@phdthesis{discovery10007413,
          school = {Institute of Education, University of London},
           title = {An application of multilevel modelling techniques to the longitudinal study of student progress in a modular degree course},
            year = {2001},
            note = {Unpublished},
             url = {http://ethos.bl.uk/ProcessSearch.do?query=536177},
          author = {Simonite, Vanessa},
        abstract = {This thesis presents a longitudinal study of undergraduate achievement within a
modular first degree course, analysing the academic records of a cohort of students who
graduated from the Modular Degree Programme at Oxford Brookes University. Multilevel
models are fitted to the marks achieved by members of this cohort in each module taken.
Level 1 units are individual module entries, nested within occasions within individual
student's programmes. These models were fitted by maximum likelihood and used to study
the effects of both student and module characteristics on performance. The effects of these
factors on mean marks, on the consistency of students performance and on the variation
between students were studied by including complex variation at level 1 and random effects
at student level in the models. In addition, individual progress charts were fitted, showing
how patterns of progress vary from one student to another.
Reviewing the hierarchical structure, it was found that a more complex, crossclassified
structure is needed to represent the data accurately. This recognises that individual
module entries are clustered within modules, as well as within students. Fitting large
multilevel cross-classified models is computationally difficult, however newly developed
MCMC estimation techniques allowed a model based on the more complex structure and
including random effects and complex variation to be fitted. This analysis shows how MCMC
estimation techniques can be used to fit a large cross-classified multilevel model,
incorporating random effects and complex variation. The results obtained describe students'
progress over the period of their degree course and measure the effects, other things being
equal, of factors such as assessment methods, age and subject on mean levels of achievement,
consistency of performance and the variation between students, providing a model for future
studies of achievement within a modular framework.}
}