Investigation of Teacher Support and Teacher Training During the COVID-19 Pandemic: Tools and Skills Moving the Classroom Forward
Bibliographic record
Abstract
Teaching remotely from home is now compulsory for lecturers as schools across the globe have closed due to the COVID-19 pandemic. A marriage of technology and teacher training is required to help educators deliver lessons effectively online. This research aimed to 1) investigate the type of technological support teachers need to teach online during and after the COVID-19 pandemic; 2) identify the type of teacher training needed during and after the pandemic; and 3) assess teachers’ satisfaction towards their training in relation to their needs. This study utilized a mixed methods research design and included a sample of 59 teachers studying for a Master’s degree in Curriculum and Instruction, majoring in English language at an open university in Thailand. Data were analyzed using the Statistical Package for the Social Sciences (SPSS) to compute means and standard deviations. In addition, qualitative data derived from a questionnaire were analyzed using typological analysis. The research findings showed: 1) the “fundamental technologies” teachers need for online teaching include computers or other computing devices, a reliable and stable-as-possible internet connection, a microphone, and a headset and camera; and 2) the task of implementing engaging lessons online and supporting students to use ICTs for projects or class work placed particular training demands on teachers. Specifically, they required: (1) training to build knowledge of the basic functions for undertaking virtual teaching and learning; (2) access to meaningful and relevant content to create lessons for students, and (3) online worksheets and projects for students.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".