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Record W2057277282 · doi:10.1119/1.4849159

Physics Exams that Promote Collaborative Learning

2013· article· en· W2057277282 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Physics Teacher · 2013
Typearticle
Languageen
FieldEngineering
TopicExperimental Learning in Engineering
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFormative assessmentSummative assessmentMathematics educationConsistency (knowledge bases)Physics educationCollaborative learningComputer scienceMedical educationStudent engagementPsychologyMedicineArtificial intelligence

Abstract

fetched live from OpenAlex

The two-stage exam is a relatively simple way to introduce collaborative learning and formative assessment into an exam. Their use is rapidly growing in the physics department at the University of British Columbia, as both students and faculty find them rewarding. In a two-stage exam students first complete and turn in the exam individually, and then, working in small groups, answer the exam questions again. During the second stage, the room is filled with spirited and effective debate with nearly every student participating. This provides students with immediate targeted feedback supplied by discussions with their peers. Furthermore, we see indications that the use of this exam format not only ensures consistency across interactive course components, but it also positively impacts how students approach the other collaborative course components. This is accomplished without losing the summative assessment of individual performance that is the expectation of exams for most instructors. In this paper we describe how to implement two-stage exams and provide arguments why they should be part of physics courses that use interactive engagement and social/collaborative learning methods.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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