Centering Cultural Knowledge in TPACK— Evidence From a Collaborative Online International Learning Collaboration
Bibliographic record
Abstract
In this qualitative study, we analyzed the processes of a collaborative online international learning (COIL) collaboration between two higher education institutions in Japan and the United States from the perspective of the technological, pedagogical, and content knowledge (TPACK) framework. The research question this study aimed to address was: What is the utility of the TPACK framework, as a lens of analysis, for this online cultural exchange? To address this question, we conducted semi-structured interviews with student participants and examined their written works. From the student participants’ learning experiences, we identified evidence of cultural exchange as well as evidence of missed opportunities for cultural exchange arising from the limited knowledge of technology, pedagogy, content, and culture. COIL and TPACK both share a common goal of increasing students’ access to multiple knowledge systems using educational technology. As a result, COIL conceptually aligns well with the TPACK framework. This collaboration showed an ongoing need for the centering of cultural knowledge and cultural exchange in both COIL and TPACK. We, accordingly, outline potential for a TPACCK, a modified TPACK framework to center cultural knowledge in both with the hope of taking steps towards a more culturally sustaining framework of international collaboration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".