A Markovian evaluation of a tertiary education faculty

Abstract

A tertiary education faculty is modelled using an absorbing Markov chain. The model takes account explicitly of full-time and part-time student stocks and flows, and thus facilitates some interesting observations. The evaluation of steady state statistics, gives rise to some worrying results, raising the question as to how efficiently resources are being utilized in tertiary (post secondary) education. The above evaluation is performed for undergraduate sections of the Faculty of Business at the Swinburne Institute of Technology (SIT), Victoria, Australia.

Operational Research in education

“…Many examples of the application of mathematical models to education planning exist at both national and institution level (Gani 1963;Clough and McReynolds 1966;Armitage and Smith 1967;Correa 1967; Forecasting Institute of the Swedish Central Bureau of Statistics 1967; Thonstad 1967;Harden and Tcheng 1971;Massy 1976;Smith 1978;Gray 1980;Nicholls 1983;Kwak et al 1986;Brandeau et al 1987). 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).…”

Assessing the progress and the underlying nature of the flows of doctoral and master degree candidates using absorbing Markov chains

“…Early work by Burke (1972) and Nicholls (1983) analysed the flow of student teachers at the undergraduate level and flows of students at undergraduate and graduate level within a business faculty, respectively. Subsequently, there appears to have been little work undertaken in this specific area with the exception of Shah and Burke (1999) who published an absorbing markov chain analysis of undergraduate students in the Australian higher education system, however, apart from Nicholls (1983), there still appears to be little work done at the graduate level.…”

“…Early work by Burke (1972) and Nicholls (1983) analysed the flow of student teachers at the undergraduate level and flows of students at undergraduate and graduate level within a business faculty, respectively. Subsequently, there appears to have been little work undertaken in this specific area with the exception of Shah and Burke (1999) who published an absorbing markov chain analysis of undergraduate students in the Australian higher education system, however, apart from Nicholls (1983), there still appears to be little work done at the graduate level. It is worth noting that large numbers of applications of absorbing markov chains in allied areas, e.g., manpower planning models in academe (Hopkins and Massey 1981), academic planning (Bowan and Schuster 1985), assessing personnel practices in higher education (Baker and Willams 1986), faculty structure (Ling and Pen-Guozhong 1987) and again, manpower planning (Hackett et al 1999) have been undertaken.…”

Projection Model of Postgraduate Student Flow

“…Transient state refers to the state where subject moves from state i during one period to state j in the next period, while the term absorbing state refers to the state where subject enters one state which is impossible to leave [9]. The transient states used in this study are the year of course enrolment [10], [11], [8], [12], [13] and the age of course enrolment [14], [12], [13], while the absorbing states for this study are dropouts and graduates from courses [14], [11], [8], [3], [12], [13]. A one-year period is used as the period of transition states [3], [12], [13], [8].…”