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Record W1660205459 · doi:10.21432/t28g6k

Screen capture technology: A digital window into students' writing processes / Technologie de capture d’écran: une fenêtre numérique sur le processus d’écriture des étudiants

2013· article· en· W1660205459 on OpenAlexaffvenue
Jérémie Séror

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsDigital literacyHumanitiesDigital humanitiesLiteracyComputer scienceRhetoricArtSociologyLinguisticsPhilosophyWorld Wide WebPedagogy

Abstract

fetched live from OpenAlex

Technological innovations and the prevalence of the computer as a means of producing and engaging with texts have dramatically transformed the ways in which literacy is defined and developed in modern society. Concurrently, this rise in digital writing practices has led to a growing number of tools and methods that can be used to explore second language (L2) writers’ writing development. This paper provides an overview of one such technique: the contributions of screen capture technology as a means of analyzing writers' composition processes. This paper emphasizes the unique advantages of being able to unobtrusively gather, store and replay what have traditionally remained hidden sequences of events at the heart of L2 writers' text production. Drawing on research data from case studies of university L2 writers, findings underscore the contribution screen capture technology can make to writing theory's understanding of the complex series of behaviours and strategies at the heart of L2 writers' interactions. Les innovations technologiques et la prévalence de l'ordinateur comme moyen de produire et d’interagir avec les textes ont radicalement transformé la façon dont la littératie est définie et développée dans la société moderne. Cette augmentation des pratiques d'écriture numérique a généré un nombre croissant d'outils et de méthodes disponibles pour explorer le développement de l'écriture dans une langue seconde (L2). Cet article donne un aperçu de l’une de ces techniques: les contributions offertes par la technologie de capture d'écran en tant que moyen d’analyse des processus d’écriture. L’article met l'accent sur les avantages incomparables qu’offre la possibilité de recueillir discrètement, de conserver et de revoir ce qui normalement reste une suite d'événements cachés au cœur du processus d’écriture dans une langue seconde. S'appuyant sur des données de recherche issues d’études de cas d’étudiants en L2 de niveau universitaire, les résultats mettent en lumière la contribution de la technologie de capture d'écran à la compréhension théorique de séries complexes de comportements et de stratégies situées au cœur des interactions des étudiants de L2 en contexte d’écriture.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0090.008
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations26
Published2013
Admission routes2
Has abstractyes

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