MétaCan
Menu
Back to cohort
Record W1829568614 · doi:10.1139/cjp-2013-0302

Using photographs to elicit student ideas about physics: The case of an unusual liquid-level phenomenon

2013· article· en· W1829568614 on OpenAlexvenueno aff
Nataša Erceg, Ivica Aviani, Vanes Mešić

Bibliographic record

VenueCanadian Journal of Physics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenonCurriculumMathematics educationPhysics educationPhotographyPhysicsTask (project management)Sample (material)PsychologyVisual artsPedagogyArtQuantum mechanics

Abstract

fetched live from OpenAlex

This work is aimed at exploring some pedagogical opportunities of using photographs in physics instruction. In our study, the photography has been used for eliciting and probing students’ ideas regarding the physics of fluids in noninertial frames of reference and under conditions of equilibrium. The study involved a heterogeneous sample of 235 secondary school students, 41 physics students, and 48 physics teachers. They were presented with a photograph of a wine glass filled with liquid whose surface appeared inclined. The students were asked to comment on the reality of the phenomenon captured in the photograph, and the teachers were asked to predict the students’ responses. The results showed that about half of the students had a complete or partially complete understanding of the physical ideas and that their practical and conceptual knowledge was not dependent on their education level or curriculum followed. Most of the respondents found the task interesting and relevant. The results indicate that the teachers’ expectations regarding students’ understanding of physics often significantly depart from reality. We suggest that physics teachers include some photography-based problems and discussions in their classes. This could encourage a broad participation of students with different levels of abilities.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.140
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.132
GPT teacher head0.412
Teacher spread0.280 · 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

Citations1
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of PhysicsSame topicScience Education and PedagogyFrench-language works237,207