A *Validity Narrative: Effects of Reading Skills Diagnosis on Teaching and Learning in the Context of NG TOEFL
By E. Jang. 2005
98 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/32760e3bb7b0894505e256d4ba96d146f9660361
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Andre A. Rupp
- Yong-Won Lee
- A. Kim
- Yasuyo Sawaki
- Sarah M. Hartz
- Gongjun Xu
- Jonathan Templin
- L. DeCarlo
Citations in the corpus
- Exploring ways to provide diagnostic feedback with an ESL placement test: Cognitive diagnostic assessment of L2 reading ability (2015, background): “For instance, in a ground-breaking study of diagnosing L2 reading, Jang (2005) first conducted a verbal protocol analysis to identify the L2 reading attributes used by ESL learners in completing the reading section of the Test of English as a Foreign Language (TOEFL).”
- Skills Diagnosis Using IRT-Based Latent Class Models (2007, background): “The development of more extensive score reports by Jang (2005) for a RUM application is another important advancement that can be adapted to the other models in this approach.”
- Unique Characteristics of Diagnostic Classification Models: A Comprehensive Review of the Current State-of-the-Art (2008, background): “An excellent example of a feedback mechanism for such an assessment that is given to learners, their teachers, and their parents is the diagnostic report card presented in Figure 1 that is taken from the dissertation by Eunice Jang (2005).”
- Cognitive Diagnosis Approaches to Language Assessment: An Overview (2009, background): “Cognitive diagnosis approaches (CDAs) are cognitively grounded, diagnostic assessment procedures (Gao & Rodgers, 2007; Gierl, 2007; Jang, 2005; Lohman & Ippel, 1993; Rupp, 2007).”
- The fusion model skills diagnosis system (2007, methodology): “real data analysis of Jang (2005, 2006; Roussos, DiBello, Henson, Jang, & Templin, in press). The misfit at the very lowest and highest parts of the distribution were expected as the mastery/nonmastery examinee model overestimated the scores of the lowest scoring examinees and underestimated the sco”