Enrollment Forecasting Research Articles

Forecasting school enrollments in the Australian Capital Territory

Abstract School enrollment forecasts are vital for effective planning. This study introduces a probabilistic multiregional population projection model, which accounts for different components, including preschool entries, migration, grade progression, and graduations. Using different distributions with a cohort component projection and Monte Carlo simulation, this paper forecasts student enrollments for each school and academic year level in the Australian Capital Territory based on the annual record-level administrative data. In the in-sample validation tests, the model’s overall performance is robust, and the probabilistic design offers reliable prediction intervals reflecting the variation in observed values. The paper ends with a discussion on the importance of prediction intervals for informing school planning.

Machine Learning Advancements in Healthcare Insurance: A Comprehensive Review and Future Directions

By conducting a thorough literature review, this study examines how sophisticated machine learning algorithms are being used in the healthcare insurance industry. Predictive modeling, fraud detection, enrollment forecasting, premium prediction, illness prediction, sentiment analysis, and claim processing optimization are just a few of the areas covered by the research articles compiled for this study. Provide insights into the present state-of-the-art and highlight possibilities for future research and innovation by reviewing the techniques, major results, and emerging patterns across these studies. The study shows how machine learning may help the healthcare insurance sector with decision-making, resource allocation, and risk mitigation. Furthermore, it reviews some difficulties, recommended approaches, and consequences of implementing these technologies, such as the significance of multidisciplinary cooperation, ethical concerns, and regulatory compliance. This study helps move the needle on healthcare insurance's and machine learning's junction, encouraging further research and making data-driven solutions more accessible to businesses

Minimizing of Forecasting Error in Fuzzy Time Series Model Using Graph-Based Clustering Method

In recent years, numerous fuzzy time series (FTS) forecasting models have been developed to address complex and incomplete problems. However, the accuracy of these models is specific to the problem at hand and varies across datasets. Despite claims of superiority over traditional statistical and single machine learning-based models, achieving improved forecasting accuracy remains a formidable challenge. In FTS models, the lengths of intervals and fuzzy relationship groups are considered crucial factors influencing forecasting accuracy. Hence, this study introduces an FTS forecasting model based on the graph-based clustering technique. The clustering algorithm, utilized during the fuzzification stage, enables the derivation of unequal interval lengths. The proposed model is applied to forecast two numerical datasets: enrollment data from the University of Alabama and the datasets of Gas prices RON95 in Vietnam. Comparisons of forecasting results between the proposed model and others are conducted for enrollment forecasts at the University of Alabama. The findings reveal that the proposed model achieves higher forecasting accuracy across all orders of fuzzy relationships when compared to its counterparts

DEVELOPMENT OF A MULTICRITERIA MODEL OF DISTRIBUTION OF ADMISSION QUOTAS

Objective. The main problem of higher education institutions is that there is a well-developed system of approval and distribution of admission control figures between educational organizations, but there is no unified system of distribution of budgetary and paid places within enlarged groups of directions and specialties (UGSS). In this regard, it is proposed to develop a multi-criteria model for the distribution of budgetary and paid places within the USGS, which will be a tool for analyzing the demand for certain directions among applicants and considering the risks of not filling the directions, which may be allocated more than the number of applicants.
 Method or methodology of work. The research is based on multi-criteria optimization, data normalization, additive convolution, construction of the BCG matrix.
 Results. The developed model makes it possible to compile the optimal distribution for the university, which can be used both for a specific USGS and for the whole university, which is ensured by multilevel optimization, which was used to solve the problem. Also the model allows to take into account the risks of not filling the places and minimize them.
 Scope of application of the results. The results of the study can be applied in the field of enrollment forecasting.

Senior High School Student Enrollment Forecasting Model: An Application of Time Series Analysis

Senior High School Student Enrollment Forecasting Model: An Application of Time Series Analysis

Application of Markov chain in students’ assessment and performance: a case study of School of Mathematical Sciences, one of the public university in Malaysia

The quality education is an essential element in economic, political and social development of any country. Therefore, enrollment forecasting is needed in higher education to assist the universities in the preparation of their educational frameworks including budgeting, provide all necessary facilities and planning the overall short and long term goals. This research study the pattern of students’ assessment and their academic performance in School of Mathematical Sciences, Universiti Sains Malaysia. The target population is all undergraduate enrollment from 2016/2017 until 2018/2019 sessions. An absorbing Markov chain model is applied to study the absorption, retention and repetitive rates of the students by the academic programs and gender. The fundamental matrix is constructed to determine the expected duration of schooling before graduating. The enrollment projection is also estimated to study the probability of the performances of the students in the long run. In summary, this research addresses on the use of Markov chain model to describe the stochastic pattern of the enrollment and assessment of the students.

Student Trajectories for Enrollment Forecasting, Management, and Planning

Student Trajectories for Enrollment Forecasting, Management, and Planning

Higher Education Institution (HEI) Enrollment Forecasting Using Data Mining Technique

Higher Education Institution (HEI) Enrollment Forecasting Using Data Mining Technique

The Impact of Lengths of Time Series on the Accuracy of the ARIMA Forecasting

The Autoregressive Integrated Moving Average model (ARIMA) is a popular time-series model used to predict future trends in economics, energy markets, and stock markets. It has not been widely applied to enrollment forecasting in higher education. The accuracy of the ARIMA model heavily relies on the length of time series. Researchers and practitioners often utilize the most recent - to -years of historical data to predict future enrollment; however, the accuracy of enrollment projection under different lengths of time series has never been investigated and compared. A simulation and an empirical study were conducted to thoroughly investigate the accuracy of ARIMA forecasting under four different lengths of time series. When the ARIMA model completely captured the historical changing trajectories, it provided the most accurate predictions of student enrollment with 20-years of historical data and had the lowest forecasting accuracy with the shortest time series. The results of this paper contribute as a reference to studies in the enrollment projection and time-series forecasting. It provides a practical impact on enrollment strategies, budges plans, and financial aid policies at colleges and institutions across countries.

Undergraduate International Student Enrollment Forecasting Model: An Application of Time Series Analysis

This study developed statistical models to forecast international undergraduate student enrollment at a Midwest university. The authors constructed a SARIMA (Seasonal Autoregressive Integrated Moving Average) model with input variables to estimate future enrollment. The SARIMA model reflected enrollment patterns by semester through highlighting seasonality. Further, authors added input variables such as visa policy changes, the rapid increase of Chinese undergraduate enrollment, and tuition rate into the model estimation. The visa policy change and the increase of Chinese undergraduate enrollment were significant predictors of international undergraduate enrollment. The effect of tuition rates was significant but minimal in magnitude. Findings of this study generate significant implications for policy, enrollment management, and student services for international students.

Strategic Enrollment Management for Chief Enrollment ­Officers: Practical Use of Statistical and Mathematical Data in Forecasting First Year and Transfer College Enrollment

Both an art and a science, enrollment projections have become a major component to effective college and university fiscal planning. With stagnant or declining state budget support for public higher education along with an increasing emphasis on revenue generation, never before has predicting the size of an entering class become more imperative. Too few students and budgets are sure to suffer; too many students and residence halls will be overflowing. This article presents several approaches to manage enrollment data for an entering class and predict enrollment yield. By examining past yield behavior coupled with trend and conversion data, a college or university will be better positioned to provide exceptionally accurate enrollment forecasts to senior administration. The authors provide pragmatic examples to empirically engage and data mine applicant pools for both first year and transfer populations in order to predict yield conversion with a greater level of confidence. Special attention will be placed on describing different statistical and mathematical techniques to predict enrollment.

FUZZY MODEL OPTIMIZATION FOR TIME SERIES DATA USING A TRANSLATION IN THE EXTENT OF MEAN ERROR

Recently, many researchers in the field of writing about the prediction of stock price forecasting, el ectricity load demand and academic enrollment using fuzzy methods. However, in general, modeling does not consider the model position to actual data yet wher e it means that error is not been handled optimally . The error that is not managed well can reduce the a ccuracy of the forecasting. Therefore, the paper wi ll discuss reducing error using model translation. The error that will be reduced is Mean Square Error (MSE). Here, the analysis is done mathematically an d the empirical study is done by applying translation to fuzzy model for enrollment forecasti ng at the Alabama University. The results of this analysis show that the translation in the extent of mean error can reduce the MSE.

Enrollment Forecasting for School Management System

Enrollment Forecasting for School Management System

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.

Modified Weighted for Enrollment Forecasting Based on Fuzzy Time Series

The enrollment study is main point for the university planning. Many previous studies have been presented for enrollment forecasting. This paper proposed the adoption the weighted and the difference between actual data toward midpoint interval based on fuzzy time series. By using the enrollment of the University of Alabama and Universiti Teknologi Malaysia (UTM), as data sets for training and testing, the performance of the adoption approach has been shown much improvement in terms of MSE (Mean Square Error) and average error of forecasting measurements. Keywords: Recurrence; chronological order; modified weighted; enrollment; fuzzy time series.

University student enrollment forecasts by analysis structural ratios using ARIMA-methods

Forecasts for the number of students in Germany are conducted by the Kultusministerkonferenz. They use a transition model which does not allow for prediction intervals and therefore lack a measure of uncertainty of the forecast. Since the uncertainty is high for such forecasts, this lack is of importance.

Applying ad hoc institutional research findings to college strategic planning

AbstractEnvironmental scanning, enrollment forecasting, budget analyses, and institutional effectiveness assessment are examples of the explicit contributions institutional research offices make to campus strategic planning.

Enrollment Forecasting and Revenue Implications for Private Colleges and Universities

AbstractThe unique issues faced by private institutions of higher education in enrollment planning are discussed with suggestions for practice.

Methods and Techniques of Enrollment Forecasting

AbstractThe factors affecting enrollment are outlined, and alternate enrollment‐forecasting methodologies are reviewed and critiqued.

Developing a Decision Support System for Enrollment Forecasting

The objectives of this article are: 1) to illustrate the strategic forecasting approach for the administrators; and 2) to lay the foundations toward a decision support system (DSS) that would achieve a high degree of enrollment forecasting accuracy and decision flexibility at both college and department levels. A strategic framework of the DSS for enrollment forecasting is presented. Administration issues are also addressed.