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Record W2111280096 · doi:10.21225/d52w2f

Academic Decision Making Among Adult Learners: Personal and Institutional Factors

2001· article· en· W2111280096 on OpenAlexaffvenue
Joan Fleet, Donna Moore, Susan Rodger

Bibliographic record

VenueCanadian Journal of University Continuing Education · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsWestern University
Fundersnot available
KeywordsAffect (linguistics)PsychologyInstitutionHigher educationMedical educationSociologyMedicinePolitical scienceSocial science

Abstract

fetched live from OpenAlex

This study, designed with considerable input from adult learners, focuses on influences that affect academic decision making. Using questionnaires and selected interviews, information was gathered on institutional and personal influences on academic decision making for current and future courses, along with demographic information. Three quarters of the respondents were under 39 years of age and were looking to their university education to provide knowledge and skills needed for future job opportunities. Within-group analysis revealed a stronger influence on academic decision making from the institution rather than from personal influences, despite a fairly strong positive correlation of the two sets of variables. This pattern was consistent across groups, with no differences being attributed to part- or full-time status or to year in program. Open-ended questions on the questionnaire, as well as follow-up individual interviews, allowed for the input of suggestions to improve the experiences of adult learners in post-secondary institutions. Additional preliminary course information and increased accessibility to courses and services were common themes. In general, the university experience had been very positive; however, some students expressed satisfaction at being invited to contribute suggestions that, if implemented, could improve the experience of future adult learners.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.182
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.312
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 teacher head, 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

Citations3
Published2001
Admission routes2
Has abstractyes

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