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2019-01-25  |  Making meaning out of Big Data: US expert presents Quantitative Ethnography at the IWM
2019 01 23 Presentation David Shaffer
© IWM Tuebingen

"My smartphone, for example, knows more about what I do than my wife," the renowned US professor David Williamson Shaffer opened his guest lecture at the Tübingen IWM on January 15 and introduced the audience so vividly to the subject of Quantitative Ethnography: Turning Big Data into Real Understanding. "In the age of Big Data, there is much more information available about what people do and how they do it than ever before," said the learning science expert from the University of Wisconsin-Madison, USA. "The challenge is to gain meaning from these large amounts of data, where traditional methods such as significance testing find only arbitrary patterns and are therefore not applicable," Shaffer continues. New statistical methods are therefore needed to allow researchers, for example, not only to determine whether learning tools are effective for students, but also to show why.

David Shaffer hopes to solve this problem with a new method: Quantitative ethnography which he presented in his lecture. It merges statistical and ethnographic analyses that were previously carried out separately. For this purpose, Shaffer and his colleagues are developing the Epistemic Network Analysis (ENA) tool to model how codes are linked in a data set. ENA models visualize a system of interconnected codes, enabling researchers to quantify and test the differences between them using statistical methods. The model should then, for example, identify critical components that are decisive for the success of a learning tool and point out correlations between the components.

"These models will certainly become even more relevant in the future as we try to understand learning and knowledge processes through Big Data," says Nora Umbach, scientific assistant for method consulting at the IWM. She therefore expects them to be of direct relevance for research at the IWM. "All the more reason for us to be pleased that David Shaffer and his research group will cooperate with us in the future in order to support the institute in the application of his methods," states Nora Umbach.

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