Weighted social tagging as a research methodology for determining systemic trends in engineering education research

As a new and emerging problem space, engineering education research continues to define its core content, methods, and theory. However, the field of engineering education research has not fully utilized or innovated new methods that leverage more modern web 2.0 techniques to understand systemic trends within the problem space. In this paper, we introduce a new technique called weighted social tagging as a research methodology. As opposed to simple frequency counts to generate word clouds, weighted social tagging allows users to assign relative weights and corresponding confidence ratings to each of the tags. We demonstrate the application of weighted social tagging on a small-scale dataset of papers from the Journal of Engineering Education (JEE) that extend over a period of 5 years from 2005 to 2009-a total of 152 papers. We attempt to address the following questions: (1) How effective is weighted social tagging compared to frequency counting in identifying trends and core concepts? (2) What trends and core topics in JEE from 2005 to 2009 can be identified through weighted social tagging method? (3) How do they vary qualitatively from the trends identified by just counting word frequencies (e.g. Wordle)? Using techniques found in the field of data mining and visual analytics, we show how the weighted social tagging method can be combined with graph-based visualization techniques to gain a deeper understanding of engineering education research literature. The power of this technique lies in its ability to quickly leverage the collective intelligence of a community of researchers. Clearly, just one reader's tags will be insufficient to derive the full context and meaning of a paper. However, when we engage a large community of researchers, the tags as a collection render a significant portion of the meaning of a dataset. When this dataset is placed on a timeline, trends of concepts, methodologies, and findings begin to emerge.