Digital Feedback in a Crisis Period: A Study of Electronic Corrective Feedback on Ghanaian Students' Business Letters During the COVID-19
Bibliographic record
Abstract
This study examined the efficacy of electronic corrective feedback (ECF) on the business letters of selected Ghanaian technical university students.A sequential exploratory mixed-methods design was used for this study.Thus, the study employed both qualitative and quantitative data.Consequently, 80 scripts from 40 students (i.e., 40 pre-test and 40 post-test scripts), and 20 questionnaire items from the same students were used.At the pre-test level, the participants composed one business letter each.Afterwards, the ECF intervention was applied to the pre-test items.Then, they were asked to write another letter at the post-test level.After the pre-and posttest activities were conducted, the participants filled out questionnaires.Therefore, the sample size (in terms of raw data) was 120.The research, thus, investigated the types of ECF provided by teachers on students' scripts, students' perceptions of ECF, and its impact on their writing skills.Findings indicate that teachers primarily used MS Word's track change feature to provide direct ECF, focusing on vocabulary, spelling, concord, punctuation, syntactic, and semantic errors.Students generally perceived this ECF as beneficial, reporting improved awareness of writing errors and enhanced writing skills.However, challenges such as limited Internet access, delayed feedback, electricity fluctuations, and difficulties in reviewing the pre-test items were noted.The study recommends that educators adapt feedback strategies to suit online learning environments better, incorporating multimedia feedback and regular virtual check-ins to enhance student engagement and understanding.The findings contribute to the growing body of research on technology-enhanced learning and feedback, offering insights into the potential of ECF to support student writing development in Ghana and at technical universities.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".