Projection Model of Postgraduate Student Flow

Rahim, Rahela Abdul; Ibrahim, Haslinda; Kasim, Maznah Mat; Adnan, Farah Adibah

doi:10.12785/amis/072l01

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…There are different methods for demand prediction such as cohort, participation rate, regression and time series. These methods are used in the current research, however there are also other methods, for example Markov chain used by Rahim et al (2013), logistic regression by Logan (2010), multiple-method approach based on the application of a cohort model (macro level) and regression techniques (Gaither et al, 1981) and Lin et al (2009) that compared four modeling methods for higher education prediction: neural networks, logistic regression, discriminate analysis and structural equation modeling. Prediction performance results show that the neural network method produced the best prediction results among these four methods.…”

Section: Review Of Literaturementioning

confidence: 99%

Higher education demand estimation & prediction by 2025 in Iran

Ghavidel

Jahani

2015

Journal of Applied Research in Higher Education

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Purpose – The purpose of this paper is to predict the number of undergraduate applicants for the National Entrance Examination in Iran during 2012-2025 periods and to identity the factors affecting the demand for higher education in Iran. Design/methodology/approach – In this paper, the method of cohort, participation rate, structural regression and time series econometric models have been used. In the present study, it has been predicted by using four methods mentioned and at the next step, in addition to identifying effective factors, the results given from these four methods have been compared with each other. Furthermore, the most important factors influencing university enrollment decision have been identified by econometric method. Findings – The results of estimating the number of the criteria applicants, show that the tendency to pursue studies is different between males and females. Therefore, their structural models differ from each other. The results of forecast in structural method support the high effectiveness of economic growth index. Most predictions are often confirmed the reduction in the number of applicants during the 2012-2025 period, especially for men. Social implications – This paper can be helpful in opening up a discourse around cross-cultural elements in higher education demands and planning for higher education. Originality/value – It’s important to forecast the demand for higher education using different methods, and to compare the results for specific countries.

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Section: Review Of Literaturementioning

confidence: 99%

Higher education demand estimation & prediction by 2025 in Iran

Ghavidel

Jahani

2015

Journal of Applied Research in Higher Education

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“…Although the typical transitions refer to students moving from first year to second year, for example, and eventually to either program completion or dropping out, the state definitions can be extended to help meet needs specific to particular applications. Gandy et al (2019) add cumulative credit hour ranges and Rahim et al (2013) add age group ranges to their state definitions to gain additional insights as well as predictive power. Nicholls (2007) specifically focuses on the use of the Markov chain model for improving the program completion results for master's and PhD students, and also identifies the usefulness of the models for longer-term analyses.…”

Section: Literaturementioning

confidence: 99%

Student Trajectories for Enrollment Forecasting, Management, and Planning

Bowman¹

2021

AIR Professional File

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The School of Management (SOM) at Union Graduate College (UGC) used student trajectories to forecast both individual course and total enrollments within and across various student categories. (The term "trajectory" refers to the enrollment pattern of the average student in a particular category.) The trajectories and the resultant projections formed an integral part of diagnostic tools for enrollment management decision-making, short-and long-term course section planning, and faculty capacity analyses in a variety of scenarios to inform program development, program promotion, and faculty hiring, and to make sure they were consistent with each other. This article describes the concepts behind trajectories, various types of trajectories and their respective uses, details for constructing trajectories, and how trajectories can be incorporated in reports and tools for maximum utility.

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“…A mintatanterv hallgatói teljesítményre vonatkozó hatását vizsgálta Jansenés van der Hulst is [14,25], a hallgatói folyamok modellezésekor figyelembe véve a tanulók korát, nemét, tanulmányiátlagátés a középiskolás eredményeit. Rahimés szerzőtársai Markovlánc alapú modellezést használnak posztgraduális képzések hallgatóiáramainak elemzésére [24]. A hallgatók előrehaladásánakés végzési rátájának jobb nyomonkövethetőségeérdekében a hallgatókáramlását vizualizálták Sankey-diagramok segítségével friss cikkükben Horváthés szerzőtársai [13].…”

Section: Bevezetésunclassified

Kreditrendszerű képzések mintatanterveinek és előtanulmányi hálóinak elemzése a hazai matematika alapszakok példáján

Bergmann¹,

Molontay²,

Szabó³

et al. 2020

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Dolgozatunkban egyrészt a kreditrendszerű képzések előtanulmányi hálóinak jellemzéséreésösszehasonlíthatóságára mutatunk néhány módszert, majd ezeket a magyarországi matematika alapképzéseken szemléltetjük.Áttekintjük a tantervi hálók elemzésének irodalmát, illetve bemutatunk egy teljesítési adatokra alapuló valószínűségi modellt is. Ezen módszerek segítségével olyan kérdésekre kaphatunk választ, hogy egy adott tantervben melyek a legfontosabb, leghangsúlyosabb tantárgyak, illetve az előtanulmányi háló topológiája hogyan hat a képzés várható teljesítési idejére. Másrészt ismertetünk néhány felvételi statisztikát az elmúltévekből, többek között a matematika alapszakokra felvett hallgatók létszámára, illetve felvételi pontszámaira vonatkozóan.

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Projection Model of Postgraduate Student Flow

Cited by 5 publications

References 10 publications

Higher education demand estimation & prediction by 2025 in Iran

Higher education demand estimation & prediction by 2025 in Iran

Student Trajectories for Enrollment Forecasting, Management, and Planning

Kreditrendszerű képzések mintatanterveinek és előtanulmányi hálóinak elemzése a hazai matematika alapszakok példáján

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