DIGITAL LIBRARY
A FRAMEWORK FOR PHENOMENOGRAPIC ANALYSIS AND CLASSIFICATION OF TROUBLESOME KNOWLEDGE IN THE ENGINEERING DOMAIN
1 KTH Royal Institute of Technology (SWEDEN)
2 Loughborough University (UNITED KINGDOM)
About this paper:
Appears in: EDULEARN16 Proceedings
Publication year: 2016
Pages: 5882-5888
ISBN: 978-84-608-8860-4
ISSN: 2340-1117
doi: 10.21125/edulearn.2016.0247
Conference name: 8th International Conference on Education and New Learning Technologies
Dates: 4-6 July, 2016
Location: Barcelona, Spain
Abstract:
The design of effective teaching and learning activities must create an experience able to elicit the intended learning outcomes of the educational unit. For this purpose, it is then fundamental to account for the different ways students can experience the specific content taught. This paper introduces a structured approach to perform phenomenographic studies aimed at disclosing the most common student perceptions of a given topic and highlight the patterns that can bring students with poor understanding of the target concept to a more sophisticated perception. The method has been formulated based on specific cases in the production engineering domain. In detail a phenomenographic study the first step is to describe, as a knowledgeable person would do, both the subject of the study and its domain. This description is then considered the target perception of the focal topic. In the second phase the students that have already been assessed for the educational unit in exam must be interviewed with open question about both subject and domain. Their answer must be plotted according to sound parameters along two dimension (again subject and domain related) of increasingly sophisticated level of understanding. The result of such interview must be then classified in clusters of understanding that will give the different common perception of the students about the given topic. Finally, the relation among the cluster must be studied with the aim of disclosing suitable teaching and learning activities to help students migrate to a perception cluster close to the above-mentioned target perception.
Keywords:
Phenomenology, Methodology, Engineering education