Developing Assessment Practices in Politics

Abstract

Up until the early 1990s the traditional approach to teaching and learning in Politics was to use examinations and essays as the primary method of assessing students. This was somewhat in contrast to other subject- based disciplines, such as the Sciences, where the nature of the discipline necessitated the use of a wider pattern of assessment, including class- based tests in the form of laboratory work. Other subjects, such as Business Studies, tended to make greater use of work placements and case studies. Over the last two decades there has been a steady expansion in research on teaching and learning from a Politics perspective, with a considerable focus being attached to the dissemination of different approaches to teaching and learning. This chapter draws on one aspect of this work by focussing on the importance of the assessment regime in the teaching of Politics. In doing so, it draws on the work of a National Teaching Fellowship Scheme (NTFS) project, ‘It's Good to Talk: Feedback, Dialogue and Learning’ that seeks to identify, evaluate, develop and promote ways to improve feedback to students within the disciplines of History and Politics/International Relations. At the centre of the project is the issue of encouraging teacher and peer dialogue around learning by drawing on feedback approaches in three universities: De Montfort University, London Metropolitan University, and the University of Warwick. The chapter proceeds as follows. First, it reviews the context of assessment within the teaching and learning of Politics. Second, it analyses the significance of assessment. Third, it investigates different types of assessment. Fourth, it examines the importance of assessment feedback for students. Finally, it presents a concluding argument that a central feature of any method of assessment should be the objective of developing student engagement in ‘deep’ rather than ‘surface’ learning.KeywordsAssessment PracticeDeep ApproachReflective PracticeSurface ApproachPlacement LearningThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.