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Record W3081868366 · doi:10.2505/4/jcst15_044_05_48

An Improved Design for In-Class Review

2015· article· en· W3081868366 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

VenueJournal of College Science Teaching · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMathematics educationClass (philosophy)Science educationTeaching methodHigher educationPedagogyPsychologyComputer scienceArtificial intelligencePolitical science

Abstract

fetched live from OpenAlex

We present the theory and implementation of a review strategy based on testing rather than lecturing. We also show the results of a beginning-of-course review using the format of a two-stage examination, in which students complete a set of questions individually, then again as a group. This format offers several benefits compared with the typical lecture review: (a) students engage with the review topics much more deeply and more accurately gauge their own preparation; (b) students receive immediate, corrective feedback from their peers and clarify their understanding through discussion during the group stage; and (c) the instructor receives detailed information on students’ background understanding that can be used to tailor instruction. These proposed benefits are supported by the improved performance of groups during the second stage and by student opinions collected by survey several days after the review activity. The two-stage review therefore serves to both diagnose and remediate deficiencies in background understanding, leaving students and instructors better prepared for the course.

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)0.121
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.502
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1210.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
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 head0.157
GPT teacher head0.479
Teacher spread0.323 · 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