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Knowledge Genesis - bridging gaps between learning and understanding
Colbeck, DF (2009) Knowledge Genesis - bridging gaps between learning and understanding. PhD thesis, University of Tasmania.
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As part of trying to understand the world around us, we all engage in the classification and assimilation of new information and knowledge, sometimes with the intention of enhancing our understanding, other times as an attempt to try and rationalise how and where we fit in this often complex world around us. This process of how we discover, qualify and justify differing forms of information and then integrate new information into our pre-existing personal beliefs, redefines our unique personal knowledge database, enabling us to learn new things. This research is an investigative study into previously unseen personal epistemological belief structures as maintained by clusters of tertiary level undergraduate learners. This study also stands as an exemplar for the methodologies that were developed and utilised in the data harvesting, computational analysis and graphical illustrations of these revealing structures. The data for this study was harvested using a purposively designed survey instrument. Much deliberation and calculation went into its construction, deployment and subsequent analysis of the response data. The harvested data was then subjected several differing trial analysis processes before a final three phase analytical methodological approach was determined. The first phase comprised a quantitative multivariate factor analysis utilising Principal Factor Analysis which was also augmented by obliquely rotating the dataset within Euclidean space to calculate meaningful and appropriate factor loadings. Secondly, a multiple regression analysis was applied to the data, revealing correlational relationships between the observed factor loadings. Finally, a qualitative overlay codified data analysis founded on grounded analysis techniques was applied to the factor statement groupings in order to enhance as well as offer rich detail to the data being observed. This mixed-method stance of quantitative and qualitative analysis is gaining greater global acceptance within the field of social research by not only offering greater insight into the data being observed, but also by providing more meaningful interpretation and conclusions from the entire analysis process. Some of the conclusions reached within this research include the provision of evidence toward: - • That there are indeed contextually unique, quantifiably founded, hierarchical structures of epistemological beliefs being maintained by clusters of learners. • That these beliefs are not more or less independent as previously hypothesised, but do in fact appear reciprocally connected within the context of each of the epistemological belief structures observed. • That these belief structures were also observed to differ when segregated into meta-domain representations of Gender, Domain and Nationality based criterion. The observed structures did however remain somewhat domain dependent, with learners within similar courses of study demonstrating comparable belief constructs. By understanding epistemic belief structures and using them to develop new strategies aimed at positively influencing learners’ personal epistemological beliefs, learners will become more active, higher level, independent thinkers by improving their own personal literacy development, thus allowing them to span the gap between their own learning and understanding. The transitional journey undertaken to establish the meticulous methodologies used within this study proved truly exhaustive, and it is hoped that the findings herein revealed will enhance the understanding of fundamental belief principles and inform instructional design practices as well as the wider academic community as a whole.
|Item Type:||Thesis (PhD)|
|Keywords:||epistemology learning education teaching elearning spss|
|Collections:||University of Tasmania > University of Tasmania Theses|
|Date Deposited:||29 Jun 2009 03:35|
|Last Modified:||18 Nov 2014 04:00|
|Item Statistics:||View statistics for this item|
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