Cognitive Diagnostic Assessment in Educational Testing: A Score Strategy-Based Evaluation

Muhamad Firdaus Mohd Noh, Mohd Effendi @ Ewan Mohd Matore, Nur Ainil Sulaiman, Mohd Tarmizi Azeman, Hamzah Ishak, Nursohana Othman, Nurbaya Mohd Rosli, Siti Hannah Sabtu

Abstract


Cognitive diagnostic assessment (CDA) represents an innovative approach in educational testing, utilizing cognitive diagnosis models (CDMs) to uncover specific cognitive attributes that influence students' responses to assessment items. Unlike traditional assessments, CDA offers detailed, actionable insights into individual learning processes, enabling more targeted and personalized educational interventions. However, the implementation of CDA is still underexplored compared to other psychometric theories, especially in Malaysia. Therefore, this study seeks to describe the application of CDA in Malaysia through the SCORE analysis model method. The SCORE model of CDA is evaluated across five key elements: Strengths (S), Challenges (C), Options (O), Responses (R), and Effectiveness (E) by analyzing past literature sourced from reputable databases such as Google Scholar, SCOPUS, and Web of Science. The findings indicate that CDA offers strengths like granular feedback, enhanced validity, and data-driven decisions, but faces challenges in complex implementation and resource demands. Its effectiveness hinges on efficient implementation and appropriacy, while exploring opportunities, managing risks, and considering stakeholders' responses help optimize its transformational and commercial value. The findings provide educators with a framework for utilizing CDA to enhance instructional strategies and tailor interventions to meet individual student needs. For policymakers, the implications of the strengths, challenges, and opportunities of CDA emphasize the importance of supporting its implementation through professional development, infrastructure investment, and policy alignment. Future studies are recommended to explore the long-term impacts of CDA on student achievement and educational equity, scalability across educational levels, and development of user-friendly tools to enhance accessibility for educators.

Keywords: Cognitive diagnostic model; SCORE model; strategy-based evaluation; educational assessment; psychometrics

References

Agnew, J. (2021). Internal and external stakeholder impact on curriculum design. Academia Letters. https://doi.org/10.20935/al2028

Akbay, L. (2021). Impact of retrofitting and item ordering on DIF. Journal of Measurement and Evaluation in Education and Psychology, 12(2), 212–225. https://doi.org/10.21031/epod.886920

Awang Hidup, D. S., & Mohd Matore, M. E. @ E. (2024). Please don’t get tired of interim assessment practice in Malaysia! International Journal of Academic Research in Progressive Education and Development, 13(1). https://doi.org/10.6007/ijarped/v13-i1/20888

Azeman, M. T., Mohd Matore, M. E., Ishak, H., Othman, N., Rosli, N. M., Sabtu, S. H., & Mohd Noh, M. F. (2024). Application of the SCORE Model in enhancing competency of agile coaches in sports coaching. International Journal of Academic Research in Progressive Education and Development, 13(3). https://doi.org/10.6007/IJARPED/v13-i3/22743

Chan, S. J., & Chou, C. (2020). Who influences higher education decision-making in Taiwan? An analysis of internal stakeholders. Studies in Higher Education, 45(10), 2101–2109. https://doi.org/10.1080/03075079.2020.1823646

Chew, C. M., & Chin, H. (2024). Online Cognitive Diagnostic Assessment with Ordered Multiple-Choice Items for Grade Four Topic of Time. 103–117. https://doi.org/10.1142/9789811287183_0008

Fan, T., Song, J., & Guan, Z. (2021). Integrating diagnostic assessment into curriculum: a theoretical framework and teaching practices. Language Testing in Asia, 11(2), 1–23. https://doi.org/10.1186/s40468-020-00117-y

Ghiasian, S. A., Hemmati, F., Alavi, S. M., & Rouhi, A. (2024). Constructing and validating a Q-matrix for cognitive diagnostic analysis of the listening comprehension section of the IELTS. International Journal of Language Testing. https://www.ijlt.ir

Gierl, M. J., & Cui, Y. (2008). Defining characteristics of Diagnostic Classification Models and the problem of retrofitting in cognitive diagnostic assessment. Measurement: Interdisciplinary Research & Perspective, 6(4), 263–268. https://doi.org/10.1080/15366360802497762

Guo, L., Zhou, W., & Li, X. (2024). Cognitive Diagnosis Testlet Model for Multiple-Choice Items. Journal of Educational and Behavioral Statistics, 49(1), 32–60. https://doi.org/10.3102/10769986231165622

Ishak, H., Mohd Matore, M. E. @ E., Othman, N., Mohd Rosli, N., Sabtu, S. H., Mohd Nor, M. F., & Azeman, M. T. (2024). Strategy-based assessment using SCORE Model in adaptive behavior evaluation. International Journal of Academic Research in Business and Social Sciences, 14(9). https://doi.org/10.6007/IJARBSS/v14-i9/22745

Javidanmehr, Z., & Sarab, M. R. A. (2017). Cognitive diagnostic assessment: Issues and considerations. International Journal of Language Testing, 7(2), 73–98.

Jiang, M., Ahmad, A. L., & Aziz, J. (2024). New media and cross-cultural adaptation: A bibliometric analysis using VOSviewer. e-Bangi Journal of Social Sciences and Humanities, 21(1), 273–285. https://doi.org/10.17576/ebangi.2024.2101.24

Li, C. (2022). Development and application of assessment tools based on cognitive diagnosis. Proceedings - 2022 3rd International Conference on Information Science and Education, ICISE-IE 2022, 98–102. https://doi.org/10.1109/ICISE-IE58127.2022.00028

Li, Y., Zhen, M., & Liu, J. (2021). Validating a reading assessment within the cognitive diagnostic assessment framework: Q-matrix construction and model comparisons for different primary grades. Frontiers in Psychology, 12(December), 1–13. https://doi.org/10.3389/fpsyg.2021.786612

Ma, W., Minchen, N., & de la Torre, J. (2020). Choosing between CDM and Unidimensional IRT: The proportional reasoning test case. Measurement, 18(2), 87–96. https://doi.org/10.1080/15366367.2019.1697122

Maas, L., Madison, M. J., & Brinkhuis, M. J. S. (2024). Properties and performance of the one-parameter log-linear cognitive diagnosis model. Frontiers in Education, 9, 1–12. https://doi.org/10.3389/feduc.2024.1287279

Mei, H., & Chen, H. (2022). Cognitive diagnosis in language assessment: A thematic review. RELC Journal, 1–9. https://doi.org/10.1177/00336882221122357

Mohd Noh, M. F., & Mohd Matore, M. E. E. (2024). Conundrum and considerations in Cognitive Diagnostic Assessment for language proficiencyevaluation. Information Management and Business Review, 16(2), 63–72.

Mohd Noor, M. A., & Lian, L. H. (2022). Consistent or inconsistent? Expert-based cognitive model vs student-based response cognitive model of cognitive diagnostic assessment in factorisation of algebraic fractions. International Journal of Academic Research in Progressive Education and Development, 11(3), 220–231. https://doi.org/10.6007/ijarped/v11-i3/14696

Nájera, P., Abad, F. J., Chiu, C. Y., & Sorrel, M. A. (2023). The Restricted DINA Model: A comprehensive cognitive diagnostic model for classroom-level assessments. Journal of Educational and Behavioral Statistics, 48(6), 719–749. https://doi.org/10.3102/10769986231158829

Nájera, P., Sorrel, M. A., de la Torre, J., & Abad, F. J. (2020). Improving robustness in Q-matrix validation using an iterative and dynamic procedure. Applied Psychological Measurement, 44(6), 431–446. https://doi.org/10.1177/0146621620909904

Nallasamy, R., & Khairani, A. Z. (2022a). Validating reading comprehension assessment under the GDINA Model. Malaysian Journal of Social Sciences and Humanities (MJSSH), 7(11), 1–12. https://doi.org/10.47405/mjssh.v7i11.1877

Nallasamy, R., & Khairani, A. Z. Bin. (2022b). Development and validation of reading comprehension assessments by using GDINA Model. Malaysian Journal of Social Sciences and Humanities (MJSSH), 7(2), 1–13. https://doi.org/10.47405/mjssh.v7i2.1278

Palazzo, M., & Micozzi, A. (2024). The SWOT analysis: An evolving decision-making model. In Rethinking Decision-Making Strategies and Tools: Emerging Research and Opportunities (pp. 53–70). Emerald Publishing Limited. https://doi.org/10.1108/978-1-83797-204-320241004

Paulsen, J., & Valdivia, D. S. (2021). Examining cognitive diagnostic modeling in classroom assessment conditions. Journal of Experimental Education, 90(4), 916–933. https://doi.org/10.1080/00220973.2021.1891008

Qin, H., & Guo, L. (2024). Using machine learning to improve Q-matrix validation. Behavior Research Methods, 56(3), 1916–1935. https://doi.org/10.3758/s13428-023-02126-0

Ravand, H., & Baghaei, P. (2019). Diagnostic Classification Models: Recent developments, practical issues, and prospects. International Journal of Testing, 20(1), 24–56. https://doi.org/10.1080/15305058.2019.1588278

Rijeng, I. S., Alavi, K., Aziz, S. F. A., & Manap, J. (2024). Active ageing and older adults’ volunteerism in Asia: A systematic review. e-Bangi Journal of Social Sciences and Humanities, 21(3), 416–434. https://doi.org/10.17576/ebangi.2024.2103.32

Sessoms, J., & Henson, R. A. (2018). Applications of Diagnostic Classification Models: A literature review and critical commentary. Measurement: Interdisciplinary Research and Perspectives, 16(1), 1–17. https://doi.org/10.1080/15366367.2018.1435104

Shi, X., Ma, X., Du, W., & Gao, X. (2024). Diagnosing Chinese EFL learners’ writing ability using polytomous cognitive diagnostic models. Language Testing, 41(1), 109–134. https://doi.org/10.1177/02655322231162840

Toprak, T. E., & Cakir, A. (2021). Examining the L2 reading comprehension ability of adult ELLs: Developing a diagnostic test within the cognitive diagnostic assessment framework. Language Testing, 38(1), 106–131. https://doi.org/10.1177/0265532220941470

Xin, T., Wang, C., Chen, P., & Liu, Y. (2022). Editorial: Cognitive Diagnostic Models: Methods for practical applications. Frontiers in Psychology 13, 1-3 https://doi.org/10.3389/fpsyg.2022.895399

Yamaguchi, K., Liu, Y., & Xu, G. (2024). Generalized Bayesian Method for diagnostic classification model. Journal of Educational and Behavioral Statistics, 48(6), 1–6.

Yoon, J. Y., Gweon, G., & Yoo, Y. J. (2024). Supervised diagnostic classification of cognitive attributes using data augmentation. PLoS ONE, 19(1), 1–20. https://doi.org/10.1371/journal.pone.0296464

Zhang, H., Wu, X., & Ju, M. (2024). Developing a cognitive model of solid geometry based on Interpretive Structural Modeling method. Heliyon, 10(5), 1–12. https://doi.org/10.1016/j.heliyon.2024.e27063

Zhu, Z. (2023). International comparative study of learning trajectories based on TIMSS 2019 G4 data on cognitive diagnostic models. Frontiers in Psychology, 14, 1-11. https://doi.org/10.3389/fpsyg.2023.1241656


Full Text:

PDF


DOI: http://dx.doi.org/10.17576/ebangi.2024.2104.21

Refbacks

  • There are currently no refbacks.


-


 

_________________________________________________

eISSN 1823-884x

Faculty of Social Sciences & Humanities
Universiti Kebangsaan Malaysia
43600 UKM Bangi, Selangor Darul Ehsan
MALAYSIA

© Copyright UKM Press, Universiti Kebangsaan Malaysia