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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

<p class="3">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.</p>

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.480
Threshold uncertainty score0.901

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
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 integrity0.0000.000
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 head0.148
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 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

Citations26
Published2016
Admission routes3
Has abstractyes

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