An investigation into the contribution of student profile, student-lecturer interaction and student workload in teaching evaluations

Background: These days, in higher education institutions, teaching evaluations are compulsory for academics. They are used as a measure of the quality of teaching provided by the academics to students. However, teaching evaluations can be affected by many variables over which academics may not have any control. These can either be the nature of a unit (the level of mathematical knowledge required for a particular unit in an engineering course), interaction between students and the lecturer, the profile of students (gender, academic and cultural background), or the profile of lecturers (personality, expertise, gender, cultural background and general appearance). In addition, assessment tasks developed by academics aimed at improving the standard of units can also have an impact on teaching evaluations as they will increase the workload of a particular unit relative to the other units for the same cohort of students. Therefore it is questionable whether teaching evaluations provide a true picture of a lecturer's ability or effectiveness in teaching. Purpose: This paper investigates the influence of student profile, student-lecturer interaction and increased student workload on teaching evaluations. Also, the paper investigates whether there is any correlation between teaching evaluations and student grades. Design/Method: Teaching evaluations received by a lecturer while teaching at two Australian universities are used for this study. These evaluations are collected for two units in the Engineering program with similar content. Also, student grades are collected for these units at the end of semester during the same period over which teaching evaluations were obtained. The student entry requirements and demographics of the two universities are used to define the profile of students. Hence the profile defined is merely an indicator of the academic achievement of students before gaining entry to each university and the cultural background. Also, the profile of the lecturer is not included because the collected teaching evaluations are for one lecturer. The influence of student workload is assessed based on the assessments given to students during the semester which contribute to the final grade, and the student-lecturer interaction is studied using teaching evaluations for small and large classes. Results: The data collected during this investigation clearly demonstrates that there is a correlation between student profile, student workload for a particular unit, student-lecturer interaction and the teaching evaluations. Conversely, there is no correlation between student grades or pass rate and the teaching evaluations. Conclusions: It appears that the profile of students, workload and student-lecturer interaction may have a significant influence on the teaching evaluations. Use of this data to measure the quality of teaching provided by academics may not be ideal. Hence other measures such as peer review processes by colleagues, and questionnaires with open ended questions rather than quantitative measures need to be integrated with traditional teaching evaluations.