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How Rule Induction Data Mining Can and Cannot Be Useful for Education Research

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dc.contributor.author Iwatani, Emi
dc.date.accessioned 2019-07-12T23:11:37Z
dc.date.available 2019-07-12T23:11:37Z
dc.date.issued 2019-04
dc.identifier.uri http://hdl.handle.net/20.500.12265/76
dc.description.abstract This paper shares insights and recommendations on how rule-induction data mining can and cannot be useful to education research, based on re-analyzing two regression studies with rule induction approaches. Processes and findings were compared to identify whether, in what ways, and why rule-induction could add value. I found that rule-based approaches can provide unique descriptions of the sample that shows at-a-glance, how key predictors relate to each other and to the outcome. They can also identify relationships between variables that held for some subgroups but not others. It was important to clearly understand the difference between mining rules and mining rulesets, as well as the unique research questions that these answer, so that they complement rather than replacement regression. en_US
dc.language.iso en_US en_US
dc.publisher AERA Online Paper Repository en_US
dc.subject data mining en_US
dc.subject rule induction en_US
dc.subject education research en_US
dc.subject research methodology en_US
dc.title How Rule Induction Data Mining Can and Cannot Be Useful for Education Research en_US
dc.type Working Paper en_US


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