Online Courses in the Higher Education System of Iran: A Stakeholder-Based Investigation of Pre-Service Teachers’ Acceptance, Learning Achievement, and Satisfaction
Bibliographic record
Abstract
This study focused on the acceptance level of higher education stakeholders of teaching English as a foreign language (TEFL) of online courses in Iran and pre-service teachers’ learning achievement in online courses. Three cohorts of participants who were teaching or learning in online courses included pre-service teachers of TEFL (n=104), TEFL university instructors (n=23), and heads of TEFL departments (n=10). A questionnaire was designed. The Kruskal Wallis test was used to detect differences among the perspectives of the participants. Semi-structured interviews were also utilized. Results indicated that there were significant differences among the perspectives of the three groups of participants about online courses. The pre-service teachers appeared to be relatively positive about online learning, while the university instructors and heads of departments showed a lower level of satisfaction. The participants pointed out several challenges, including the lack of rigor of online courses, the incredibility of the certificates, the lack of technological infrastructures, technical problems, the impractical content of the lessons, the lack of human interaction, the low competence levels of online learning students, and employers’ lack of interest in employing graduates of online courses. The participants also mentioned that pedagogical and technological training was required for both university instructors and pre-service teachers of TEFL. The comparison of pre-service teachers’ mid-term and final scores in the online courses showed a significant difference and improvement of students’ learning achievement in online courses with medium to large effect sizes. In the interviews, the participants also confirmed that online courses could improve student learning.
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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 | 0.003 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".