![]() These implications draw attention to the extent to. There are numerous practical implications that follow this analysis for which space limitations preclude discussion here. This paper presents some preliminary thoughts about the implications for SSM that arise out of a careful demarcation of the phenomenal domains that are involved in observing, and consideration of the relationship between them. We argue that the application of evaluative expertise to make sense of this graduate learning outcome can further the debate on how assuring graduate learning outcomes can enhance student learning. ![]() Results showed the majority of students effectively evaluated their quantitative skills as low performance-low confidence. Students were assigned to one of four categories: high performance-high confidence low performance-low confidence high performance-low confidence or low performance-high confidence – with those in the first two categories demonstrating evaluative expertise. The question guiding the study was: do final year science students graduate knowing the quantitative skills that they have, and knowing the quantitative skills that they do not have? Confidence indicators for the 10 topics gathered students’ perceptions of their quantitative skills. of quantitative reasoning questions across 10 mathematical and statistical topics. We draw on assessment theories from Sadler (evaluative expertise) and Boud (sustainable assessment) to interpret final-year bioscience students’ responses to an assessment task comprised. Efforts to evidence student attainment at the whole of degree programme level are rare and making sense of such data is complex. In the biosciences, quantitative skills are an essential graduate learning outcome. A good example is the development of a $US/day income line as a global indicator of poverty.¹ This can be usefully combined with other, similarly constructed indicators to create indexes such as the UNDP’s Human Development Index which combines indicators of economic wellbeing, health and knowledge. This is particularly useful for monitoring and evaluation, and involves the identification of a numerical indicator of a qualitative issue. Quantification involves a degree of standardization that enables us to aggregate and compare qualitative issues across time and between populations. Thus new concerns make it imperative that all research should be well theorized and designed (Campbell 2002) and that, where relevant, qualitative data should allow for a degree of standardization that comes with the use of statistical procedures. Increasingly, policy analysis requires development research to explore a range of issues and problems that traditional forms of research, with their separation of qualitative and quantitative methods, prevent. There is a long-established tradition in the social sciences of applying statistical techniques in the analysis of qualitative data (Mitchell 1980 Silverman 1985, 1993 Gilbert 1993), a trend that has expanded with the introduction of increasingly sophisticated techniques that allow ever-larger qualitative data sets to be analysed (Born 1997 White 2002a). Appealing to a pragmatic philosophical perspective, this paper argues that no incompatibility between quantitative and qualitative methods exists at either the level of practice or that of epistemology and that there are thus no good reasons for educational researchers to fear forging ahead with "what works." ![]() The chief worry is that the capitulation to "what works" ignores the incompatibility of the competing positivistic and interpretivist epistemological paradigms that purportedly undergird quantitative and qualitative methods, respectively. Progress has been halting, and it is not surprising that certain thinkers are now balking at the latest stage of development. Over approximately the last 20 years, the use of qualitative methods in educational research has evolved from being scoffed at to being viewed as useful for provisional exploration, to being accepted as a valuable alternative approach in its own right, to being embraced as capable of thoroughgoing integration with quantitative methods.
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