Adaptive AHP-Entropy Combined Weighting Model for Teachers’ Digital Literacy Assessment
DOI:
https://doi.org/10.67541/jdf2611Keywords:
Teachers’ digital literacy; Combined weighting; Conflict diagnosis; Adaptive reconciliation; Ranking stabilityAbstract
To address the widespread reliance on equal-weight assumptions in the assessment of teachers’ digital literacy and the lack of a mechanism for diagnosing disagreements between subjective and objective weighting, this study proposes a conflict-aware and adaptively balanced combined weighting model (CAW-AE). The model jointly measures structural conflicts between subjective and objective weights using a geometrically weighted combination of cosine similarity and rank displacement. Weighting states are classified into low-, medium-, and high-conflict intervals according to the degree of conflict. Differentiated indicator-level reconciliation coefficients incorporating a conflict penalty are then constructed. Their reconciliation intensity is determined by an analytical solution that minimizes overall deviation and is subject to truncation constraints ensuring a convex combination. Empirical results based on assessment data from 347 teachers and 20 indicators show a global conflict degree of 0.271, with conflict concentrated in digital social responsibility. CAW-AE has a Herfindahl–Hirschman index of 0.066 and a coefficient of variation of 0.334, occupying a justified position between the individual methods and a fixed-coefficient compromise. Tests show a rank reversal rate of 8.2% under ±20% perturbations of expert judgments, lower than the 14.8% of AHP alone and the 9.7% of game-theoretic combination. Its group discrimination (Kruskal-Wallis H = 33.76) is the highest among the six schemes. Its rank correlation with the external criterion is 0.428, significantly higher than those of equal weighting and entropy weighting alone. These results indicate that CAW-AE improves ranking stability and differentiation between groups without sacrificing the reasonable contribution of domain knowledge.
References
[1] Bachtiar, B. (2025). Preparing citizens for the future of digital literacy and AI: With a focus on Indonesian EFL teachers. In Digital citizenship and the future of AI engagement, ethics, and privacy (pp. 405-440). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-9015-3.ch015
[2] Liu, C., Gao, Z., Tu, Y., Zeng, S., Li, S., Chen, H., & Chiou, W. K. (2026, July). Research on Potential Risks and Governance Mechanisms of Journalism and Communication Education Enabled by Generative Artificial Intelligence. In International Conference on Human-Computer Interaction (pp. 230-245). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-032-29903-1_14
[3] Heinsen, R. S. (2024). Gaze behavior reveals automaticity and attention allocation during music teaching vs. observing. Journal of Eye Movement Research, 17(2), 9. https://doi.org/10.16910/jemr.17.2.3
[4] Angus, D., Hayden, L., Obeid, A. K., Tan, X. Y., Carah, N., Burgess, J., ... & Bagnara, J. (2024). Computational methods for improving the observability of platform-based advertising. Journal of advertising, 53(5), 661-680. https://doi.org/10.1080/00913367.2024.2394156
[5] Pham Thi, T. D., Pham, V. K., & Duong, N. T. (2025). Understanding M-learning app adoption: an integrated model for college students in Vietnam. Interactive Learning Environments, 33(10), 5847-5869. https://doi.org/10.1080/10494820.2025.2488144
[6] Kim, T., & Lee, J. (2020). Measuring teacher effectiveness for equity: value-added model of teachers’ distributive effects on classroom achievement gaps. Educational Research and Evaluation, 26(1-2), 30-52. https://doi.org/10.1080/13803611.2021.1906708
[7] Triventi, M. (2020). Are children of immigrants graded less generously by their teachers than natives, and why? Evidence from student population data in Italy. International Migration Review, 54(3), 765-795. https://doi.org/10.1177/0197918319878104
[8] Charalambous, C. Y., Hill, H. C., Chin, M. J., & McGinn, D. (2020). Mathematical content knowledge and knowledge for teaching: exploring their distinguishability and contribution to student learning: CY Charalambous et al. Journal of Mathematics Teacher Education, 23(6), 579-613. https://doi.org/10.1007/s10857-019-09443-2
[9] Bohra, S. S., & Anvari‐Moghaddam, A. (2022). A comprehensive review on applications of multicriteria decision‐making methods in power and energy systems. International Journal of Energy Research, 46(4), 4088-4118. https://doi.org/10.1002/er.7517
[10] Sarkar, A., & Goswami, S. S. (2026). A comprehensive review: The novel weighting methods for multi-criteria decision-making (MCDM). Spectrum of Operational Research, 1-26. https://doi.org/10.31181/sor202781
[11] Zhao, H., Song, S., Lv, X., & Bao, Y. (2025). A multi-dimensional evaluation model for power enterprise procurement performance based on fuzzy analytic hierarchy process and TOPSIS integration. Scientific Reports, 15(1), 41290. https://doi.org/10.1038/s41598-025-25042-z
[12] Gallego-Arrufat, M. J., García-Martínez, I., Romero-López, M. A., & Torres-Hernández, N. (2024). Digital rights and responsibility in education: A scoping review. Education Policy Analysis Archives, 32. https://doi.org/10.14507/epaa.32.7899
[13] Lan, H. (2025). Sustainable development of social responsibility in universities in the digital era: Perceptions and advances. Information Development, 02666669241303124. https://doi.org/10.1177/02666669241303124
[14] Fan, J., Wang, H., & Gu, X. (2025). Development and validation of a framework and scale for primary and secondary school teachers’ data-artificial intelligent competence. Frontiers in Psychology, 16, 1756893. https://doi.org/10.3389/fpsyg.2025.1756893
[15] Yadav, S. (2026). Addressing Sustainable Development Goals Through Competency-Based Education: Artificial Intelligence Innovation and Teachers' Competency Development. In Addressing Sustainable Development Goals Through Competency-Based Education (pp. 1-26). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-3720-3.ch001
[16] Panagiotou, N. (2025). AI literacy and the future of education: A framework for ethical and inclusive learning models. Journal of Interdisciplinary Knowledge, 8, e01635-e01635. https://doi.org/10.37497/jik.v8iknowledge.1635
[17] Ochieng, P. A. (2027). Artificial Intelligence in Teacher Professional Development for Instructional Leadership Capacity. In Harnessing AI for Education Reforms: Frameworks, Best Practices and Considerations for Quality Education (pp. 75-96). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-8740-6.ch004
[18] Liu, Z., & Yang, Y. (2025). Fundamental usability evaluation in visualization: Integrating Nielsen’s principles and the analytic hierarchy process. International Journal of Human–Computer Interaction, 41(20), 13012-13040. https://doi.org/10.1080/10447318.2025.2467455
[19] Kovari, A. (2026). Generative AI, performance, and learning: A framework for comparing interventions across assessment regimes. Computers, 15(9), 633. https://doi.org/10.3390/computers15090633
[20] Yu, Z., Sun, G., Lin, S., Hu, H., & Xu, J. (2025). Operationalizing game-theoretic weighting in public hospital cost control: an implementation framework from Chinese tertiary hospitals. Cost Effectiveness and Resource Allocation, 23(1), 11. https://doi.org/10.1186/s12962-025-00616-w
[21] Zhang, C., Wang, Z., Luo, L., & Zhan, W. (2026). Sustainable Flood Emergency Material Demand Grading and Routing Re-Optimization: A Game-Theoretic Combined Weighting and Grey-TOPSIS Approach in Jiaozuo, China. Sustainability. https://doi.org/10.3390/su182010617
[22] Jiang, F., Zhu, Z., Li, S., & Zhang, J. (2026). Navigating Sustainable Energy Transitions and Environmental Constraints in PV Siting. Sustainability, 18(8), 3915. https://doi.org/10.3390/su18083915
[23] Kozáková, M., & Kmety Barteková, M. (2020). Analysis of market concentration in creative industry. In SHS Web of Conferences (Vol. 83, p. 01035). EDP Sciences. https://doi.org/10.1051/shsconf/20208301035
[24] Evren, A., Tuna, E., Ustaoglu, E., & Sahin, B. (2021). Some dominance indices to determine market concentration. Journal of Applied Statistics, 48(13-15), 2755-2775. https://doi.org/10.1080/02664763.2021.1963421
[25] Bastianin, A. (2020). Robust measures of skewness and kurtosis for macroeconomic and financial time series. Applied Economics, 52(7), 637-670. https://doi.org/10.1080/00036846.2019.1640862
[26] Chen, Q., & Xiao, Z. (2025). Spread regression, skewness regression, and kurtosis regression with an application to the US wage structure. Journal of Applied Econometrics, 40(3), 325-340. https://doi.org/10.1002/jae.3105
[27] Jin, L., Dong, Y. Y., Xu, J. P., Chen, M. S., Zeng, R. X., & Guo, L. H. (2025). Relationship between the laboratory test-based frailty index and overall mortality in critically ill patients with acute pancreatitis: a retrospective study based on the MIMIC-IV database. Frontiers in Medicine, 12, 1524358. https://doi.org/10.3389/fmed.2025.1524358
Published
Data Availability Statement
The data that support the findings of this study are available upon request from the corresponding authors, J.Z.
Issue
Section
License
Copyright (c) 2026 Jie Zhang (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
