Using digital peer observation to balance professional development and performance evaluation
Bibliographic record
Abstract
This paper reports on how our Digital Peer Observation Process was developed; it describes the small scale pilot project, analyses feedback from the participants and manager, and speculates about further refinements to the process and possible future applications. The benefits of peer observation include evaluating expectations and beliefs, increasing confidence and collegiality, and improving pedagogy (Brockbank & McGill, 2006; Chester, 2012). Limitations included risk of self-deception and a lack of action following reflection (Brookfield, 1995; Carroll, 2009), time commitments (Chester, 2012; Hampton et al. 2004; Malthus, 2013) and the potential impact of having an observer in the consultation room. While acknowledging these benefits and limitations, the Navitas Academic Language and Learning (ALL) team had some additional concerns with the traditional peer observation process. These concerns included participants’ geographical distance, variations in work schedules, and balancing requirements for performance evaluation and low-cost professional development. During the pilot project, various ALL services were recorded via video conferencing or screen capture software, then observed using reflection guidelines developed by the team. The new digital process had three main benefits: team collegiality, clarity of the team’s vision and identity, and a balance of professional development and performance evaluation. In the pilot project, three challenges emerged from staff feedback: time commitment, misunderstanding of the process and materials, and concerns around giving colleagues ‘negative feedback’. In subsequent iterations, there is potential to explore further uses of technology and data in other contexts. The aim of this pilot project was to examine if digital tools and explicit processes could effectively balance teacher professional development using critical reflection and performance review for our national ALL team.
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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 | 0.094 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".