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Record W2155923365

Argumentation in web-based collaborative inquiry learning: scripts for writing and scripts for talking aren't the same

2008· article· en· W2155923365 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

VenueInternational Conference of Learning Sciences · 2008
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsInstitute for Christian Studies
Fundersnot available
KeywordsScripting languageArgumentation theoryStructuringComputer scienceWeb applicationQuality (philosophy)World Wide WebMultimediaPsychologyLinguisticsEpistemologyProgramming language
DOInot available

Abstract

fetched live from OpenAlex

We use the script concept to describe knowledge structures that help individuals understand and act in specific contexts as well as scaffolds structuring collaborative learning. External scripts can be presented in different ways, e.g. as written text. For internal scripts, it is not clear whether they have identical effects on collaborative argumentation processes in oral vs. written discussions. We empirically investigated the effects of two differently structured external scripts on the structural quality of written and oral arguments produced in dyads with either low or high structured internal scripts. External scripts were presented in a written mode at specific instances in a web-based inquiry learning environment. Results indicate that the high structured external script strongly improved the structural quality of the written arguments, but had hardly any effects on orally produced arguments, which were instead more strongly influenced by the learners' internal scripts.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.183
GPT teacher head0.438
Teacher spread0.255 · 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