MétaCan
Menu
Back to cohort
Record W2186820346

THE CHALLENGE OF USING WIKIS IN SCHOOL: THE EXPERIENCES OF TWO GRADE SIX TEACHERS.

2012· dissertation· en· W2186820346 on OpenAlexaboutno aff
Jane Chin

Bibliographic record

VenueQSpace (Queen's University Library) · 2012
Typedissertation
Languageen
FieldSocial Sciences
TopicEducation and Technology Integration
Canadian institutionsnot available
Fundersnot available
KeywordsMathematics educationPedagogyPsychology
DOInot available

Abstract

fetched live from OpenAlex

This study augments an expanding body of research literature examining the complex process of integrating Information and Communication Technology (ICT) and introducing ICT-related literacy skills into English Language Arts classrooms.Widespread social changes related to ICT are impacting the literacy practices of people in everyday society.These changes in literacy practices represent challenges to teachers who have never used these literacy skills and have never seen them taught.Through two case studies, this hermeneutic phenomenological inquiry examines the experiences of two teachers who learn and lead learning using wikis.Data collection was conducted in two Grade 6 classrooms: one in an Ontario public school and one in a private international school in Mexico and consisted of observations, informal and formal interviews with the teachers as well as observations and focus group interviews with their students.The complex phenomenon of learning ICT and almost simultaneously having to teach it is documented and analyzed.The research involved teaching the teachers how to use a wiki and then co-planning and observing them teach a creative writing unit on the wiki.Data was I would like to thank my supervisor and good friend, Rebecca Luce-Kapler.You have provided unwavering support and phenomenal mentorship since our first meeting over 10 years ago.My sincere thanks for all of the inspiration, pep talks, probing questions, laughs and good times

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.231
Threshold uncertainty score0.975

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.020
GPT teacher head0.279
Teacher spread0.259 · 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

Citations1
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueQSpace (Queen's University Library)Same topicEducation and Technology IntegrationFrench-language works237,207