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Record W2151153786 · doi:10.1080/01421590801965111

Rules of engagement–12 tips for successful use of “clickers” in the classroom

2008· article· en· W2151153786 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

VenueMedical Teacher · 2008
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFormative assessmentSummative assessmentVariety (cybernetics)Computer scienceStudent engagementClickerAudience responseMedical educationMultimediaMathematics educationPsychologyMedicine

Abstract

fetched live from OpenAlex

BACKGROUND: Student response system or clickers is an electronic application where a receiver in the instructor's computer captures responses to questions from student keypads. Used effectively, clickers can promote learner engagement and serve to improve learning. It can be used in a variety of ways such as to provide feedback to learners and instructor, to start discussions, for peer evaluation, for formative and summative assessment, to build a learning community, and to experiment on human responses. AIMS AND METHODS: Using our experience in the use of this technology and literature review, we provide twelve tips for successful use of the student response system. RESULTS AND CONCLUSIONS: We have found these strategies useful and envisage that the application of these tips can help maximize learner engagement and learning.

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)0.012
metaresearch head score (Gemma)0.012
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.844
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.240
GPT teacher head0.429
Teacher spread0.189 · 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