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Record W2189287958 · doi:10.7146/hjlcb.v16i31.25735

Using WebCT to teach grammar selectively

2017· article· en· W2189287958 on OpenAlexafffund
Kathleen Vance

Bibliographic record

VenueHERMES - Journal of Language and Communication in Business · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsBritish Columbia Institute of Technology
FundersBritish Columbia Institute of Technology
KeywordsGrammarComputer scienceComponent (thermodynamics)Mathematics educationMultimediaPsychologyLinguistics

Abstract

fetched live from OpenAlex

Teachers of business and technical communication are supposed to teach grammar, but only to a limited extent, according to the literature. Technology program faculty at the British Columbia Institute of Technology and employers of graduates want grammar to be taught, along with an ever-expanding list of other employment-related communication skills. In response to these demands, a series of eight mini grammar lessons was developed for students in four technology programs. The software WebCT was used to facilitate the development and delivery of the lessons, which formed a component of the students’ business and technical communication course. Exercises, self-tests, and quizzes used sentences from workplace documents from the students’ technologies in order to hold the students’ attention and to validate the study of language. For students, this online component proved to be an attractive feature of their course.

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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.535

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 studies0.0010.000
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 head0.061
GPT teacher head0.329
Teacher spread0.269 · 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 designQualitative
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

Citations2
Published2017
Admission routes2
Has abstractyes

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