The analysis of Iranian students' persistence in online education
Bibliographic record
Abstract
<p>In the following research, the relationship between instructional interaction and student persistence in e-learning has been analyzed. In order to conduct a descriptive- analytic survey, 744 undergraduate e-students were selected by stratified random sampling method to examine not only the frequency and the methods of establishing an instructional interaction, but also the barriers to the student persistence in e-learning. The research findings confirmed the relationship between the instructors’ and the students’ application of two interaction methods including the discussion forum and email (asynchronous method), as well as the relationship between the frequency of instructional interaction and the student persistence in e-learning. According to the findings, family and job commitment, loss of instructional motivation and economic problems constitute the most important barriers to the student persistence in e-learning. The research results can help reduce one of the primary concerns of online learning, that is the student persistence rate, if they would be implemented in various instructional systems such as higher instructional system, for the purpose of providing favorable condition in e-learning, facilitating online learning interactions and, eventually, increasing the student persistence in e-learning.</p>
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".