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Record W1549095265 · doi:10.18806/tesl.v30i1.1128

Show me! Enhanced Feedback Through Screencasting Technology

2013· article· en· W1549095265 on OpenAlexfundvenueno aff
Jérémie Séror

Bibliographic record

VenueTESL Canada Journal · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsThe InternetMultimediaComputer scienceHumanitiesSociologyWorld Wide WebPedagogyArt

Abstract

fetched live from OpenAlex

Technology is an ever-increasing part of how teachers and learners work on lan- guage and texts. Indeed, computers, the Internet, and Web 2.0 applications are revolutionizing how texts are consumed, discussed, and produced in classrooms. This article focuses on a specific technological innovation emerging from this dig- ital revolution: the use of screencasts and their potential to transform how feed- back can be offered to language-learners on written assignments. Drawing on a brief review of the literature and the author’s own experiences as a second-lan- guage writing teacher, this article presents an overview of screencasting technol- ogy and examines how freely available software can be used by teachers and students to produce and share asynchronously online video recordings of them- selves as they edit and comment on documents viewed on a computer screen. It is argued that at a time when teaching and learning is increasingly migrating to online digital contexts, screencasting represents a low-cost, intuitive, and time- saving interface the multimodal nature of which can counter limitations typically associated with more traditional feedback approaches.La technologie joue un rôle croissant dans le travail des enseignants et des étudiants portant sur la langue et les textes. En fait, les ordinateurs, l’Internet et les applica- tions Web 2.0 transforment la façon dont les textes sont employés, discutés et pro- duits dans les salles de classe. Cet article évoque une innovation technologique spécifique qui découle de cette révolution numérique : l’emploi de la vidéographie et son potentiel pour transformer la façon dont les apprenants de langue reçoivent de la rétroaction sur leurs travaux écrits. Puisant dans une brève analyse documentaire et dans les expériences personnelles de l’auteur comme enseignant d’écriture en langue seconde, cet article présente un aperçu de la technologie de la vidéographie et examine l’emploi que peuvent faire les enseignants et les étudiants de logiciels gra- tuits pour produire et partager en mode asynchrone des enregistrements vidéo en ligne d’eux-mêmes pendant qu’ils révisent et commentent des documents affichés sur un écran d’ordinateur. On fait valoir que pendant cette période où l’enseignement et l’apprentissage se déplacent de plus en plus vers des contextes numériques en ligne, la vidéographie représente une interface économique et intuitive qui permet d’économiser du temps et dont le caractère multimodal peut éliminer les contraintes souvent associées aux approches plus traditionnelles à la rétroaction.

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)0.002
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0500.011

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 head0.023
GPT teacher head0.213
Teacher spread0.190 · 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 designNot applicable
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

Citations63
Published2013
Admission routes2
Has abstractyes

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