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Record W2337657492

Shifting Towards Inquiry-Orientated Learning in a High School Outreach Program

2015· article· en· W2337657492 on OpenAlexaboutno aff
Tom Gordon, Manjula Sharma, Helen Georgiou, M. J. Hill

Bibliographic record

VenueResearch Online (University of Wollongong) · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsLogbookSyllabusOutreachMathematics educationSession (web analytics)Quarter (Canadian coin)Medical educationPsychologyPedagogyComputer scienceMedicinePolitical scienceWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

This paper presents results of an examination the effect of the introduction of inquiry-orientated learning, IOL, activities into the formal education outreach program for senior high school Physics students run by School of Physics at the University of Sydney, 'Kickstart Physics.' This is the flagship outreach program from the Faculty of Science and accommodates approximately a quarter of the total number of students that sit the state Physics exam. The project considered how students arrive at different inquiry-orientated outcomes such as making hypotheses, displaying and interpreting data, validity, reliability, as well as the mental effort reported by the students during the Kickstart workshop. Results from a survey developed for this study show that a significant increase in mental effort was applied by the students in the inquiry-orientated logbook. In addition, the results presented here suggest that the existing, non-modified logbook, based on the current state (NSW) syllabus, has a strong inquiry focus. The methodology was to survey Senior high school students taking part in the Kickstart workshops. Some were given a logbook to accompany the session designed from the NSW Physics syllabus, and some were given a logbook designed around inquiry-orientated learning. The content related to the syllabus for each workshop remained constant.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.377
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.325
GPT teacher head0.506
Teacher spread0.180 · 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 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

Citations4
Published2015
Admission routes1
Has abstractyes

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