Assuring online assessment quality: the case of unproctored online assessment
Bibliographic record
Abstract
Purpose This study aims to examine the impact of the transformation of an assessment on students’ performance and perspectives in an English for Academic Purposes course in Hong Kong. The assessment was changed from the traditional pen-and-paper mode to an unproctored online mode. Design/methodology/approach Using mixed methods, the research team analysed the differences between the assessment performances of those who took the course before the pandemic (n = 664) and those who took it during the pandemic (n = 702). Furthermore, focus group interviews were conducted with seven students regarding their perspectives on the unproctored assessment. Findings The results revealed that, although there were no major differences in the overall grades of the two groups, students who were assessed online during the pandemic performed significantly better in terms of their English use. Nevertheless, the shift to online assessment had several negative effects on the students. Originality/value Previous studies on unproctored online assessments (UOA) were concerned with potential learning quality issues, such as plagiarism and grade inflation. This study, however, provided empirical evidence that high-quality assessment delivery can be provided via UOA if the question types and assessment arrangements are carefully decided.
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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 | 0.046 | 0.273 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".