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Record W2074503693 · doi:10.1080/19388070609558452

Literature‐based collaborative internet projects in elementary classrooms

2006· article· en· W2074503693 on OpenAlexaboutno aff
Rachel Karchmer‐Klein, Victoria Layton

Bibliographic record

VenueReading Research and Instruction · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicLiteracy, Media, and Education
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetCurriculumPsychologyMathematics educationLiteracyCollaborative learningPedagogyWorld Wide WebComputer science

Abstract

fetched live from OpenAlex

Abstract The purpose of this study was to examine teachers' use of literature‐based collaborative Internet projects (CIP) in their elementary classrooms. These practices require two or more classrooms to read and analyze texts on specified topics and then share responses over the Internet. The participants, all female, represented 15 different U.S. states as well as Canada and Australia. Three types of data were collected and analyzed including electronic surveys, semi‐structured email interviews, and project websites. Results indicated that teachers' pedagogical beliefs led to the introduction of CIP. Specifically, teachers reported the projects provided opportunities to foster learning by helping students (1) make connections between new content and their background knowledge, (2) actively participate in their own learning, and (3) recognize and appreciate differences among their peers. Teachers also reported CIP supported literacy curriculum standards. Finally, data indicated differences in how CIP were implemented across grade levels. In light of the study's results, four lessons are shared along with educational and research implications.

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 score0.454
Threshold uncertainty score0.433

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 head0.049
GPT teacher head0.312
Teacher spread0.263 · 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

Citations8
Published2006
Admission routes1
Has abstractyes

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