2012
DOI: 10.1016/j.jsp.2012.01.002
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Effects of linguistic complexity and accommodations on estimates of ability for students with learning disabilities

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Cited by 15 publications
(11 citation statements)
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“…However, these test items can be often fraught with problems that can be distracting or confusing to students (Zorin et al, 2013). Research has shown that construct-irrelevant factors such as language complexity and item format can interfere with student performance on assessments (Haladyna et al 2002;Shaftel et al 2006;Martiniello 2008;Cawthon et al 2012;). These complications can restrict the appropriateness of educational measurement, which can result in inaccurate judgments about student understanding.…”
Section: Introductionmentioning
confidence: 99%
“…However, these test items can be often fraught with problems that can be distracting or confusing to students (Zorin et al, 2013). Research has shown that construct-irrelevant factors such as language complexity and item format can interfere with student performance on assessments (Haladyna et al 2002;Shaftel et al 2006;Martiniello 2008;Cawthon et al 2012;). These complications can restrict the appropriateness of educational measurement, which can result in inaccurate judgments about student understanding.…”
Section: Introductionmentioning
confidence: 99%
“…Including a random effect suggests that not all features that affect item difficulty are included in the model, but their net effect is a normal distribution of item difficulties with some known mean and variance. Random item models have been extended to EIRM in several different contexts including, but not limited to, explaining a construct Janssen, 2010;Janssen, Schepers, & Peres, 2004), understanding the components of item sets created using automatic item generation (Holling, Bertling, & Zeuch, 2009), predicting item difficulty (Hartig, Frey, Nold, & Klieme, 2012), understanding the impact of cognitive supports on alternative assessments (Ferster, 2013), investigating differential facet functioning (Cawthon, Kaye, Lockhart, & Beretvas, 2012), and modeling item position effects (Albano, 2013). Extending the EPCM in Equation 4, which parameterizes the model for the example considering the role of images in item difficulty for primary and secondary English-speaking students on a mathematics test, the cross-classified EPCM can be written as: 4, the only difference in Equation 6is the additional parameter of ϵ , where ϵ~(0, ), representing the random effect for residual item difficulty.…”
Section: Cross-classified Explanatory Partial Credit Modelmentioning
confidence: 99%
“…In addition, this arrangement created scheduling limitations that required students to always be pulled from social studies and a need for grouping ELs according to their linguistic classification instead of their learning needs. Both practices go against research-based recommendations for supporting diverse student learning needs and culturally inclusive learning environments (Abedi & Herman, 2006Cawthon, Kaye, Lockhart, & Beretvas, 2012;Collier & Thomas, 2002;Muller et al, 2010).…”
Section: Implications and Recommendationsmentioning
confidence: 99%