MétaCan
Menu
Back to cohort
Record W1904233774 · doi:10.21432/t2x60f

Comparison of Student Experiences with Different Online Graduate Courses in Health Promotion

2005· article· en· W1904233774 on OpenAlexvenueno aff
Stanley Varnhagen, Douglas A. Wilson, Eugene Krupa, Susan Kasprzak, Vali Hunting

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAsynchronous communicationComputer-mediated communicationQualitative researchContent analysisMathematics educationDistance educationInstructional designPromotion (chess)Graduate studentsEducational technologyTeaching methodMedical educationPedagogyComputer scienceThe InternetWorld Wide WebMedicine

Abstract

fetched live from OpenAlex

The purpose of this study was to understand the experience of students as they progressed through three specific online graduate courses in health promotion studies delivered primarily by asynchronous computer conferencing. Focused teleconference discussions were conducted with approximately 45 students from the different courses and the transcripts subjected to qualitative analysis. Themes that emerged included what new students appreciated most when adapting to learning online, factors that contributed to learner satisfaction, and the difficulties encountered by students taking a course when the content was not as well suited to the instructional method. The findings are discussed in relation to the three components of Garrison, Anderson and Archer’s (2000) Community of Inquiry model of learning: cognitive, social and teacher presence. Implications are presented for assisting students with the process of adapting to online learning and enhancing the ‘fit’ between course content and online instructional methods.

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)0.004
metaresearch head score (Gemma)0.016
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0020.002
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 head0.050
GPT teacher head0.392
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 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

Citations20
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Learning and TechnologySame topicOnline and Blended LearningFrench-language works237,207