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Record W2969891286 · doi:10.1145/3418196

Longer Delays in Rehearsal-based Interfaces Increase Expert Use

2020· article· en· W2969891286 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACM Transactions on Computer-Human Interaction · 2020
Typearticle
Languageen
FieldComputer Science
TopicMobile Crowdsensing and Crowdsourcing
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsComputer scienceTransition (genetics)Human–computer interactionExpert systemHarmMode (computer interface)MultimediaArtificial intelligencePsychologySocial psychology

Abstract

fetched live from OpenAlex

Rehearsal-based interfaces are designed to encourage a transition from novice to expert, but many users fail to make this transition. Most of these interfaces activate novice mode after a short delay, between 150 and 500 ms. We investigate the impact of delay time on expert usage and learning in three crowdsourced experiments. The first experiment examines an 8-item marking menu with delay times from 200 ms to 2 s. Results show longer delays increase successful expert selections. The second and third experiments generalise this result to a different rehearsal-based menu, a desktop clone of FastTap with 8 items and 15 items. Together, our results show that expert use correlates positively with increased delay time, but can increase errors since users are less risk averse. We also find imperceptible delays of 200 ms can harm long-term retention of menu items. Designers should consider longer delays in rehearsal-based interfaces to encourage a transition to expert usage.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.053
GPT teacher head0.288
Teacher spread0.235 · 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