Evaluating Current Status of MA Educational Technology Curriculum in Iran from Viewpoint of Experts and Professors in Order to Offering a Desirable Curriculum
Bibliographic record
Abstract
The aim of this research is evaluating status of MA field of educational technology in Iran. This research is qualitative and it is conducted based on survey method. The statistical community of this research is expert professors in educational technology area. Accordingly, 15 persons were chosen among this statistical community as statistical sample using objective sampling of desirable cases. Used tool was semi-structured interview. Questions of the interview were determined based on research questions and five expert professor confirmed its content and apparent validity. The interview was conducted face-to-face during 30 to 60 minutes. Collected information was initially classified and then it was analyzed by category method. Results of the research indicated that from viewpoint of professors, the ‘current’ curriculum does not meet the needs and expectations of students in scope of objectives, content and topics, strategies of learning-teaching and assessment methods. Results that are more precise showed a minimum attention of current curriculum to ‘empowerment’ and ‘attitude’ of students in this field. The offered curriculum of professors for more desirable status emphasized on entrepreneurship and empowerment objectives of students and various, student-oriented educational strategies and practical combined assessment methods.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".