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Record W2525901244 · doi:10.19173/irrodl.v17i5.2024

Online Professional Skills Workshops: Perspectives from Distance Education Graduate Students

2016· article· en· W2525901244 on OpenAlexaffvenueabout
Sarah Gauvreau, Deborah Hurst, Martha Cleveland‐Innes, Pamela Hawranik

Bibliographic record

VenueThe International Review of Research in Open and Distributed Learning · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Practises and Engagement
Canadian institutionsAthabasca University
Fundersnot available
KeywordsMedical educationGraduate studentsFlexibility (engineering)Distance educationFocus groupPsychologyProfessional developmentClass (philosophy)PedagogyMathematics educationSociologyComputer scienceMedicineManagement

Abstract

fetched live from OpenAlex

While many online graduate students are gaining academic and scholarly knowledge, the opportunities for students to develop and hone professional skills essential for the workplace are lacking. Given the virtual environment of distance learning, graduate students are often expected to glean professional skills such as analytical thinking, self-awareness, flexibility, team-building, and problem-solving inherently through informal means (Cleveland-Innes & Ally, 2012). The goal of this study was to evaluate the experiences of online graduate students participating in synchronous online professional skills workshops. Students attended the sessions from the various graduate programs at an online Canadian university. The discussions from the focus group held at the end of the project were used to achieve the research goals. This paper used a phenomenological lens to accomplish its research goals. The participants reported that they experienced a “sense of community” and learned skills that were not included in their academic 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 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)0.007
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0110.006
Scholarly communication0.0090.004
Open science0.0020.009
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.148
GPT teacher head0.552
Teacher spread0.404 · 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 designQualitative
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

Citations26
Published2016
Admission routes3
Has abstractyes

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