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Record W1970193156 · doi:10.2202/1548-923x.1132

Goal Orientation and its Relationship to Academic Success in a Laptop-based BScN Program

2005· article· en· W1970193156 on OpenAlexaff
Sandra Goldsworthy, Bill A. Goodman, Bill Muirhead

Bibliographic record

VenueInternational Journal of Nursing Education Scholarship · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsLaptopPsychological interventionOrientation (vector space)PsychologyApplied psychologyMedical educationGoal orientationComputer scienceMedicineSocial psychologyMathematics

Abstract

fetched live from OpenAlex

This longitudinal study, conducted within a laptop-based BScN program examines the relationship of goal orientation profiles to comfort with technology and academic success. In phase 1 of this study, 101 first year nursing students completed an on line survey. The measurement tools used were Goal Orientation Assessment, Multiple Intelligences Learning Inventory and a locally developed Technology Comfort survey. Results showed that students were predominantly high in the mastery goal orientation profile. Males had a higher comfort level with technology. Age was inversely related to comfort with technology. An unexpected finding was that grade point average was inversely related to comfort with use of technology. The data did not support the commonly held belief that today's students are uniformly well-skilled and comfortable with new technologies. This study will continue over the next three years and will allow comparison of variables over time. Specific teaching interventions may be developed to accommodate varying learning and motivational styles in relation to comfort with technology.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
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 head0.076
GPT teacher head0.498
Teacher spread0.422 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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