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Record W2117805383 · doi:10.14746/ssllt.2011.1.1.6

Second language writing anxiety, computer anxiety, and performance in a classroom versus a web-based environment

2011· article· en· W2117805383 on OpenAlexaff
Effie Dracopoulos, François Pichette

Bibliographic record

VenueStudies in Second Language Learning and Teaching · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicGender and Technology in Education
Canadian institutionsUniversité TÉLUQUniversité du Québec à Montréal
Fundersnot available
KeywordsAnxietyGrammarPsychologySecond language writingLanguage acquisitionDistance educationComputer-Assisted InstructionMathematics educationLanguage proficiencySecond languageLinguistics

Abstract

fetched live from OpenAlex

This study examined the impact of writing anxiety and computer anxiety on language learning for 45 ESL adult learners enrolled in an English grammar and writing course. Two sections of the course were offered in a traditional classroom setting whereas two others were given in a hybrid form that involved distance learning. Contrary to previous research, writing anxiety showed no correlation with learning performance, whereas computer anxiety only yielded a positive correlation with performance in the case of classroom learners. There were no significant differences across learning environments on any measures. These observations are discussed in light of the role computer technologies now play in our society as well as the merging of socio-demographic profiles between classroom and distance learners. Our data suggest that comparisons of profiles between classroom and distance learners may not be an issue worth investigating anymore in language studies, at least in developed countries.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.305
Teacher spread0.280 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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