Using the Technological, Pedagogical, and Content Knowledge Framework to Design Online Learning Environments and Professional Development
Abstract
In this article we sought to understand how social studies teachers' metacognitive awareness of their technological, pedagogical, and content knowledge (TPACK) changed after their participation in a program that consisted of: (a) professional development for the use of an online learning environment; and (b) using an online learning environment in their classrooms. Inservice teachers who went through the TPACK-based program experienced considerable movement within the TPACK diagrammatic knowledge domains and expressed positive and encouraging comments regarding their knowledge domains portrayed within the TPACK framework. Quantitative and qualitative results are shared along with implications of designing professional development, online learning environments using TPACK, and advancing the TPACK framework itself.
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Evidence from participants carrying out training programmes based on the Technological Pedagogical Content Knowledge framework reported gains across the different knowledge domains and a more integrated knowledge structure, which in one study also influenced their thinking about concerns associated with technology.
“From a different perspective, evidence from participants carrying out training programmes based on the Technological Pedagogical Content Knowledge (TPACK) framework 2 reported gains across the different knowledge domains and a more integrated knowledge structure (Doering et al. 2009;Niess and Gillow-Wiles 2014) that, in the latest study, also influenced their thinking about the concerns associated”
From COVID-19 and teacher education: a literature review of online teaching and learning practices (2020) · cites this paper as Doering et al. 2009
More and more studies are acknowledging the different pedagogical approach required for an effective online learning experience.
“More and more studies are acknowledging the different pedagogical approach required for an effective online learning experience (e.g., Doering et al. 2009;Niess and Gillow-Wiles 2014)”
From COVID-19 and teacher education: a literature review of online teaching and learning practices (2020) · cites this paper as Doering et al. 2009
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- Authors
- Doering, A., Veletsianos, G., Scharber, C., & Miller, C.
- Affiliations
- University of Minnesota (US)
- University of Manchester (GB)
- Corresponding author
- Doering, Aaron
- Venue
- Journal of Educational Computing Research
- Publisher
- SAGE Publications
- Published
- 2009-10
- Volume
- 41
- Issue
- 3
- Pages
- 319-346
- ISSN
- 0735-6331 (ISSN-L)
- 1541-4140
- OpenAlex
- W2099109822
- Cited by
- 257
- References
- 39
- Citations by year
- 2026: 2 · 2025: 6 · 2024: 15 · 2023: 13 · 2022: 14 · 2021: 9 · 2020: 17 · 2019: 24 · 2018: 20 · 2017: 17 · 2016: 22 · 2015: 35 · 2014: 16 · 2013: 20 · 2012: 10
- FWCI
- 38.25
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Educational Computing Research (publishedVersion)
- Topics
- Social Sciences › Education › Mathematics Education and Teaching Techniques
- Social Sciences › Education › Teacher Education and Leadership Studies
- Social Sciences › Education › Education and Technology Integration
- Keywords
- TPACK, technological pedagogical content knowledge, social studies teachers, teacher professional development, virtual learning environments, metacognitive awareness, in-service teachers, teacher knowledge domains
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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