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Record W1685220373 · doi:10.22329/celt.v5i0.3354

25. Using Technology for Tutor and Student Learning Exchange

2012· article· en· W1685220373 on OpenAlexvenueno aff
Katherine Hewlett

Bibliographic record

VenueCollected Essays on Learning and Teaching · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicReflective Practices in Education
Canadian institutionsnot available
FundersUniversity of WestminsterDe Montfort University
KeywordsHigher educationTUTORNegotiationVariety (cybernetics)Learning stylesPedagogyThe artsAction researchSociologyMathematics educationPsychologyPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This project built upon the AchieveAbility initiative, which develops materials and training for teaching specific learning difference learners in schools and colleges. AchieveAbility devised the concept for the ‘InCurriculum’ Project and brought together a consortium of United Kingdom higher education institutions to deliver the practice: Norwich University College of the Arts, the University of Westminster, and De Montfort University. All partners delivered a range of art and design courses, using a variety of complementary learning techniques.The project was set up to investigate how changing teaching and assessment practice could be beneficial to different learning styles. The contextual justification for this action research project was to investigate effective practice to retain students within their higher level courses and to support their successful attainment. The project was funded by the Higher Education Academy for a three year period, during which the United Kingdom educational landscape changed rapidly from a widening access perspective to a more business-orientated model of delivery. To make these changes, technology was shown to be essential to the negotiation that evolved for the learning exchange between the student and staff.

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 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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.843
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.426
Teacher spread0.391 · 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 teacher head, not a consensus.

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".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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