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Identification, estimation, and Q-matrix validation of hierarchically structured attributes in cognitive diagnosis

Başlık çevirisi mevcut değil.

  1. Tez No: 403038
  2. Yazar: LOKMAN AKBAY
  3. Danışmanlar: DR. JIMMY DE LA TORRE
  4. Tez Türü: Doktora
  5. Konular: Eğitim ve Öğretim, Education and Training
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2016
  8. Dil: İngilizce
  9. Üniversite: Rutgers, The State University of New Jersey-New Brunswick Campus
  10. Enstitü: Yurtdışı Enstitü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: 126

Özet

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Özet (Çeviri)

Many cognitive diagnosis model (CDM) examples assume independent cogni- tive skills; however, cognitive skills need not be investigated in isolation (Kuhn, 2011; Tatsuoka, 1995). Kuhn (2001) argues that some preliminary knowledge can be the foundation for more sophisticated knowledge or skills. When this type of hierarchical relationships among the attributes are not taken into account, estimation results of the conventional CDMs may be biased or less accurate. Hence, this dissertation in- vestigates the change in the degree of accuracy and precision in the item parameter estimates and correct attribute classi cation rates of di erent estimation approaches based on modi cation of either the Q-matrix or prior distribution. Modi cation of the prior distribution and the Q-matrix depend on the assumed hierarchical structure, as such, identifying the correct hierarchical structure is of the essence. To address the subjectivity in the conventional methods for attribute structure identi cation (i.e., expert opinions via content analysis and verbal data analyses such as interviews and think-aloud protocols), this dissertation proposes a likelihood-ratio test based exhaustive empirical search for identifying hierarchical structures. It further suggests a likelihood-approach for selection of the most accurate hierarchical structure when multiple candidates are present. Furthermore, implementation of the CDMs requires construction of a Q-matrix to indicate the associations between test items and attributes required for successful completion of the items (de la Torre, 2008; Chiu, 2013). Q-matrix construction heavily depends on content expert opinions, as such this subjective process may result in misspeci cations in the Q-matrix. Up to date, several parametric and nonparametric Q-matrix validation methods have been proposed to address the misspeci cations that may emerge due to fallible judgments of experts in Q-matrix construction (Chiu, 2013). Yet, although they have been examined under various conditions, none of these methods was tested under hierarchical attribute structures. Therefore, this dissertation further investigates the reciprocal impact of misspeci ed Q-matrix and hierarchical structure on hierarchy identi cation and Q-matrix validation. The results showed that structured prior distribution led to the most accu- rate and precise item parameter estimation, and highest correct examinee classi - cation. When an unstructured prior was employed, impact of structured Q-matrix was di erent for compensatory and noncompensatory CDMs. Furthermore, study results showed that likelihood-based exhaustive search was promising in identi ca- tion/validation of hierarchical attribute structure. Lastly, results indicated that per- formance of Q-matrix validation methods might not be as high when they are used as is under hierarchical attribute structures.

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