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Record W2884511286 · doi:10.1177/1098214018781506

Making Space for Adaptive Learning

2018· article· en· W2884511286 on OpenAlex
Barbara Szijarto, J. Bradley Cousins

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAmerican Journal of Evaluation · 2018
Typearticle
Languageen
FieldDecision Sciences
TopicEvaluation and Performance Assessment
Canadian institutionsUniversity of Ottawa
FundersUniversity of Ottawa
KeywordsInterrogationSpace (punctuation)MediationPsychologySocial learningProcess (computing)Social psychologyComputer scienceSociologyPedagogyPolitical scienceSocial science

Abstract

fetched live from OpenAlex

This article reports findings from a research program exploring the role of mediation in an “adaptive learning” process through study of developmental evaluation (DE). Our study focuses on how mediators might influence the relationships between components of a social learning system and the implications for adaptive learning. Specifically, we focused on evaluators making space for the interrogation of ideas and choices, why this is important, what strategies are used, and what challenges present. Data from a multiple case study of four DEs revealed multiple drivers behind a need to make space, including new trust factors, uncertainty and anxiety, and learning-related norms. Strategies that were employed included turning down the heat, seeking balance among competing needs, normalizing evaluation practice, and legitimizing DE. Results are discussed in terms of implications for evaluation capacity building in adaptive learning contexts. Questions for future inquiry are posed.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.389
GPT teacher head0.584
Teacher spread0.195 · 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