Effects of Interaction in Online Language Classroom: A Case Study in Najran University
Bibliographic record
Abstract
This research investigated the effects of interaction in online language classroom. A mixed method research design using questionnaire and interview was employed to elicit quantitative data from teachers (N=30) and students (N=55). In addition, a semi-structured interview was conducted with faculty members (N=10) to collect the qualitative responses on the effects of interaction in online language classroom. The data was analyzed using SPSS software (23.0). The results of the questionnaire revealed that students’ interaction in online language classroom had positive effects on learning. However, the qualitative data contradicted the questionnaire results. Based on the contrary findings, the researchers offered the possible reasons for the difference of opinions. Solutions, based on the participants’ qualitative responses, are offered to address the interactions’ issues as highlighted by the respondents during the semi-structured interview. As e-learning platforms are being used worldwide in academia, this study will have universal implications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".