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Record W3151050678 · doi:10.29173/iasl7804

Collaborative Inquiry in Digital Information Environments

2021· article· en· W3151050678 on OpenAlexvenueno aff
Ross J. Todd, Punit Dadlani

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldHealth Professions
TopicDigital Storytelling and Education
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumTask (project management)Interpersonal communicationReading (process)Resource (disambiguation)The artsMathematics educationDynamics (music)PsychologyPedagogyComputer scienceEngineeringVisual arts

Abstract

fetched live from OpenAlex

This paper presents selected findings from current research being undertaken by the Center for International Scholarship in School Libraries (CISSL) at Rutgers University that examines the research and writing processes of high school students undertaking a group research task in a New Jersey High school library. The purpose of this task was for students to produce a co-constructed product that represents the group’s understanding of their chosen curriculum topic. The study involved 42 grade 9 students undertaking an accelerated English Language Arts curriculum unit focusing on examining a wide range of challenging literature in the genres of short story, novel, drama, nonfiction, and poetry. The course includes independent reading assignments, and stresses critical thinking and speaking skills, study skills, and research strategies. The learning environment was supported by a Wiki/ Google documents digital environment that tracked the group dynamics, student-to-student interactions, resource use patterns, and knowledge building processes, as well as classroom teacher and school librarian interactions with the students, as groups and as individuals. This paper reports specifically on cognitive, personal and interpersonal dynamics reported by students as they worked in groups.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.201
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.038
GPT teacher head0.346
Teacher spread0.308 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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