Curriculum Evaluation in Online Education: The case of Teacher Candidates Preparing Online for Public Personnel Selection Examination
Bibliographic record
Abstract
The aim of the present study is to evaluate the efficiency of an online curriculum based on the views of lecturers and students enrolled in the program. The study is mainly based on survey method. In order to collect qualitative data, interviews forms developed by the researcher were used. The reliability and validity of the interview forms were checked by experts of the field. The qualitative data was analyzed through content analysis. In the first place, data was coded, the themes emerged, the codes and themes were arranged by the researcher. As a final step, the findings were coded and interpreted. Based on the findings of the study, the strengths and weaknesses of the online curriculum were identified and a number of suggestions were offered based on the findings. The results of the study indicate that both learners and lecturers believe that online education is beneficial as well as productive and they are satisfied with the process. Online education was found to be preferable because it is time-saving, more economical, and flexible. On the other hand, limited interaction, unsustainable motivation, and problems caused by insufficient infrastructure were found to be negative aspects of online curriculum.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".