Discrepancies and Similarities Between Online and Face-to-Face Teachers’ Use of Open Educational Resources (OER) for Teaching Purposes
Bibliographic record
Abstract
The integration of open educational resources (OER) in the educational curricula of universities and educational organizations has gained tremendous popularity. However, there is a gap in research on teachers’ attitudes toward OER in many developing countries. Using a mixed-methods approach, this study explored the use of OER by online and face-to-face teachers of English as a foreign language (EFL) in Iran. A total of 62 teachers (31 online teachers and 31 face-to-face teachers) participated in the study. Survey and interview results indicated that there were significant differences between online and face-to-face teachers’ attitudes toward OER. Online teachers had a more positive attitude toward OER than face-to-face teachers. The perceived benefits of OER included developing the flexibility of curricula, encouraging personalized learning, and offering pedagogical options for teachers. There were several perceived OER-based challenges in the educational context of Iran as well. The challenges included teachers’ uncertainty about copyright issues, the low quality of OER, teachers’ low levels of digital literacy, teachers’ unawareness of the existence of OER, the lack of quality control over OER, the lack of credibility of OER content, and the lack of up-to-dateness of OER. There were also significant differences between participants’ perspectives on the types and frequency of using OER. More specifically, online teachers used OER for teaching practices more frequently than face-to-face teachers. Participants perceived that they needed various types of training for the appropriate use of OER. This study proposes several implications for renewing and improving teacher training/education programs and material development projects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".