A sequential mixed-methods study interviewed and observed 21 technology teachers, then tested a model with 246 teachers across Thai education sectors. Professional learning networks were strongly associated with teacher identity (β=.81) and with technological-literacy transfer (β=.34), supporting a socially and critically negotiated view of technology literacy.
Key findings
- Reported fit was χ²/df=1.53, RMSEA=.047 and CFI=1.00. Professional learning networks predicted teacher identity at β=.81 (p
Why this matters globally
As AI and platforms change rapidly, one-off training may be insufficient. Peer networks can bring bias, power, privacy and learner-impact questions into technology literacy.
Thai researcher contribution
Srinakharinwirot University researchers built the model from Thai teacher narratives and classrooms and tested it across education sectors.
Limitations to consider
Purposive qualitative sampling may not transfer broadly. One-time self-report data invite common-method bias; CFI=1.00 and β=.81 may indicate construct overlap or same-sample tuning. No learner outcomes or network intervention were tested.