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Record W2803987840

Using digital peer observation to balance professional development and performance evaluation

2018· article· en· W2803987840 on OpenAlexaboutno aff
Rachel Anna Maissan, Fiona Elizabeth Perry

Bibliographic record

VenueJournal of academic language and learning · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
Fundersnot available
KeywordsCollegialityCLARITYProcess (computing)Peer feedbackProfessional developmentComputer sciencePsychologyProcess managementPedagogyBusiness
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.094
metaresearch head score (Gemma)0.165
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.094
Threshold uncertainty score0.497

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0940.165
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0040.004
Scholarly communication0.0050.005
Open science0.0030.012
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0060.002

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.

Opus teacher head0.084
GPT teacher head0.455
Teacher spread0.371 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes1
Has abstractyes

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