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Record W1992361844 · doi:10.1002/bmb.20264

Using clickers to improve student engagement and performance in an introductory biochemistry class

2009· article· en· W1992361844 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

VenueBiochemistry and Molecular Biology Education · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsClass (philosophy)Student engagementMathematics educationSignificant differencePsychologyMedical educationComputer scienceMathematicsMedicineStatistics

Abstract

fetched live from OpenAlex

As part of ongoing efforts to enhance teaching practices in a large-class introductory biochemistry course, we have recently tested the effects of using a student response system (clickers) on student exam performances and engagement with the course material. We found no measurable difference in class mean composite examination score for students taught with clickers than for those taught in traditional lectures. However, there were significantly more students in the highest achievement category (91-100%) in the section that incorporated clickers than in any other section over five academic terms. Overall, students gave high approval ratings for the use of the clickers, particularly in increasing their participation and engagement in lectures. However, students who reported their performance to be in the lowest performance categories gave a lower level of approval for the use of the clickers than those who reported their performance to be in the higher performance categories. The implications for using clickers to improve teaching in biochemistry are discussed.

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)0.001
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.030
GPT teacher head0.422
Teacher spread0.392 · 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