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Record W1504272963 · doi:10.19173/irrodl.v3i2.89

Online Polling Services

2002· article· en· W1504272963 on OpenAlexaffvenue
Jon Baggaley, Tom Kane, Bill Wade

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicPrivacy, Security, and Data Protection
Canadian institutionsAthabasca University
Fundersnot available
KeywordsPollingComputer scienceDistance educationMultimediaWorld Wide WebSociologyPedagogyComputer network

Abstract

fetched live from OpenAlex

Real-time collection of student opinions and their instant feedback can be useful in guiding the design and implementation of online educational sessions.Students can gain insights into each others' attitudes and opinions, often anonymously and confidentially; and teachers can adapt to the feedback in directing the progress of a session based on, in effect, a series of "if then" contingency measures.Online tabular and diagrammatic features can provide useful visual summaries of polling results, indicating whether or not the session's learning outcomes are being achieved.Online polling services available differ greatly in the extent to which they provide these useful educational features.To guide distance educators in their selection and implementation of online polling and quizzing procedures, an evaluation study was conducted featuring a selection of the poll creation systems that were available at the time of study.All questionnaire, quizzing, survey, and assessment products/ services are referred to generically in the report as "polling systems."

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)0.005
metaresearch head score (Gemma)0.020
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: none
Teacher disagreement score0.091
Threshold uncertainty score0.306

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.004
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0910.033

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 head0.148
GPT teacher head0.468
Teacher spread0.320 · 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

Citations7
Published2002
Admission routes2
Has abstractyes

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