Simulating student flow through a college of business for policy and structural change analysis

Saltzman, Robert M.; Roeder, Thomas

doi:10.1057/jors.2011.59

Cited by 9 publications

(11 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A mintatantervekés előtanulmányi hálók vizsgálatának másik megközelítési módja a hallgatói folyamok modellezése. Számos tanulmányban vizsgálták hallgatóiáramokat szimulálva, hogy a mintatantervekátszervezésének milyen hatása lenne a hallgatók előrehaladására vonatkozóan [19,23,26]. 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.…”

Section: Bevezetésunclassified

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

Bergmann¹,

Molontay²,

Szabó³

et al. 2020

AML

View full text Add to dashboard Cite

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.

show abstract

Section: Bevezetésunclassified

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

Bergmann¹,

Molontay²,

Szabó³

et al. 2020

AML

View full text Add to dashboard Cite

show abstract

“…Studies differ in the mathematical representation -a simple Leontief input-output depiction of interdependence between students at various education levels (Stone 1965;Oliver and Hopkins 1972); a sequence of discrete events in time (students in different modules on a programme, for example) to which simulation can be applied (Saltzman and Roeder 2012); a Markov chain framework based on students in each state (education level, for example) and their probability of moving to another state -but all are capable of providing forecasts of student numbers 3 .…”

Section: Planningmentioning

confidence: 99%

“…Because they are based on student flows from state to state, Markov chain models have proved particularly useful at the faculty and programme levels in providing not just predictions of students but also additional insights into, for example, noncompletion both in postgraduate programmes (Bessent and Bessent 1980;Nicholls 2007) and undergraduate programmes (Shah and Burke 1999), required deployment of supervisors in a doctoral programme (Nicholls 2009), and evaluation of the efficacy of early-retirement programmes for university faculty (Hopkins 1974). There are fewer examples of the application of simulation to students flows; one such study, however, has proved useful in evaluating the potential effects on students, in terms of their time to complete the programme and graduation rates, of changes in curriculum provision brought about by recent budget cuts (Saltzman and Roeder 2012).…”

Section: Planningmentioning

confidence: 99%

Operational Research in education

Johnes

2015

European Journal of Operational Research

120

View full text Add to dashboard Cite

Operational Research (OR) techniques have been applied, from the early stages of the discipline, to a wide variety of issues in education. At the government level, these include questions of what resources should be allocated to education as a whole and how these should be divided amongst the individual sectors of education and the institutions within the sectors. Another pertinent issue concerns the efficient operation of institutions, how to measure it, and whether resource allocation can be used to incentivise efficiency savings. Local governments, as well as being concerned with issues of resource allocation, may also need to make decisions regarding, for example, the creation and location of new institutions or closure of existing ones, as well as the day-to-day logistics of getting pupils to schools. Issues of concern for managers within schools and colleges include allocating the budgets, scheduling lessons and the assignment of students to courses. This survey provides an overview of the diverse problems faced by government, managers and consumers of education, and the OR techniques which have typically been applied in an effort to improve operations and provide solutions.

show abstract

“…The prerequisite network is a directed graph (network) where the nodes correspond to the courses (also referred to as subjects or classes) and an edge goes from one course to another if the former course is a prerequisite of the latter one. The analysis of the prerequisite network is extremely important since it determines the learning goals of the program, moreover, the structure of the network has a huge impact on dropout rates and on graduation time [1], [2]. Here, we characterize university curricula by a data-driven probabilistic student flow approach and determine the expected graduation time by considering both the topology of the prerequisite network and the completion rates of the courses.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Curriculum Prerequisite Networks by a Student Flow Approach

Molontay

Horvath

Bergmann

et al. 2020

IEEE Trans. Learning Technol.

View full text Add to dashboard Cite

Curriculum prerequisite networks have a central role in shaping the course of university programs. The analysis of prerequisite networks has attracted a lot of research interest recently since designing an appropriate network is of great importance both academically and economically. It determines the learning goals of the program and also has a huge impact on completion time and dropping out. In this article, we introduce a data-driven probabilistic student flow approach to characterize prerequisite networks and study the distribution of graduation time based on the network topology and on the completion rate of the courses. We also present a method to identify courses that have a significant impact on graduation time. Our student flow approach is also capable of simulating the effects of policy changes and modifications of the network. We compare our methods to other techniques from the literature that measure structural properties of prerequisite networks using the example of the electrical engineering program of the

show abstract

Simulating student flow through a college of business for policy and structural change analysis

Cited by 9 publications

References 9 publications

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

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

Operational Research in education

Characterizing Curriculum Prerequisite Networks by a Student Flow Approach

Contact Info

Product

Resources

About