An Exploration of Junior High School Biology Curriculum Evaluation Based on Q-Matrix Cognitive Diagnostic Theory

Li Zhu

doi:10.6918/IJOSSER.202512_8(12).0041

Authors

Li Zhu

DOI:

https://doi.org/10.6918/IJOSSER.202512_8(12).0041

Keywords:

Biology curriculum, Cognitive diagnostic, Q-Matrix, Academic evaluation

Abstract

This study introduces and applies a Q-Matrix cognitive diagnostic theory-based framework for the comprehensive evaluation of competency development within the junior high school biology curriculum. Departing from traditional summative assessment, the model establishes a systematic mapping between nine defined attributes—spanning life concepts, scientific thinking, inquiry, and socio-ethical responsibility—and specific test items via a structured Q-matrix. By transforming student response data into a binary score matrix (R) and employing a sequence of matrix operations, the method calculates individualized probabilities of mastery for each attribute. A case study involving two students with nearly identical total scores (80/100 vs. 79/100) demonstrates the model's diagnostic power: it reveals starkly contrasting competency profiles, with Student A excelling in critical thinking and model construction but weaker in evolutionary understanding, while Student B shows superior performance in evolution-adaptation and social responsibility but lags in practical application skills. These nuanced insights, unobtainable from aggregate scores, validate the framework's capacity for precise, personalized diagnosis. The results underscore its utility in identifying instructional strengths, pinpointing areas for pedagogical intervention, and providing actionable feedback for curriculum refinement. This approach offers a robust, theory-driven tool for advancing formative assessment and promoting the integrated development of scientific knowledge and ethical competencies in science education.

Downloads

Download data is not yet available.

References

[1] M. L. Thomas, “The value of item response theory in clinical assessment: A review,” Assessment, vol. 18, no. 3, pp. 291–307, 2011.

[2] J. C. Cappelleri, J. J. Lundy, and R. D. Hays, “Overview of classical test theory and item response theory for the quantitative assessment of items in developing patient-reported outcomes measures,”Clin. Therapeutics, vol. 36, no. 5, pp. 648–662, 2014.

[3] A. Anderson, D. Huttenlocher, J. Kleinberg, and J. Leskovec, “Engaging with massive online courses,” in Proc. 23rd Int. Conf. World Wide Web, 2014, pp. 687–698.

[4] H. Burns, C. A. Luckhardt, J. W. Parlett, and C. L. Redfield, Intelligent Tutoring Systems: Evolutions in Design, Hove, U.K.: Psychology Press, 2014.

[5] J. De La Torre, “DINA model and parameter estimation: A didactic,” J. Educ. Behav. Statist., vol. 34, no. 1, pp. 115–130, 2009.

[6] S. E. Embretson and S. P. Reise, Item Response Theory, Hove, U.K.: Psychology Press, 2013.

[7] M.D. Reckase, “Multidimensional item response theorymodels,” in Multidimensional Item Response Theory, Berlin, Germany: Springer, 2009, pp. 79–112.

[8] Y. Koren, R. Bell, and C. Volinsky, “Matrix factorization techniques for recommender systems,” Computer, no. 8, pp. 30–37, 2009.

[9] Chiu, C.Y.; Douglas, J. A Nonparametric Approach to Cognitive Diagnosis by Proximity to Ideal Response Patterns. J. Classif. 2013, 30, 225–250.

[10] Chiu, C.Y.; Sun, Y.; Bian, Y. Cognitive Diagnosis for Small Educational Programs: The General Nonparametric Classification Method. Psychometrika 2017, 83, 355–375.

[11] de la Torre, J. The Generalized DINA Model Framework. Psychometrika 2011, 76, 179–199.

[12] Ma,W.; de la Torre, J. GDINA: An R Package for Cognitive Diagnosis Modeling. J. Stat. Softw. 2020, 93, 1–26.

[13] Zhan, P. Deterministic Input, Noisy Mixed Modeling for Identifying Coexisting Condensation Rules in Cognitive Diagnostic Assessments. J. Intell. 2023, 11, 55.

[14] Zhu Jinxin, Zhang Shumei, Xin Tao. A modelof cognitively diagnostic base on q matrix: classifying model of the probability of attributes' mastery [J]. Journal of Beijing Normal University (Natural Science), 2009, 45(2): 117-122.