Dergiler / Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi / 2021 / Cilt: 12 - Sayı: 3

Monitoring Student Achievement with Cognitive Diagnosis Model

Sayfa
303–320
Yayın Tarihi
2022-10-12
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The high classification accuracy seen as a result of the analysis applied to HSPT, which is not intended for the cognitive diagnosis, shows that the results can be used for monitoring student achievement.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this study, it is aimed to show how student achievement can be monitored by using the cognitive diagnosis models.
📊
Temel Bulgular & Veri:For this purpose, responses of the 6th, 7th, and 8th grade Mathematics subtests of High School Placement Tests (HSPT) in 2009, 2010, and 2011, which provide longitudinal data, were used, respectively.
💡
Sonuç & Katkı:The high classification accuracy seen as a result of the analysis applied to HSPT, which is not intended for the cognitive diagnosis, shows that the results can be used for monitoring student achievement.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2009 Verileri
⚙️Değişkenler:Cognitive diagnosis · student achievement · g-dina

Abstract

In this study, it is aimed to show how student achievement can be monitored by using the cognitive diagnosis models. For this purpose, responses of the 6th, 7th, and 8th grade Mathematics subtests of High School Placement Tests (HSPT) in 2009, 2010, and 2011, which provide longitudinal data, were used, respectively. There were 49933 examiners’ responses in data sets. The attributes examined by these tests were determined by the Mathematics experts, and the Q matrix consisting of five attributes was developed. As a result of the analysis, it was seen that the largest latent class for all three years consisted of those non-master for any attribute. It was observed that the probability of attribute mastery increased in the 7th grade and decreased in the 8th grade. The high classification accuracy seen as a result of the analysis applied to HSPT, which is not intended for the cognitive diagnosis, shows that the results can be used for monitoring student achievement.

🔖

Bu Makaleye Atıf Yap

DOST, Ş., DOĞAN, N., YÜKSEL, N. S., YAKAR, L. (2022). Monitoring Student Achievement with Cognitive Diagnosis Model. Eğitimde ve Psikolojide Ölçme ve Değerlendirme Dergisi, Cilt: 12 - Sayı: 3, 303–320. https://ajindex.com/articles/497161

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (5)

  1. Akaike, H. (1974). A new look at the statistical identification model. IEEE Transactions on Automated Control, 19,716–723. doi:10.1109/TAC.1974.1100705 Akbay, L., Terzi, R., Kaplan, M., &Karaaslan, K. G. (2017). Expert-based attribute identification and validation on fraction subtraction: A cognitively diagnostic assessment application. Journal on Mathematics Education, 9(1), 103-120.
  2. Chen, H., ve Chen, J., (2016). Retrofitting Non-cognitive-diagnostic Reading Assessment Under the Generalized DINA Model Framework, Language Assessment Quarterly,13(3), 218-230, DOI: 10.1080/15434303.2016.1210610 Cheng, Y. (2010). Improving cognitive diagnostic computerized adaptive testing by balancing attribute coverage: the modified maximum global discrimination index method. Educational and Psychological Measurement, 70 (6), 902-913.
  3. Cui, Y., Gierl, M. J., & Chang, H. H. (2012). Estimating classification consistency and accuracy for cognitive diagnostic assessment. Journal of Educational Measurement, 49(1), 19-38. de la Torre, J. (2009). A cognitive diagnosis model for cognitively-based multiple-choice options. Applied Psychological Measurement, 33(3), 163–183. de la Torre, J. (2011). The generalized DINA model framework. Psychometrika, 76(2), 179-199. de la Torre, J., Hong, Y., & Deng, W. (2010). Factors affecting the item parameter estimation and classification accuracy of the DINA model. Journal of Educational Measurement, 47(2), 227-249. de la Torre, J., &Minchen N. (2014). Cognitively diagnostic assessments and the cognitive diagnosis model framework. PsicologíaEducativa, 20(2), 89-97. de la Torre, J., van der Ark, L. A., & Rossi, G. (2017). Analysis of clinical data from a cognitive diagnosis modeling framework. Measurement and Evaluation in Counseling and Development, 1-16.
  4. DiBello, L. V., Stout, W. F., & Roussos, L. A. (2007). Cognitive diagnosis Part I. In C. R. Rao & S. Sinharay (Eds.), Handbook of statistics (Vol. 26): Psychometrics (pp. 979-1029). Amsterdam: Elsevier Embretson, S. E. (1998). A cognitive design system approach to generating valid tests: Application to abstract reasoning. Psychological Methods, 3, 380-396
  5. Hartz, S. M. (2002). A bayesianframework for the unified model for assessing cognitive abilities: Blending theory with practicality, Unpublished PhD dissertation, University of Illinois at Urbana-Champaign, ABD. Huang, H. (2017), Multilevel cognitive diagnosis models for assessing changes in latent attributes. Journal of Educational Measurement