Effectiveness of Using Screencast Feedback on EFL Students’ Writing and Perception
Bibliographic record
Abstract
<p>This mixed-methods research was carried out to investigate the effect of screencast video feedback on the writing of freshmen, studying academic writing course at a university in Egypt, and explore their perception towards receiving screencast feedback. Two classes of 63 students were chosen to participate in this study and were assigned into two groups; an experimental group (33 students) and a control one (30 students). While the control group received written comments, the experimental group received video feedback to the higher order concerns of writing (content, organization and structure) and written feedback to the lower order concerns (accuracy) of their writings. Two writing tests were administered to the two groups before and after the experiment. To investigate the perception towards screencast feedback, an online questionnaire was applied to the experimental group after the experiment. Results showed that the experimental group outperformed the control group in the higher order concerns of writing as well as the overall writing skill in the writing posttest. Findings also revealed that the majority of students in the experimental group perceived screencast feedback positively for being clear, personal, specific, supportive, multimodal, constructive, and engaging. However, they reported few challenges such as slow loading time and inability to download videos to their computers. The research concludes with implications for practitioners and researchers.</p>
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".