Hierarchical Characteristics and Cultivation Mechanism of Teachers’ Data Literacy in the Context of Digital Transformation
Main Article Content
Abstract
Efficient data acquisition, intelligent analysis, and adaptive decision-making are fundamental capabilities for modern digital engineering systems. To address the limitations of homogeneous teacher training under digital transformation, this study proposes a hierarchical diagnosis and progressive cultivation framework for teacher data literacy based on multimodal assessment and latent profile analysis. A context-embedded evaluation tool integrating situational judgment, behavioral retrospection, and ethical dilemma tasks was developed and applied to 1,263 teachers to identify four distinct competency levels. Based on the diagnosed developmental characteristics, a four-stage cultivation mechanism incorporating intelligent recommendation, competency dashboards, and micro-certification was established and iteratively optimized through design-based research. Experimental results demonstrate that the proposed framework achieves an upward competency migration rate of 60.7%, significantly outperforming conventional training approaches, while the competency dashboard provides the greatest independent contribution to performance improvement. The hierarchical and evidence-driven strategy effectively enhances personalized learning trajectories and supports scalable digital capability development. Beyond educational applications, the proposed multimodal profiling and adaptive optimization framework provides a practical reference for intelligent information processing, data fusion, human-centered decision support, and adaptive sensing architectures, offering potential guidance for data-driven optimization and intelligent management in electromagnetic wave analysis, antenna-enabled sensing systems, and propagation-oriented engineering applications.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
R. Lin, J. Yang, F. Jiang, and J. Li, “Does teacher’s data literacy and digital teaching competence influence empowering students in the classroom? Evidence from China,” Education and Information Technologies, vol. 28, no. 3, pp. 2845-2867, 2023, doi: 10.1007/s10639-022-11274-3.
K. Michos, M. L. Schmitz, and D. Petko, “Teachers’ data literacy for learning analytics: a central predictor for digital data use in upper secondary schools,” Education and Information Technologies, vol. 28, no. 11, pp. 14453-14471, 2023, doi: 10.1007/s10639-023-11772-y.
M. Gümü¸s M and V. Kukul, “Developing a digital competence scale for teachers: validity and reliability study,” Education and Information Technologies, vol. 28, no. 3, pp. 2747-2765, 2023, doi: 10.1007/s10639-022-11213-2.
J. Cabero-Almenara, D. Guillén-Gámez F, J. Ruiz-Palmero, and A. Palacios-Rodríguez, “Digital competence of higher education professor according to DigCompEdu,” Statistical research methods with ANOVA between fields of knowledge in different age ranges. Education and Information Technologies, vol. 26, no. 4, pp. 4691-4708, 2021, doi: 10.1007/s10639-021-10476-5.
J. Lee, D. Alonzo, K. Beswick, V. Abril J M, W. Chew A, and Z. Oo C, “Dimensions of teachers’ data literacy: A systematic review of literature from 1990 to 2021,” Educational Assessment, Evaluation and Accountability, vol. 36, no. 2, pp. 145-200, 2024, doi: 10.1007/s11092-024-09435-8.
B. Boesdorfer S, I. Del Carlo D, and J. Wayson, “Secondary science teachers’ definition and use of data in their teaching practice,” Research in Science Education, vol. 52, no. 1, pp. 159-171, 2022, doi: 10.1007/s11165-020-09936-8.
J. Filderman M, R. Toste J, L. Didion, and P. Peng, “Data literacy training for K-12 teachers: A meta-analysis of the effects on teacher outcomes,” Remedial and Special Education, vol. 43, no. 5, pp. 328-343, 2022, doi: 10.1177/07419325211054208.
J. Puccioni and S. Desir, “Using digital tools to engage in collaborative data-based decision-making,” The Reading Teacher, vol. 75, no. 2, pp. 241-247, 2021, doi: 10.1002/trtr.2042.
L. Ruhter and M. Karvonen, “The impact of professional development on data-based decision-making for students with extensive support needs,” Remedial and Special Education, vol. 45, no. 1, pp. 44-57, 2024, doi: 10.1177/07419325231164636.
S. Fuchs L, D. Fuchs, L. Hamlett C, and M. Stecker P, “Bringing data-based individualization to scale: A call for the next-generation technology of teacher supports,” Journal of Learning Disabilities, vol. 54, no. 5, pp. 319-333, 2021, doi: 10.1177/0022219420950654.
N. McCammon M, K. Wolfe, R. Gao, and A. Starrett, “Training preservice teachers to make data-based decisions: A comparison of two interventions,” Remedial and Special Education, vol. 46, no. 1, pp. 18-30, 2025, doi: 10.1177/07419325231222482.
M. Dülger, A. van Leeuwen, J. Janssen, N. van den Boom-Muilenburg S, N. Wijns, and J. van Driel, “Designing a classroom-level teacher dashboard to foster primary school teachers’ direct instruction of self-regulated learning strategies,” Education and Information Technologies, vol. 30, no. 7, pp. 14785-14819, 2025, doi: 10.1007/s10639-025-13389-9.
A. Agathangelou S, C. Hill H, and Y. Charalambous C, “Customizing professional development opportunities to teachers’ needs: Results from a latent profile analysis,” The Elementary School Journal, vol. 124, no. 3, pp. 386-412, 2024, doi: 10.1086/728590.
M. Torsney B, J. Patterson T, and I. Eisman J, “Teacher motivation nuances: A latent profile analysis of personal utility value, civic mindedness, and future professional development,” The Teacher Educator, vol. 59, no. 1, pp. 66-83, 2024, doi: 10.1080/08878730.2023.2263854.
V. Lim F and H. Nguyen T T, “Design-based research approach for teacher learning: A case study from Singapore,” ELT Journal, vol. 76, no. 4, pp. 452-464, 2022, doi: 10.1093/elt/ccab035.
E. Peters-Burton E, H. Tran H, and B. Miller, “Design-based research as professional development: Outcomes of teacher participation in the development of the Science Practices Innovation Notebook (SPIN),” Journal of Science Teacher Education, vol. 35, no. 3, pp. 221-242, 2024, doi: 10.1080/1046560X.2023.2242665.
S. Öz and A. Özdemir, “Validity and reliability study on the development of data literacy scale for educators,” International Journal of Contemporary Educational Research, vol. 9, no. 3, pp. 649-661, 2022, doi: 10.33200/ijcer.1079774.
K. Tzafilkou, M. Perifanou, and A. Economides A, “Assessing teachers’ digital competence in primary and secondary education: Applying a new instrument to integrate pedagogical and professional elements for digital education,” Education and Information Technologies, vol. 28, no. 3, pp. 16017-16040, 2023, doi: 10.1007/s10639-023-11848-9.
B. Mandinach E and B. Jimerson J, “Data ethics in education: A theoretical, practical, and policy issue,” Studia Paedagogica, vol. 26, no. 4, pp. 9-26, 2021, doi: 10.5817/SP2021-4-1.
C. Pedersen, T. Aagaard, S. Daus, I. Nagel, H. Amdam S, S. Vika K, M. Røkenes F, and K. Andreasen J, “Profiling teacher educators’ strategies for professional digital competence development,” Teachers and Teaching, vol. 30, no. 4, pp. 417-436, 2024, doi: 10.1080/13540602.2024.2336612.
S. Puntambekar, “Distributed scaffolding: Scaffolding students in classroom environments,” Educational Psychology Review, vol. 34, no. 1, pp. 451-472, 2022, doi: 10.1007/s10648-021-09636-3.
R. Kaliisa and C. Dolonen J A, “CADA: A teacher-facing learning analytics dashboard to foster teachers’ awareness of students’ participation and discourse patterns in online discussions,” Technology, Knowledge and Learning, vol. 28, pp. 937-958, 2023, doi: 10.1007/s10758-022-09598-7.
A. van Leeuwen, N. Knoop-van Campen C A, I. Molenaar, and N. Rummel, “How teacher characteristics relate to how teachers use dashboards: Results from two case studies in K-12,” Journal of Learning Analytics, vol. 8, no. 2, pp. 6-21, 2021, doi: 10.18608/jla.2021.7325.
S. White, “Developing credit based micro-credentials for the teaching profession: An Australian descriptive case study,” Teachers and Teaching, vol. 27, no. 7, pp. 696-711, 2021, doi: 10.1080/13540602.2021.2003324.