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Record W2933673437

Grade 11 Students' Strategies for Collaboratively Writing from Online Sources of Information

2019· article· en· W2933673437 on OpenAlexaff
Lori Kirkpatrick, Michelle Searle, Rachael Smythe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsQueen's UniversityWestern University
Fundersnot available
KeywordsPrewritingPresentation (obstetrics)Computer scienceSpellProduct (mathematics)World Wide WebVariety (cybernetics)Section (typography)MultimediaMathematics educationPsychologyTeaching methodCooperative learningArtificial intelligenceSociology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study is to identify the strategies used by Grade 11 students to collaboratively create an opinion piece based on online sources of information. Using either iPads or Chromebooks, 22 grade 11 students worked in pairs to research a topic online and create a persuasive piece. They could choose what type of persuasive product to create, for example, slides, an essay, a brochure, or a website. As students worked, their voices and computer screens were recorded using Screencastify or Camtasia. Students’ written products were captured on the screen recordings and their final products were also collected. Initial analyses show that student pairs used a variety of strategies for prewriting (e.g., taking notes collaboratively in a Google Doc), writing (e.g., each student writing one section), and revising (e.g., using a spell checker), with the majority of time spent on the creation of content. In addition, students used strategies for locating and using source material (e.g., searching online by keyword). In some pairs, each student wrote a section and then they spliced their sections together. In others, content was created more collaboratively. Analysis is ongoing; the presentation will provide detailed descriptions of students’ strategy use.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.181

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.320
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 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
Published2019
Admission routes1
Has abstractyes

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