Factors Determining University Enrollment Status: The Case of High School Students Recruited to Attend Louisiana State University College of Agriculture (original) (raw)

An Integrated Model of Application, Admission, Enrollment, and Financial Aid

The Journal of Higher Education, 2006

We jointly model the application, admission, financial aid determination, and enrollment decision process. We simulate how enrollment and application behavior change when important factors like financial aid are permitted to vary. An innovation is the investigation into the role of financial aid expectations and how they relate to application and enrollment behavior. . Statistical alternatives for studying college student retention: A comparative analysis of logit, probit, and linear regression. Research in Higher Education, 34(5): 569-581.

Modeling the College Application Decision Process in a Land-Grant University

Economics of Education Review, 1999

Over the past two decades as student recruitment has become increasingly important, numerous studies have examined the college choice process in an attempt to identify factors influencing students' decision making. The findings from these studies are particularly helpful for college administrators in identifying a potential pool of desirable students and in implementing new recruitment techniques. In this study we used a logistic regression model to investigate the effects of variables relating student characteristics and institutional factors on the decision to apply to a large land-grant university.

Statistical Modeling of Some Factors Influencing Student’s Choice of Institution

2020

Education is one of the main instruments in developing human resources. It is thus not surprising that education and skill development training are accorded high priority.It is of critical importance that factors that influence students’ choices of institutions are investigated to enable effective planning of studies. The study employed a design structured questionnaire for data collection. A simple random sampling procedure was used to select a sample of 600 participants across the institutions. The result reveals that for every one-unit increase in economic factors, the odds of not changing institution increases by 0.608 times. For every one-unit increase in motivation factors, the odds of not changing institution increases by 1.313times while for every one unit increase in parental factors, the odds of not changing institution increase by 6.954 times. It is observed that both economic and parental factors are significant in the choice of the institution by students.

A discriminant function model for admission at undergraduate university level

International Review of Education, 1992

The study is aimed at predicting objective criteria based on a statistically tested model for admitting undergraduate students to Beirut University College. The University is faced with a dual problem of having to select only a fraction of an increasing number of applicants, and of trying to minimize the number of students placed on academic probation (currently 36 percent of new admissions). Out of 659 new students, a sample of 272 students (45 percent) were selected; these were all the students on the Dean's list and on academic probation. With academic performance as the dependent variable, the model included ten independent variables and their interactions. These variables included the type of high school, the language of instruction in high school, recommendations, sex, academic average in high school, score on the English Entrance Examination, the major in high school, and whether the major was originally applied for by the student. Discriminant analysis was used to evaluate the relative weight of the independent variables, and from the analysis three equations were developed, one for each academic division in the College. The predictive power of these equations was tested by using them to classify students not in the selected sample into successful and unsuccessful ones. Applicability of the model to other institutions of higher learning is discussed. Zusammenfassung -In Dieser Studie wird versucht, objektive, auf einem statistisch getesteten Modell basierende Kriterien zur Zulassung von Studenten zum Beirut University College herauszufinden. Diese Hochschule mug sich mit zwei Problemen auseinandersetzen, zum ersten nur einen Teil der wachsenden Zahl von Bewerbern zulassen zu k6nnen und zum zweiten die Anzahl der Studenten auf akademischer Probezeit zu reduzieren (gegenw~irtig 36% der Neuzulassungen). Von 659 neuen Studenten wurden 272 Studenten (45%) fur die Studie ausgew~ihlt: alle vom Dekan erw~hlte Studenten und Studenten auf akademischer Probezeit. Das Modell beinhaltet zehn unabh~ingige Variablen und ihre Interaktionen und basiert auf akademischer Leistung als abh~ingige Variable. Unter diesen Variablen sind: die Art der Sekundarschule, die Lehrsprache an der Sekundarschule, Empfehlungen, Geschlecht, Sekundarschulnoten, Leistungen beim English Entrance Examination (Leistungstest in Englisch), das Hauptfach der Schtiler und ob das Hauptfach das vom Schtiler ursprtinglich gewahlte ist. Die statistische Analyse wurde zur Bewertung der reIativen Bedeutung der unabh~ingigen Variablen angewandt, und es wurden drei Gleichungen aus der Analyse entwickelt; eine ftir jede akademische Abteilung der Hochschule. Die Gtiltigkeit dieser Gleichungen wurde getestet, indem man sie ftir die Aufteilung von nicht in die Studie einbezogenen Studenten in erfolgreiche und nicht erfolgreiche Studenten anwandte. Abschliegend wird die Anwendbarkeit des Modells auf andere Einrichtungen der H6heren Bildung diskutiert. R6sum6 -La pr6sente 6tude vise ~t pr6dire les crit~res objectifs fond6s sur un modble statistiquement v6rifi6 pour l'admission des 6tudiants a l'Universit6 de Beyrouth. Cette derni~re fait face ~ un probl~me dual: devoir s61ectionner une fraction seulement d'un nombre croissant de candidats et essayer de minimiser le nombre d'6tudiants inscrits International Review of Education --International Zeitschrift fiir Erziehungswissenschaft --Revue Internationale de Pgdagogie 38(5): 505-518, 1992. 9 1992 Unesco Institute of Education and Kluwer Academic Publishers. Publishers. Printed in the Netherlands.

An Integrated Enrollment Forecast Model. IR Applications, Volume 15, January 18, 2008

2008

Enrollment forecasting is the central component of effective budget and program planning. The integrated enrollment forecast model is developed to achieve a better understanding of the variables affecting student enrollment and, ultimately, to perform accurate forecasts. The transfer function model of the autoregressive integrated moving average (ARIMA) methodology and linear regression model are major forecasting techniques. The structural approach embedded in the models allows the researcher to construct candidate models, eliminate inappropriate ones, and retain the most suitable model. In addition, the expert system for the ARIMA model is a supplementary tool used to verify the resulting models in terms of model structure and forecasting accuracy. The enrollment series of interest is the 1962-2004 student enrollment for Oklahoma State University (OSU). Fifteen independent variables are used in an attempt to increase explanatory power. These variables include demographics (Oklahoma high school graduates and competitor college enrollment from the University of Oklahoma), state funding, economic indicators, (e.g., state unemployment rate and gross national product), and one-year lagged demographics and economic indicators. The best ARIMA and linear regression models yield remarkably high R-squared values and exceptionally small mean absolute percentage errors (MAPEs), respectively. Moreover, they contain two identical demographics: Oklahoma high school graduates and one-year lagged OSU enrollment. Hence, the first-order autoregressive models appropriately depict the longitudinal and aggregated OSU enrollment series. An additional linear regression model shows that one-year lagged Oklahoma high school graduates and three economic indicators significantly contribute to OSU enrollment. This integrated enrollment forecast model has demonstrated its model validity and accuracy. Hence, it could be replicated for comparable universities elsewhere.

A COMPREHENSIVE STUDY ON FACTORS AFFECTING ENROLLMENT

In the 21 st century educational institution looks toward data generated, collected and stored. Data analysis using statistical and datamining exploring new and hidden information. These data analysis are helpful for decision maker to take necessary action before any event happening and laid path for safe steps. In this paper a detailed literature review is conducted to understand the factors affecting enrollment and dropout in educational Institution.

A Matching Model to Measure Compliance between Department and Student

Cumhuriyet Science Journal, 2020

The aim of all education systems is to train students who are equipped with knowledge. In that case, that student is able to determine the most suitable profession for him/her success in education and career that are related to this profession will be higher. Studies done up to this day have been focused on finding out the factors affecting the career choice of the student, but they have not suggested any method for determining the most suitable procession. It is not possible to obtain satisfying results from a system that does not lead students to appropriate higher education departments. In this context, a student-department matching system is proposed which aims to increase the success of the education systems in our study. The department of computer engineering was dealt with as a sample department and the proposed study was examined to determine whether a student was suitable for computer engineering or. The required data was obtained with the help of the questionnaire, and then a model of successful and unsuccessful students was created. Data mining algorithms such as C4.5, C-SVC, MLP, and Naïve Bayes are used during the test of the generated model. The best result was obtained by the C-SVC algorithm and the second best result by Naive Bayes. The lowest error rate achieved was 0.2700 and the highest accurate recognition rate was 73.00%.

Recruiting higher education students: a systematic review of the college selection process models

International Journal of Education Economics and Development, 2011

The study of student enrolment decision-making or college choice behaviour is an extremely practical operation that leads to greater efficiency and effectiveness. Understanding student college choice behaviour enables institutions of higher learning to estimate the probability of student interest and enrolment. Higher education institutions (HEIs) would be able to strategise their marketing efforts in the scramble for students. This paper seeks to review systematically the student college choice models. The models to be reviewed are Chapman model (1981), Hanson and Litten model (1982), Jackson model (1982) and Hossler and Gallagher model (1987). It is suggested that future research should investigate on 'campus security' on student decision making in college enrolment as it has not been empirically studied in the models abovementioned. Furthermore, future empirical research can also be carried out on prospective students, i.e., students who have yet to enrol in HEI in Southeast Asia countries.