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Modelling factors for predicting student learning outcomes in higher education

Price, Linda (2014). Modelling factors for predicting student learning outcomes in higher education. In: Gijbels, David; Donche, Vincent; Richardson, John T. E. and Vermunt, Jan D. eds. Learning Patterns in Higher Education: Dimensions and Research Perspectives. New Perspectives on Learning and Instruction. Abingdon: Routledge, pp. 56–77.

URL: http://www.routledge.com/books/details/97804158425...
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Abstract

This chapter presents a heuristic model of student leaning as a means to understanding the scope of factors to be considered in making predictions about student learning. It is underpinned by a review of a wide body of literature. The model is drawn from Price and Richardson’s 4P model (2004) that considered factors in improving student learning and argues that the same issues apply to predicting student learning outcomes. It builds upon existing research into learning and teaching. It is an articulation and an extension of Dunkin and Biddle’s (1974) model, the Biggs (1985) original Presage-Process-Product model and research by Prosser and Trigwell (1999). The model has four main groups of factors: presage, perceptions, process and product. The presage group contains personological and situational factors such as context. Perceptions include how students conceive learning, how teachers conceive teaching, and the context. The process group of factors incorporates approaches to learning in students and teachers approaches to teaching. The model is presented as a basis for engaging in future research in a holistic manner that may bear further fruit in predicting student learning.

Item Type: Book Section
Copyright Holders: 2014 David Gijbels, Vincent Donche, John T. E. Richardson, Jan D. Vermunt
ISBN: 0-415-84252-2, 978-0-415-84252-5
Academic Unit/School: Learning and Teaching Innovation (LTI) > Institute of Educational Technology (IET)
Learning and Teaching Innovation (LTI)
Research Group: Centre for Research in Education and Educational Technology (CREET)
Item ID: 39321
Depositing User: Linda Price
Date Deposited: 27 Jan 2014 09:48
Last Modified: 08 May 2019 13:37
URI: http://oro.open.ac.uk/id/eprint/39321
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