Issues in interpreting student feedback statistical data
thesisposted on 08.10.2018 by Dean Polworth
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Student feedback ratings are becoming an increasingly utilised means of measuring the quality of the student learning experience in U.K. universities. Despite this there has been little published statistical analysis on student feedback ratings using U.K. data. The research explores whether lecturers should have confidence in the validity of the student feedback ratings they receive. Guidance in the presentation and interpretation of the data is offered. This is intended to facilitate a more sophisticated understanding of the data, allowing decisions based on it to be made on a more informed basis. The research used the student feedback data collected on all taught modules (both undergraduate and postgraduate) in the Business School at Loughborough University over two academic years (October 1996–June 1998). This consisted of 305 modules and 13813 individual student feedback forms. The thesis contributes to the literature in the following ways: (1) Through illustrating the existence of heterogeneous groups of students in many Business School modules, which reflect the presence of different learning styles being utilised by Business School students, and discussing the consequences of this for: (a) the use of factor analysis on student feedback data; (b) the appropriateness of reporting the results of student feedback in the form of class averages. (2) Through illustrating the effects on student feedback ratings specific to modules taught by more than one lecturer. Two variables not previously reported in the student feedback literature are shown to influence the ratings that lecturers receive, namely: (a) the proportion of lecturing hours undertaken by a lecturer on a particular module; (b) the ratings of the lecturer(s) with whom a lecturer teaches alongside on a particular module. (3) Through examining the impact of external factors on the ratings lecturers receive. Regression analysis is used to model the influence of a set of nine predictor variables on student feedback ratings. Lecturers' ratings are shown to be significantly influenced by the level of the module, the class size and the subject area of the module. Characteristics of the lecturer, namely, the lecturer's age, rank and experience are shown to significantly influence lecturers' ratings for some aspects of lecturing.
- Business and Economics