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Record W2591254413 · doi:10.1080/02660830.2017.1283754

Learning attributes, academic self-efficacy and sense of belonging amongst mature students at a Canadian university

2017· article· en· W2591254413 on OpenAlexaffabout
Sandra E. Erb, Maureen Drysdale

Bibliographic record

VenueStudies in the Education of Adults · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsSt. Jerome's UniversityUniversity of Waterloo
Fundersnot available
KeywordsSelf-efficacyPsychologyPersistence (discontinuity)Test anxietyHigher educationTest (biology)Academic achievementSelf-conceptAnxietySocial psychologyPsychology of selfDevelopmental psychologyMathematics educationPolitical science

Abstract

fetched live from OpenAlex

The current study investigated learning attributes, academic self-efficacy and sense of belonging of mature adult students in higher education. Undergraduate students of all ages at a large Canadian research intensive university were recruited to participate in an online survey. Mature and traditional age students were compared on their motivation and learning strategies, test anxiety, academic self-efficacy and sense of belonging. Results revealed that mature students possessed significantly higher levels of overall academic self-efficacy and lower levels of test anxiety than traditional age students, boding well for their success in higher education. However, significantly lower levels of sense of belonging reported by mature students provide cause for concern considering the relationship between sense of belonging, persistence in higher education and well-being. Implications, limitations and recommendations for future research are provided.

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)0.002
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 score0.341
Threshold uncertainty score0.685

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0020.000
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 head0.031
GPT teacher head0.400
Teacher spread0.369 · 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

Citations30
Published2017
Admission routes2
Has abstractyes

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