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Record W2095085672 · doi:10.1037/1076-898x.11.2.98

The Relative Effectiveness of Various Instructional Approaches in Developing Anticipation Skill.

2005· article· en· W2095085672 on OpenAlexaff
Nicholas J. Smeeton, A. Mark Williams, Nicola J. Hodges, Paul Ward

Bibliographic record

VenueJournal of Experimental Psychology Applied · 2005
Typearticle
Languageen
FieldPsychology
TopicSport Psychology and Performance
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAnticipation (artificial intelligence)Discovery learningAnxietyCognitionPsychologyCognitive psychologyPerceptionDevelopmental psychologyComputer scienceArtificial intelligenceNeuroscience

Abstract

fetched live from OpenAlex

The relative effectiveness of explicit instruction, guided discovery, and discovery learning techniques in enhancing anticipation skill in young, intermediate-level tennis players was examined. Performance was assessed pre- and postintervention, during acquisition, and under transfer conditions designed to elicit anxiety through the use of laboratory and on-court measures. The 3 intervention groups improved from pre- to posttest compared with a control group (n = 8), highlighting the benefits of perceptual-cognitive training. Participants in the explicit (n = 8) and guided discovery (n = 10) groups improved their performance during acquisition at a faster rate than did the discovery learning (n = 7) group. However, the explicit group showed a significant decrement in performance when tested under anxiety provoking conditions compared with the guided discovery and discovery learning groups. Although training facilitated anticipation skill, irrespective of the type of instruction used in this experiment, guided discovery methods are recommended for expediency in learning and resilience under pressure.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 science0.0000.001
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 head0.055
GPT teacher head0.381
Teacher spread0.325 · 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

Citations178
Published2005
Admission routes1
Has abstractyes

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