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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 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.
venuePublished in a venue whose home country is Canada.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.900
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
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 head0.011
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