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Record W2966558582 · doi:10.19173/irrodl.v20i1.3789

Mid-Career Adult Learners in an Online Doctoral Program and the Drivers of Their Academic Self-Regulation

2019· article· en· W2966558582 on OpenAlexvenueno aff
Peter Williams, Natalie Wall, Wade W. Fish

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2019
Typearticle
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAcademic achievementGraduate studentsSocial supportHigher educationMedical educationComputer-mediated communicationSocial psychologyDevelopmental psychologyPedagogyThe Internet

Abstract

fetched live from OpenAlex

Adult professionals enroll in online graduate programs and rely on social support and on their ability to self-regulate to be successful. The literature on academic self-regulation among emerging adults (traditional college age) is ample, but we do not know how social support interacts with academic self-regulation among adult graduate students at mid-career, particularly among those students who are first generation college goers. This study addressed the following questions: (1) To what degree do parental education level and cohort progression predict academic self-regulation? and (2) What sources of social support – family, friends, loved one (significant other), and classmates – are predictive of academic self-regulation for adult students in an online doctoral program? Findings include evidence that the influence of parental educational level on academic self-regulation persists through midlife. Also, that perceived social support from family, friends, and peers predicts academic self-regulation. We conclude with implications for the design of online programs.

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)0.004
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 score0.044
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 science0.0010.000
Research integrity0.0000.001
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 head0.114
GPT teacher head0.456
Teacher spread0.342 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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