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Ideas to Minimize Exam Anxiety

2003· article· en· W2170929093 on OpenAlexafffund
Steve D. Roney, Donald R. Woods

Bibliographic record

VenueJournal of Engineering Education · 2003
Typearticle
Languageen
FieldMedicine
TopicMedical Education and Admissions
Canadian institutionsMcMaster UniversityStatistics Canada
FundersMcMaster University
KeywordsAnxietyPsychologyTest anxietyTerm (time)Test (biology)Clinical psychologyMedical educationApplied psychologyMathematics educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract The debilitation score from the Alpert‐Haber Anxiety Achievement Test was used to identify students suffering from exam anxiety. Principal component Analysis was then used to show that the debilitation score was strongly related to short‐term and long‐term anxiety and self‐image and not, as expected, to study skills, problem‐solving skills, or avoidance to engage in solving difficult problems. Required workshops to help students address low self‐image and high short‐term and long‐term anxiety were introduced, but they had modest short‐term success. However, significant improvement in student performance occurred when faculty included measures of student performance other than the final exam (such as term work, projects, and self‐assessment) and when students contracted for the weighting that the final exam would contribute to their final grade. The use of self‐assessment was effective for all students regardless of their level of exam anxiety.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0140.004

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 head0.013
GPT teacher head0.306
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations11
Published2003
Admission routes2
Has abstractyes

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