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Record W1784477253 · doi:10.21432/t27g7n

Graduate Students' Experiences of Challenges in Online Asynchronous Discussions

2004· article· en· W1784477253 on OpenAlexaffvenue
Elizabeth Murphy, Elizabeth Burns Coleman

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2004
Typearticle
Languageen
FieldSocial Sciences
TopicOnline and Blended Learning
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsAsynchronous communicationOnline discussionComputer-mediated communicationFeelingGraduate studentsPsychologyQualitative researchQuality (philosophy)Qualitative propertyData collectionMathematics educationMedical educationComputer sciencePedagogyThe InternetWorld Wide WebSocial psychologySociologyMedicine

Abstract

fetched live from OpenAlex

This paper presents one of five categories of findings of a qualitative study of students' experiences of challenges encountered in a web-based graduate program. The findings relate to the category of experiences with online asynchronous discussions. Data collection relied on a discussion, questionnaire and interview all conducted within WebCTTM. The category's findings were grouped into four sub-categories of challenges as follows: student behaviour; text-only, online communication; purpose and quality of the discussion; and forum features. Challenges related to students' behaviour included domination of the discussion by individual students or groups of students resulting in feelings of exclusion, frustration and inadequacy. Text-only communication caused difficulties related to misinterpretation and conveying and deriving intent. Challenges related to the purpose and value of the discussion resulted from low quality and high quantities of postings to meet grade requirements. Technical features that presented challenges included the inability to delete messages.

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)0.012
metaresearch head score (Gemma)0.039
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 score0.012
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.039
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0100.006
Scholarly communication0.0080.005
Open science0.0030.011
Research integrity0.0040.005
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 head0.033
GPT teacher head0.331
Teacher spread0.299 · 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

Citations91
Published2004
Admission routes2
Has abstractyes

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