MétaCan
Menu
Back to cohort
Record W1549804090 · doi:10.1109/bwcca.2014.98

A Language-Learning Support System with a Handwriting-Based Communication Interface

2014· article· en· W1549804090 on OpenAlexaff
Shu Li, Masato Kasahara, Kosuke Takano, Kin Fun Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicMobile Learning in Education
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsComputer scienceWhiteboardHandwritingScalable Vector GraphicsMultimediaInterface (matter)Class (philosophy)ScalabilityGraphicsHuman–computer interactionWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we present a language-learning support system using an interactive whiteboard on a mobile touch device. Using this system, a teacher and learner participate in an online language class by sharing voice and handwriting information. Each lesson is recorded as movie-like content in a cloud server, and the learner can review the lesson immediately or later by re-playing the recorded lesson content. In this study, we incorporate the scalable vector graphics (SVG) format to represent handwritten information for sharing during the online lesson. We also examine the basic network performance of our method using a prototype system with several experiments.

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 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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.885
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.006
GPT teacher head0.243
Teacher spread0.237 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations2
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicMobile Learning in EducationFrench-language works237,207