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Record W3202243141 · doi:10.1119/10.0006460

TiltTray: A 3D-Printed Apparatus to Teach Inclined-Plane Physics Through Smartphone Portrait-Landscape Transitions

2021· article· en· W3202243141 on OpenAlexaff
Chris Isaac Larnder

Bibliographic record

VenueThe Physics Teacher · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicExperimental and Theoretical Physics Studies
Canadian institutionsJohn Abbott College
Fundersnot available
KeywordsEveryday lifeInclined planeMobile devicePhysics educationMechanism (biology)PortraitPlane (geometry)Human–computer interactionMultimediaComputer sciencePhysicsMathematics educationEngineeringVisual artsPsychologyWorld Wide WebMechanical engineeringGeometryMathematicsArtEpistemology

Abstract

fetched live from OpenAlex

Today’s students are increasingly immersed in a landscape of screens and handheld digital devices through which a good deal of their interactions with the world around them are mediated. Physics educators, meanwhile, continue to rely on traditional human interactions with the physical world, such as sliding down a ramp or throwing a baseball, in order to illustrate fundamental concepts in physics. Regrettably, these interactions are decreasingly representative of the kinds of everyday activities that our students engage in, reducing their degree of engagement with the material. A new opportunity lies in the behavior of smartphones in response to sustained tilted orientations, which has for some time become a familiar mechanism of interaction between students and many of the mobile apps that they engage with on a daily basis. Here we demonstrate how a methodical investigation of this digital-era mechanism can be used to introduce the inclined plane, a standard topic in most introductory mechanics courses. We also present an open-source 3D-printed apparatus designed to support this investigation and the experiences from well over 1000 students in three different colleges.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.799
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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