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Record W2171989406

Examining the Use of Audience Response Systems in Secondary School Classrooms: A Formative Analysis.

2010· article· en· W2171989406 on OpenAlexaff
Robin Kay, Ann LeSage, Liesel Knaack

Bibliographic record

VenueThe Journal of Interactive Learning Research · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSummative assessmentFormative assessmentAudience responseKey (lock)Perspective (graphical)Computer sciencePsychologyMathematics educationMultimediaMedical educationMedicine
DOInot available

Abstract

fetched live from OpenAlex

To date, extensive research has been done on the use of au� dience Response Systems (aRSs) in colleges and universi� ties, but not in secondary schools. The purpose of this study was to conduct a detailed formative analysis on the benefits, challenges, and use of aRSs from the perspective of 659 sec� ondary school students. Key benefits reported were increased levels of engagement and motivation, the effective use of for� mative assessment, and a better quality learning environment. Key challenges included a perceived decrease in learning per� formance when an aRS was used for summative assessment, technological malfunctions, resistance to using a new method of learning, and increased stress due to perceived time con� straints. Finally, students consistently rated the use of an aRS significantly higher when it was used for formative as op� posed to summative assessment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.105
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.153

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.105
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.230
GPT teacher head0.494
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations13
Published2010
Admission routes1
Has abstractyes

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