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Record W216483488

Pupils' Interaction with the Exhibits According to the Learning Behaviour Model

2004· article· en· W216483488 on OpenAlexaboutno aff
Kristel Mõistus

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2004
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsNatural scienceNatural (archaeology)Informal learningScience educationScience learningMathematics educationComputer scienceSociologyData sciencePsychologyEpistemologyPedagogyGeography
DOInot available

Abstract

fetched live from OpenAlex

Science centres are one of the best opportunities for informal study of natural science. There are many advantages to learn in the science centres compared with the traditional methods: it is possible to motivate and supply visitors with the social experience, to improve people’s understandings and attitudes, thereby bringing on and attaching wider interest towards natural science. In the science centres, pupils show interest, enthusiasm, motivation, self-confidence, sensitiveness and also they are more open and eager to learn. Traditional school-classes however mostly do not favour these capabilities. This research presents the qualitative study in the science centre. Data was gathered from observations and interviews at Science North science centre in Canada. Pupils’ learning behaviours were studied at different exhibits in the science centre. Learning behaviours are classified as follows: labels reading, experimenting with the exhibits, observing others or exhibit, using guide, repeating the activity, positive emotional response, acknowledged relevance, seeking and sharing information. In this research, it became clear that in general pupils do not read labels; in most cases pupils do not use the guides help; pupils prefer exhibits that enable high level of interactivity; pupils display more learning behaviours at exhibits that enable a high level of interactivity.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.895
Threshold uncertainty score0.729

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.051
GPT teacher head0.365
Teacher spread0.313 · 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.

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".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueKTH Publication Database DiVA (KTH Royal Institute of Technology)Same topicInnovative Teaching and Learning MethodsFrench-language works237,207