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Record W2055465723 · doi:10.11139/cj.30.2.179-203

Preparing Students to use Wiki Software as a Collaborative Learning Tool

2013· article· en· W2055465723 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCALICO Journal · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsFrancophone University Association
Fundersnot available
KeywordsComputer scienceComputer-Assisted InstructionSoftwareMultimediaCollaborative writingTeaching methodWorld Wide WebMathematics educationPsychologyProgramming language

Abstract

fetched live from OpenAlex

In this paper we outline an approach to learner preparation that provides a framework to effectively structure a collaborative research assignment using a wiki site as a writing platform. Even though most students are literate in using social networking software, such as Facebook and Twitter, they do not naturally translate these technology skills to the language learning environment. While students are familiar with posting pictures and videos online, they may not be aware of how to effectively integrate text and visual information to present their research in the online wiki format. Likewise, collaborative writing for an audience requires an understanding of authorship, how to productively collaborate on composing and editing, and interact synchronously and asynchronously through online means to provide feedback. The framework we provide explains how to scaffold wiki-based assignments to ensure an optimal language and culture learning experience. Examples of a wiki research project for intermediate language learners are provided.

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.

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.362
Teacher spread0.344 · 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