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Record W1990800148 · doi:10.22318/cscl2009.2.162

Automating the analysis of collaborative discourse: identifying idea clusters

2009· article· en· W1990800148 on OpenAlex
Nobuko Fujita, Christopher Teplovs

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

VenueScholarship at UWindsor (University of Windsor) · 2009
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceComplement (music)Latent semantic analysisVisualizationQualitative analysisData visualizationQuantitative analysis (chemistry)Data scienceSemantic analysis (machine learning)Discourse analysisScale (ratio)Information retrievalNatural language processingQualitative researchData miningLinguisticsSociology

Abstract

fetched live from OpenAlex

This design-based research study investigated instructional scaffolding for knowledge building discourse among participants (n=17, n=20) in two online graduate courses. In particular, designs of software-based scaffolding as found in web-based Knowledge Forum's scaffold support feature were refined. Analyses of the student discourse data suggests that Knowledge Forum's scaffold supports offer a promising avenue for future design innovations to encourage knowledge building discourse. Results show that students increasingly used the scaffolds to focus their reading and writing of notes over iterations of the study. The proportion of scaffolds for knowledge building discourse increased during each iteration with a corresponding decrease in the proportion of scaffolds for expressing an opinion in the second iteration. Finally, notes with scaffolds contained significantly more words than notes without scaffolds, suggesting that scaffolds promoted more student reflectivity. Implications for formative assessment of student learning and knowledge building are discussed.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.360
Teacher spread0.319 · 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