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Record W2116895316 · doi:10.47678/cjhe.v43i3.184674

Online Graduate Student Identity and Professional Skills Development

2013· article· en· W2116895316 on OpenAlexaffvenue
Deborah Hurst, Martha Cleveland‐Innes, Pamela Hawranik, Sarah Gauvreau

Bibliographic record

VenueCanadian Journal of Higher Education · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsAthabasca University
Fundersnot available
KeywordsProfessional developmentGraduate studentsMedical educationPsychologyIdentity (music)Higher educationGraduate educationPedagogyProfessional studiesMathematics educationMedicinePolitical science

Abstract

fetched live from OpenAlex

Graduate students are assumed to develop skills in oral and written communication and collegial relationships that are complementary to formal graduate programs. However, it appears only a small number of universities provide such professional development opportunities alongside academic programs, and even fewer do so online. There appears to be an assumption in higher education that students develop professional skills by virtue of learning through required academic tasks and having proximity to other students and faculty. Skeptics of online study raise questions about whether graduate students studying online can participate fully in such graduate communities and access these informal professional skill-building opportunities. It is possible that such activities may have to be designed and delivered for online graduate students.
 This paper presents preliminary qualitative findings from a project that developed, offered, and evaluated such online opportunities. Findings suggest that while online graduate students can and do develop professional skills while navigating their studies, building relationships, and participating in online learning communities, they are keen to develop such professional skills in a more deliberate way.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.261
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.383
Teacher spread0.350 · 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

Citations17
Published2013
Admission routes2
Has abstractyes

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