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Record W2616027936 · doi:10.1080/0309877x.2017.1323188

Do clickers work for students with poorer grades and in harder courses?

2017· article· en· W2616027936 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 Further and Higher Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInnovative Teaching Methods
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMathematics educationPsychologyCurriculumAptitudeStudent achievementAcademic achievementPedagogyDevelopmental psychology

Abstract

fetched live from OpenAlex

We studied the impact of clickers, also known as electronic student response systems, on the performance of students on two undergraduate finance courses. Consistent with some of the recent literature, we found that clickers have very little impact on student performance, as measured by final course grades. Further, we found that clickers do not have a significant impact on course grades for students in relation to their designated performance ability (weak versus strong) or whether the course in question is less or more difficult. However, after simultaneously controlling for course difficulty and student aptitude, we found that clickers have a meaningfully positive impact on the performance of poorly performing students on more challenging quantitative courses. Our results suggest that the impact of clickers on student performance may depend on the type of student (academically weak, average or strong) and the type of course (average or difficult). This finding has particular implications for curriculum planners at the post-secondary level, although the findings may also have application at the secondary school level.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.250

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.066
GPT teacher head0.486
Teacher spread0.420 · 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