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Record W2273713207 · doi:10.1558/lst.v2i2.25956

Diagnostic and Developmental Potentials of Dynamic Assessment for L2 writing

2015· article· en· W2273713207 on OpenAlex
Mohammad Rahimi, Ali Kushki, Hossein Nassaji

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

VenueLanguage and Sociocultural Theory · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational and Psychological Assessments
Canadian institutionsUniversity of VictoriaUniversité du Québec à Montréal
Fundersnot available
KeywordsDynamic assessmentPsychologyWriting assessmentComputer scienceSecond language writingMathematics educationMedical educationSecond languageLinguisticsMedicineDevelopmental psychology

Abstract

fetched live from OpenAlex

Many theoretical claims have been made about the role and effectiveness of Dynamic assessment (DA) in L2 learning. It has, for example, been suggested that this kind of approach provides learners with appropriate and timely feedback in a supportive and interactive environment and in ways that can maximize L2 development (Poehner & Lantolf, 2013). However, issues remain about how to measure the effects of DA and how these effects compare with those of traditional ways of providing feedback. This qualitative case study explores the role of interactional DA in the development of L2 writing skills. Three advanced EFL students each produced first 10 writing samples in ten individualized writing sessions. They then engaged in 10 collaborative tutorial sessions with their teacher and received feedback based on the DA principles. The interactions were recorded, transcribed, and analyzed. The results revealed important diagnostic and treatment effects for interactive DA.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.519
Threshold uncertainty score0.257

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 communication0.0000.000
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 head0.034
GPT teacher head0.399
Teacher spread0.365 · 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