Teacher Candidates’ Perceived Learning in an International Exchange Program: An ICT Course Example
Bibliographic record
Abstract
This article reports the perceived learning of a group of Chinese teacher candidates who audited an ICT (Information and Communication Technologies) literacy course while participating in an exchange programme between Southwest University in China and the University of Windsor in Canada. Data were collected through 1) reflective notes written by visiting students and 2) semi-structured interviews conducted with them towards the end of their visit. The majority of participants stated that the learning experience helped them to realise the important role theory plays in the learning of ICT and to seek ideas of how to creatively integrate ICT in their future classrooms. Participants with limited ICT knowledge and skills reported that by being exposed to various functions of frequently used programmes and many free software programmes, they felt more confident in using ICT in their own teaching. Furthermore, those with strong ICT backgrounds found that the course helped them to understand the relationship among ICT, society, and pedagogy. The teacher candidates’ perceived learning included aspects of culture and pedagogy in addition to ICT knowledge and skills. Coming to know in ways like this is critically important to international partnerships and foundational to reciprocal learning where each learns from the other.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".