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Record W1537299921

A 'uses and gratifications' approach to understanding the role of wiki technology in enhancing teaching and learning outcomes

2009· article· en· W1537299921 on OpenAlexaff
Zixiu Guo, Ying Zhang, Kenneth J. Stevens

Bibliographic record

VenueJournal of the Association for Information Systems · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceGratificationPopularityContext (archaeology)Knowledge managementSet (abstract data type)Educational technologyMathematics educationPsychology
DOInot available

Abstract

fetched live from OpenAlex

The use of the Wikis in both post-graduate and undergraduate teaching is rapidly increasing in popularity. Much of the research into the use of this technology has focused on the practical aspects of how the technology can be used and is yet to address why it is used, or in what way it enhances teaching and learning outcomes. A comparison of the key characteristics of the constructivist learning approach and Wikis suggests that Wikis could provide considerable support of this approach, however research into the motivations for using the technology is required so that good teaching practices may be applied to the use of Wikis when utilized in the higher education context. This study articulates a research design grounded in the Technology Mediated Learning (TML) paradigm that could be used to explore teachers and students’ motivations for using Wiki technology to enhance teaching and learning outcomes. Using the ‘Uses and Gratification’ approach, a popular technique used for understanding user motivation in technology adoption, a two-stage research design is set out. Finally, the paper concludes with a discussion of the implications for both information systems researchers and higher education.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.743
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.292
Teacher spread0.277 · 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 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

Citations9
Published2009
Admission routes1
Has abstractyes

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