduate on time and those who graduate late. The application of data mining techniques in this research uses the neural network method, which is expected to offer deeper insight into the factors that influence students' graduation times. In this study, the neural network method was used to classify graduation data from 150 students. The results of this analysis were very encouraging, with 149 students identified as graduating on time and one student graduating late. The level of accuracy achieved in this classification is 98%, which shows the effectiveness of the neural network method in processing and analyzing academic data. These results confirm that neural networks are a powerful and reliable tool for predictive tasks like this. The successful use of neural networks in this study also proves their potential in broader educational applications, particularly in optimizing educational and intervention strategies. By understanding the characteristics of students who graduate on time versus those who graduate late, educators and administrators can design more effective programs to support student success. This is important not only to improve graduation statistics, but also to improve the overall educational experience for students.

Impact of a Networked Improvement Community’s Interventions on STEM Student Persistence. Review of Results and a Call to Action.

It is well known that high impact practices such as student learning communities and peer-mentoring, early engagement in research and faculty-student relationship building can have a positive influence on undergraduate student engagement and retention in their chosen field of study. The First2 Network is a Networked Improvement Community comprised of students, faculty, staff and administrators from eight higher education institutions in West Virginia and one in Kentucky, that has, over the past six years, engaged in creating interventions to implement and test these high impact practices with the aim of doubling the graduation rate of first-generation and other underrepresented STEM students. Relying on data collected, mostly from WVU, we see very promising outcomes on the persistence of STEM majors. These results will be shared. However, our data story is incomplete as there is no mechanism to sort data on first-generation status across all universities and colleges in the state. The value of this information and its inclusion in a future state funding formula will be explored.

Enhancing STEM Enrollment, Retention, and Graduation

JOHNNA SMITH, Dept of Mathematics, Shepherd University, Shepherdstown, WV, 25443, RAINA BURTON, Dept of Mathematics, Shepherd University, Shepherdstown, WV, 25443, QING WANG and KAREN ADAMS, Dept of Computer Sciences, Mathematics, and Engineering, Shepherd University, Shepherdstown, WV, 25443. Enhancing STEM enrollment, retention, and graduation: insights and strategies for student success. The objective of this study is to synthesize over 20 findings related to STEM success and its measurement to enhance student recruitment, retention, and graduation rates. Surveys were analyzed focusing on various student categories, including underrepresented minorities (URMs), high-achievers, low-income individuals, first-generation students, community college transfers, and gender diversity, among others. Our findings demonstrate that improving students’ self-efficacy and fostering a sense of belonging significantly contribute to their success and persistence in STEM. Consequently, programs such as undergraduate research opportunities, networking, seminars, summer bridge programs, field trips, and workshops are identified as crucial for enhancing students’ confidence and sense of belonging, thereby facilitating their success and perseverance in STEM fields. The project was supported by the Summer Undergraduate Research Experience Grant (SOARS) from West Virginia Higher Education Policy Commission Division of Science and Research and the NSF S-STEM Grant (award No. DUE-2130267).

Navigating the Academic Maze

Academic advising is an integral component of higher education that impacts student achievement, university retention, and graduation rates. Given the current competitive arena in Thai higher education, advising plays an increasingly critical role in students’ and universities’ success. However, advising is a complex process, and what actually occurs, with whom, when, and where is complicated. Furthermore, little is known of the current state of advising in Thai higher education as in the extant literature there are as yet no articles in English on the subject. The present article presents an auto-ethnographic reflection of the author’s lived experience in conducting advising at a university in Thailand between 2020 and 2022. The purposes of this article were to shed light on the current state of advising in Thai higher education by sharing the author’s lived experience, and to connect this experience to broader issues in advising at Thai universities. Suggestions are given to further the professionalization and quality of advising in Thai higher education, including a call for a paradigmatic shift from a traditional, prescriptive approach to a developmental, holistic one. Given the lack of English literature on advising in Thailand, this paper provides needed insights into the subject.

Effects of a Mentorship Program on High Need College Students: Reflections from Mentors and Mentees

On average, the college graduation rates for minority and/or high-need students are generally low. To address this situation, a Mid-Atlantic Consortium recently secured a grant to improve 4-year graduation rates of high-need students (i.e., new first-time students and transfer students who qualify as low-income students, first-generation college students, adult students, and/or students of color) by 20% over each selected college’s baseline. The purpose of this qualitative case study was to explore the experiences of students and faculty mentors toward accomplishing this goal. Data were transcribed, coded, and analyzed thematically. Some of the effects identified in this study included enhanced academic and career planning, strengthening relationships between mentors–mentees through enhanced communication and trust building, enhanced research skills, development of mentorship skills, development of intellectual curiosity, and improved academic outcomes. We discuss implications for practice, policy, and further research.

Comparison of Classification Algorithms for Predicting Graduation of Informatics Engineering Students with Orange Data Mining

Timely completion of studies is an important parameter to assess the competence of graduates. However, challenges arise because not all students can complete their studies according to the predetermined schedule. In this study, the prediction of student graduation status was developed using four classification models, namely Decision Tree, Naïve Bayes, K-NN and SVM. This research also involves the addition of new variables to improve the accuracy of predicting student graduation rates. The dataset used is the 2018-2020 batch student data at Prof. Dr. Hamka Muhammadiyah University, consisting of 500 student data (60% training data and 40% test data). The analysis was carried out by utilizing Orange Data Mining software, with model evaluation involving K-Fold Cross Validation with a value of (K=5), Confusion Matrix, and ROC. The results show that the K-NN algorithm is the most effective algorithm in predicting student graduation status, with accuracy and precision rates reaching 92%, recall rate of 89%.