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Differential item functioning method as an item bias indicator for big data assessment in the 21st century.
Differential item functioning is an approach that is widely used to find out test items that have bias, especially as it regards to Big Data Assessment. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. This study sought to find out items that are biased using differential item functioning approach in relation to school type (private and public schools), school location (urban and rural schools) using National Business and Technical Examination Board (NABTEB) Agricultural Science, Biology and Physics Multiple choice Test items used in 2015 NABTEB Examination. The research design employed in this study was Expo-Facto research design. The sample comprised students in Abia State, Nigeria. Five hundred and forty three (543) students were used for Agricultural Science, 583 students for Biology and 518 students for Physics making a total of 1644 participants. The test contained 50 items each which were administered to the students. Logistic regression was used to analyze the data. The research findings showed that some items in NABTEB Agricultural Science, Biology and Physics were biased in relation to school type and some items in relation to school location. The implication of these findings is that NABTEB Agricultural Science, Biology and Physics examinations questions have item biases which could be deleted. From the result of the findings, it was then recommended that test experts and developers should explore the use of DIF approach to detect biased items in Big Data Assessment.
Keywords: Differential item functioning, Big Data Assessment, Agricultural Science, Physics and Biology.
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