The research reported in this thesis investigated issues relating to the use of computer-assisted assessment in Higher Education through the design, implementation and evaluation of a computer-adaptive test (CAT) for the assessment of and provision of feedback to Computer Science undergraduates. The CAT developed for this research unobtrusively monitors the performance of students during a test, and then employs this information to adapt the sequence and level of difficulty of the questions to individual students. The information about each student performance obtained through the CAT is subsequently employed for the automated generation of feedback that is tailored to each individual student. In the first phase of the research, a total of twelve empirical studies were carried out in order to investigate issues related to the adaptive algorithm, stakeholders’ attitude, and validity and reliability of the approach. The CAT approach was found to be valid and reliable, and also effective at tailoring the level of difficulty of the test to the ability of individual students. The two main groups of stakeholders, students and academic staff, both exhibited a positive attitude towards the CAT approach and the user interface. The second phase of the research was concerned with the design, implementation and evaluation of an automated feedback prototype based on the CAT approach. Five empirical studies were conducted in order to assess stakeholders’ attitude towards the automated feedback, and its effectiveness at providing feedback on performance. It was found that both groups of stakeholders exhibited a positive attitude towards the feedback approach. Furthermore, it was found that the approach was effective at identifying the strengths and weaknesses of individual students, and at supporting the adaptive selection of learning resources that meet their educational needs. This work discusses the implications of the use of the CAT approach in Higher Education assessment. In addition, it demonstrates the ways in which the adaptive test generated by the CAT approach can be used to provide students with tailored feedback that is timely and useful.

Universities are under pressure to justify the time and expense expended by students in obtaining a degree which has stimulated interest in measuring more formally how learning outcomes have been met by students. The 1997 NCHE (Dearing) Report called for improvements in higher education (HE) assessment practice and while assessment is widely regarded as the critical catalyst for student learning it is often in practice relegated to an afterthought. The potential for information and communications technology (ICT) to automate aspects of learning and teaching is widely acknowledged although promised productivity benefits have been slow to appear. Computer-assisted assessment (CAA) is seen by many as one way of meeting these conflicting demands. CAA has considerable potential both to ease the assessment load and to provide innovative and powerful modes of assessment. Moreover, as the use of ICT increases there may be ‘inherent difficulties in teaching and learning online and assessing on paper’. Given the importance of assessment activities in higher education, the level of current interest in CAA and widespread disagreement about how it should be implemented, there is a clear need for rigorous, grounded models of good practice. A national survey of CAA practice was conducted using online tools and interviews with enthusiast and early adopting CAA experts and practitioners throughout the UK which explored the critical factors associated with the uptake and embedding of CAA. A grounded theory analysis of the interview and survey data was carried out and a theory of dual path CAA uptake in universities emerged from which three models of uptake were derived. These were validated against qualitative data obtained from a final set of interviews and by triangulation with survey data from the 2003/4 UK CAA survey. Tutors’ motivations and perceptions of risk influence the way they use CAA and this is significant in credit-bearing applications where non-optimal outcomes have long lasting effects on uptake. Institutions can benefit from using project risk management techniques to manage these risks.