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Identifying predictors of physics item difficulty: A linear regression approach

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dc.creator Hasnija Muratovic
dc.creator Vanes Mesic
dc.date 2011-06-01T00:00:00Z
dc.date.accessioned 2015-07-20T22:11:54Z
dc.date.available 2015-07-20T22:11:54Z
dc.identifier 1554-9178
dc.identifier https://doaj.org/article/ddd294754bae412ca3fb58aa6e458a6d
dc.identifier.uri http://evidence.thinkportal.org/handle/123456789/14801
dc.description Large-scale assessments of student achievement in physics are often approached with an intention to discriminate students based on the attained level of their physics competencies. Therefore, for purposes of test design, it is important that items display an acceptable discriminatory behavior. To that end, it is recommended to avoid extraordinary difficult and very easy items. Knowing the factors that influence physics item difficulty makes it possible to model the item difficulty even before the first pilot study is conducted. Thus, by identifying predictors of physics item difficulty, we can improve the test-design process. Furthermore, we get additional qualitative feedback regarding the basic aspects of student cognitive achievement in physics that are directly responsible for the obtained, quantitative test results. In this study, we conducted a secondary analysis of data that came from two large-scale assessments of student physics achievement at the end of compulsory education in Bosnia and Herzegovina. Foremost, we explored the concept of “physics competence” and performed a content analysis of 123 physics items that were included within the above-mentioned assessments. Thereafter, an item database was created. Items were described by variables which reflect some basic cognitive aspects of physics competence. For each of the assessments, Rasch item difficulties were calculated in separate analyses. In order to make the item difficulties from different assessments comparable, a virtual test equating procedure had to be implemented. Finally, a regression model of physics item difficulty was created. It has been shown that 61.2% of item difficulty variance can be explained by factors which reflect the automaticity, complexity, and modality of the knowledge structure that is relevant for generating the most probable correct solution, as well as by the divergence of required thinking and interference effects between intuitive and formal physics knowledge structures. Identified predictors point out the fundamental cognitive dimensions of student physics achievement at the end of compulsory education in Bosnia and Herzegovina, whose level of development influenced the test results within the conducted assessments.
dc.language English
dc.publisher American Physical Society, APS
dc.relation http://link.aps.org/doi/10.1103/PhysRevSTPER.7.010110
dc.relation https://doaj.org/toc/1554-9178
dc.source Physical Review Special Topics. Physics Education Research, Vol 7, Iss 1 (2011)
dc.subject Education (General)
dc.subject L7-991
dc.subject Education
dc.subject L
dc.subject DOAJ:Education
dc.subject DOAJ:Social Sciences
dc.subject Education (General)
dc.subject L7-991
dc.subject Education
dc.subject L
dc.subject DOAJ:Education
dc.subject DOAJ:Social Sciences
dc.subject Education (General)
dc.subject L7-991
dc.subject Education
dc.subject L
dc.subject Education (General)
dc.subject L7-991
dc.subject Education
dc.subject L
dc.subject Education (General)
dc.subject L7-991
dc.subject Education
dc.subject L
dc.title Identifying predictors of physics item difficulty: A linear regression approach
dc.type article


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