Predicting secondary school mathematics teachers' digital teaching behavior using partial least squares structural equation modeling
Open accessAbstract
Digital technologies play a key role in the digital transformation of education. In order to improve teaching effectiveness and efficiency, teachers should use digital technologies appropriately. However, some secondary school mathematics teachers have little confidence in their digital teaching behavior. This study aimed to explore the predictors of secondary school mathematics teachers' digital teaching behavior. An extended unified theory of acceptance and use of technology (UTAUT) model was adopted to predict secondary school mathematics teachers' digital teaching behavior. A questionnaire survey was conducted with all junior high school mathematics teachers in a state-level new area, which is located in a central province of China. Three hundred and eighty-five valid data were collected. The partial least squares structural equation modeling (PLS-SEM) method was used to analyze the data. It was found that technological pedagogical content knowledge (TPACK) was the biggest predictor of secondary school mathematics teachers' digital teaching behavior. Attitude, behavioral intention, performance expectancy, effort expectancy and social influence can also affect teachers' digital teaching behavior in direct and/or indirect ways. The findings have noteworthy realistic implications for enhancing digital teaching behavior of secondary school mathematics teachers and promoting digital transformation of secondary school mathematics education.
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TPACK has been linked to teachers' capacity to implement structural instructional reform.
“As an integrative representation of technological, pedagogical, and content knowledge, TPACK has been linked to teachers’ capacity to implement structural instructional reform (Tang et al., 2023).”
From AI teaching innovation behavior among college teachers: a structural equation modeling analysis based on the AI-TPACK framework, teaching self-efficacy, professional identity, and AI literacy (2026) · cites this paper as Tang et al., 2023
Section 2.4 describes TPACK as knowledge integrating technology, pedagogy, and content. The Discussion and Conclusions link it to secondary school mathematics teachers’ digital teaching behavior, but they do not establish a link to teachers’ capacity to implement structural instructional reform. (LLM, conf=0.85)
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- Authors
- Tang, X., Yuan, Z., Deng, X., & Xiang, L.
- Affiliations
- Hunan Normal University (CN)
- Venue
- Electronic Research Archive
- Published
- 2023
- Volume
- 31
- Issue
- 10
- Pages
- 6274-6302
- ISSN
- 2688-1594 (ISSN-L)
- OpenAlex
- W4386959055
- Cited by
- 14
- References
- 95
- Citations by year
- 2026: 5 · 2025: 3 · 2024: 6
- FWCI
- 10.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-sa
- Locations
- Electronic Research Archive (publishedVersion, cc-by-nc-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Education › Mathematics Education and Teaching Techniques
- Social Sciences › Education › Education and Technology Integration
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Keywords
- secondary mathematics teachers, UTAUT, TPACK, technology acceptance, teacher attitudes, behavioral intention, performance expectancy, effort expectancy, social influence, digitalization of education, junior high school mathematics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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