Students’ Perception of the Use of a Rubric and Peer Reviews in an Online Learning Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Moving towards online learning during the coronavirus pandemic presented challenges, such as identifying assessments for learning. Assessments for learning involve using assessments as part of the learning process. Alternative assessments, as opposed to traditional assessments, are favoured for promoting for learning. These assessments include peer assessments and using criteria-referenced tools such as a rubric. Online learning environments often favour automated grading tools such as multiple choice. However, essay-type probing questions help students adopt a deep learning approach. Peer assessments and rubrics can help with grading essay-type questions. However, while the benefits of rubrics and peer assessments are well documented, there is limited research on students’ perceptions in South Africa on the use of rubrics and peer assessments in online environments to facilitate a deep approach to learning. A mixed method approach using a Likert scale and an online qualitative questionnaire was undertaken to explore students’ perceptions of the use of peer assessments with a rubric in an undergraduate module at the University of Johannesburg. Despite a low response rate, four main themes emerged: a clear performance criterion, structured writing, and a deep approach to learning and critical thinking. However, the study also showed limitations of the peer rubric and peer assessments in helping students prepare for formal summative assessment. The results suggest that the rubric and peer assessments, with amendments, could help students adopt a deep approach in online learning environments.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it