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Record W3138128324 · doi:10.21125/inted.2021.1267

BEYOND PROFESSIONAL NOTICING

2021· article· en· W3138128324 on OpenAlexaboutno aff
Marie J. Myers

Bibliographic record

VenueINTED proceedings · 2021
Typearticle
Languageen
FieldPsychology
TopicVisual and Cognitive Learning Processes
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceHuman–computer interaction

Abstract

fetched live from OpenAlex

Professional ‘noticing’ has received increased attention in recent years (Grossman, 2011; Rooney & Boud, 2019).Researchers, however, especially for research in visual perception suggest that students make choices to concentrate on something, often regardless of teacher’s effort to direct them in a given way. To increase paying attention, Wageman’s et al, 2012) recommend to signal abnormalities, rather than similarities. The break from the flow of habitual things through surprise, the unexpected, various different positioning than anticipated will consolidate learning. Brock (1983) suggests that the proposed visual context be complex as the additional effort required also increases noticing, as eyes gloss swiftly over what is familiar, only paying attention to what makes the eyes stop, which in turn creates concentration. Palmer and Rock (1994) and Biederman (1984) recommend to just let people’s eyes move freely. This would mean the need to create contexts from which all learners can glean what is intended for learning. Nevertheless, psychologists in visual perception and visual cognition add more steps. They still see the need to research the points of visual impact for drawing attention, create the desire and the willingness to comprehend and to learn, without which input will not turn into intake. So this need to create a willingness to understand what to concentrate on, appears to be more important in order to take next steps and draw information from it and follow by action.Methodology: The site is a university teacher education program in Canada. The participants are three groups of approximately 20 students in their professional preparation course. The design consists first of a theoretical research component, followed by a design of applications with simulated activities and their implementation followed by a qualitative study, during which observation notes were taken and analyzed to uncover relevant findings (Creswell,1994; Creswell & Poth, 2018; Patton, 2002, 2014). Sequences were developed, not only for professional noticing but also for willingness to understand and the follow-up effort to spring into action. Such observations stem from interactions with people and learning materials in professional practice (Fenwick et al, 2011). As well, studies on experienced teachers point to the importance of observation (Barnhart & Van Es, 2015; Stürmer et al, 2015).Observational notes taken during the processes in these teacher-training classes were analyzed to uncover themes and strategies.Results: Among a number of unusual findings, some general tendencies were uncovered. Specifically directed questions guide attention more closely to where it is wanted and bring about more noticing. Group work helps engagement into intention to carry out tasks. The synergy among participants, especially coming from group leaders tends to produce expected results. That would mean that team teaching or teaming up professionals, with carefully assigned partnerships would prove to be more effective. Another important finding points to the fact that the will, to participate actively in action, is distributed unevenly. Some participants showed a capacity to carry out fully while others stopped in mid-stream. It is, however, unclear if the observation of those who appeared to be stopping mid-stream, does not also carry a momentum that goes full out later. There needs to be a further study of action as run-through for reinforcement and verification.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.103

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.007
Scholarly communication0.0080.008
Open science0.0010.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0310.007

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 head0.023
GPT teacher head0.354
Teacher spread0.331 · 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 designNot applicable
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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