Self-, Peer, and Tutor Assessment in Online Microteaching Practice and Doctoral Students’ Opinions
Bibliographic record
Abstract
In online microteaching, pre-service teachers (PSTs) deliver lessons through online platforms, thus acquiring valuable experience in effective use of technological tools. In refining these experiences, it is crucial for the PSTs to undergo self-, peer, and tutor assessments. This study examined the concordance among self-, peer, and tutor assessments in online microteaching practices, along with students’ views on their online microteaching experiences. A case study model was adopted, involving doctoral students enrolled in the Planning and Evaluation in Instruction course. The findings indicated alignment between students’ self-assessment and peer assessment, albeit with lower scores compared to those provided by the course tutor. Overall, students expressed positive views regarding online microteaching. They highlighted the benefits of critical thinking, self-reflection, and peer feedback in refining their teaching strategies. However, challenges such as time management, communication, and planning were noted by the students.
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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 | 0.025 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".