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ENCOURAGING DEEP LEARNING THROUGH COLLABORATIVE PROJECTS AND PUBLIC EXHIBITION

Journal: PEOPLE: International Journal of Social Sciences (Vol.3, No. 3)

Publication Date:

Authors : ;

Page : 1108-1124

Keywords : Product design; Project based learning; deep learning; Collaboration; Education;

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Abstract

This paper will present the advantages in the use of methodologies and tools of project based learning (PBL) in the teaching of a group of year one product design students in the Hong Kong context. The use of projects within the curriculum is not a revolutionary idea in design education nor education in general. In fact, PBL within design education at the tertiary level is common practice because its content is similar to real world jobs, in which tasks and projects are encountered by workers in their everyday jobs. Through PBL students can investigate things and explore some new insights within a working environment, in which over a period of time they can produce some realistic outputs (Kumari & Nandal.) It has also been noted by educators that long projects within PBL, where students work together, can promote deep learning (Prenuel et al.) This paper focuses on one project within a particular first year module taken by product design students in the first semester as part of their four-year degree programme. The research will look at how student learning can be enhanced by the use of PBL and how it can be further strengthened by collaboration with external organizations with the incentive of having selected students' work exhibited in a major cultural event. A simple questionnaire was used to gauge the receptiveness of the students taking part in the module to this mode of collaboration to see whether this had any effect on their learning and outcome of the project. The findings are expected to show that collaborating with external parties will enhance the learning experience for the students and that the works from the project will demonstrate the link between the desire to perform well (i.e. to have one's work exhibited) as a catalyst for deep learning.

Last modified: 2018-04-27 18:56:41