Forecasting enrollments in a Virginia community college

Most institutions of higher education are interested in enrollment projections because they are closely related to institutional goals and missions and, are, therefore, essential to financial and program planning at every level. This study was undertaken to determine if relevant factors could be identified and used in a statistical forecasting model to project enrollments in a multidimensional urban community college within the accuracy limitations imposed by a state such as Virginia (who requires State institutions of higher education) to project their enrollment within (+OR-) 1 percent.;Two general types of statistical forecasting models, causal and extrapolation models were explored for use in forecasting fall and summer headcount, and total FTE enrollments within the prescribed accuracy limits. The relevant factors for possible inclusion in the models were identified from previous studies and a student flow model for the institution. The relevant factors used in the final models were selected on the basis of simple correlation coefficients, the mean square error, and average error as variables were added and removed from the models.;The optimum fall and summer headcount forecasts were produced by a combination time-series and multiple regression model. The independent variables used in fall and summer headcount forecasts were a seasonal factor, a time-trend factor, and national economic indicators. In the optimum total FTE forecast, produced by a multiple regression model, the relevant factors were full-time enrollment shifted forward three years and national economic indicator shifted forward three years. The basis for acceptance or rejection of the models was made in context with the fiscal system of the Commonwealth of Virginia for the distribution of public funds to the state colleges and universities. The fiscal system was established primarily to provide a basis for financial planning. Forecasting models were produced for 1 year for fall headcount enrollment and for 2 years for summer headcount and total FTE enrollment within (+OR-) 1 percent.;On the basis of this study certain general conclusions were reached: the large variations between national enrollment projections resulted from different assumptions; enrollment projections have been too generalized for institutions with diverse goals and objectives; present data bases are inadequate to produce accurate enrollment projections; and most projections are not sufficiently reliable for planning purposes. More specific conclusions reached were: state data bases are inadequate for multidimensional institutions; removing quarterly seasonal variations permits the identification of relevant factors; traditional projection models such as the cohort survival and Markov are not applicable in multidimensional institutions such as community colleges; models such as time-series and multiple regression can be developed to accurately project enrollments for less than two years; the current limits of accuracy for Virginia multidimensional institutions are unrealistic; verification of the accuracy of prediction models is valuable for evaluating forecasting models; and models for multidimensional institutions must be revised periodically because relevant factors are constantly in flux

When the Future is not what it used to be: Lessons from the Western European Experience to Forecasting Education and Training in Transition Economies

In an era of rapid technological change, information exchange, and emergence of knowledge-intensive industries it is critical to be able to identify the future skill needs of the labour market. Growing unemployment in EU member states and pre-accession countries in Eastern Europe combined with technological changes which make the skills of a significant number of workers obsolescent each year demand adequate knowledge of medium- and long-term demand for specific skills. Some EU members states have developed employment forecasting methods to identify future skill requirements which take account of the sectoral, occupational, and educational and training factors which influence supply and demand in the labour market for skills. A number of countries in Eastern Europe which are preparing to join the EU are interested in developing employment forecasting models that would provide them with similar information relating to skills. Taking account of the requirements of the Single European Market and increasing international mobility, it is desirable that the pre-accession countries should develop models which, if possible, are comparable with existing methods of forecasting training and qualification needs in existing member states of the EU. This task requires regular medium-term forecasts which will extend the time horizon of decision makers beyond the current economic cycle, be applicable to the whole economy, allow speedy adjustment to changing circumstances, and which will take account of relevant factors such as investment plans, output and labour productivity forecasts, and technological change. The objective of this paper is to provide a summary of existing methods and data sets used to forecast education and training needs in four members of the European Union, in order to motivate similar work in three pre-accession countries. We first provide a detailed account of the different approaches to forecast education and training needs in France, Germany, Ireland and The Netherlands. For each of these countries, we consider the labour market data on which employment forecasts are based and the current methods in use, examine how data reliability and accuracy of forecasts are dealt with, and discuss the dissemination and usage of forecast information generated by those systems. We then look at the same range of issues for three pre-accession Central European countries (Czech Republic, Poland and Slovenia.) The paper concludes by suggesting a number of needed actions in preparation for developing an approach to forecasting education and training needs in the three pre-accession countries.http://deepblue.lib.umich.edu/bitstream/2027.42/39650/3/wp265.pd

Forecasting Pre-K Enrollment in Georgia Counties

This report provides a manual that documents the forecasting methodology and provides the actual forecast of Pre-K enrollment by county for 2007-2011. FRC Report 15

Managing stimulation of regional innovation subjects’ interaction in the digital economy

The reported study was funded by RFBR according to the research project No. 18-01000204_a, No. 16-07-00031_a, No. 18-07-00975_a.Purpose: The article is devoted to solving fundamental scientific problems in the scope of the development of forecasting modeling methods and evaluation of regional company’s innovative development parameters, synthesizing new methods of big data processing and intelligent analysis, as well as methods of knowledge eliciting and forecasting the dynamics of regional innovation developments through benchmarking. Design/Methodology/Approach: For regional economic development, it is required to identify the mechanisms that contribute to (or impede) the innovative economic development of the regions. The synergetic approach to management is based on the fact that there are multiple paths of IS development (scenarios with different probabilities), although it is necessary to reach the required attractor by meeting the management goals. Findings: The present research is focused on obtainment of new knowledge in creating a technique of multi-agent search, collection and processing of data on company’s innovative development indicators, models and methods of intelligent analysis of the collected data. Practical Implications: The author developed recommendations before starting the process of institutional changes in a specific regional innovation system. The article formulates recommendations on the implementation of institutional changes in the region taking into account the sociocultural characteristics of the region’s population. Originality/Value: It is the first time, when a complex of models and methods is based on the use of a convergent model of large data volumes processing is presented.peer-reviewe

The efficacy of using data mining techniques in predicting academic performance of architecture students.

In recent years, there has been a tremendous increase in the number of applicants seeking placement in the undergraduate architecture programme. It is important to identify new intakes who possess the capability to succeed during the selection phase of admission at universities. Admission variable (i.e. prior academic achievement) is one of the most important criteria considered during selection process. The present study investigates the efficacy of using data mining techniques to predict academic performance of architecture student based on information contained in prior academic achievement. The input variables, i.e. prior academic achievement, were extracted from students' academic records. Logistic regression and support vector machine (SVM) are the data mining techniques adopted in this study. The collected data was divided into two parts. The first part was used for training the model, while the other part was used to evaluate the predictive accuracy of the developed models. The results revealed that SVM model outperformed the logistic regression model in terms of accuracy. Taken together, it is evident that prior academic achievement are good predictors of academic performance of architecture students. Although the factors affecting academic performance of students are numerous, the present study focuses on the effect of prior academic achievement on academic performance of architecture students. The developed SVM model can be used a decision-making tool for selecting new intakes into the architecture program at Nigerian universities

Passing the California High School Exit Exam: Have Recent Policies Improved Student Performance?

This report evaluates the effectiveness of three support services in helping struggling students pass the California High School Exit Examination (CAHSEE). The report highlights the need to help students before they first take the exam in grade 10 and introduces the CAHSEE Early Warning Model, a forecasting tool to identify at-risk students in earlier grades