MétaCan
Menu
Back to cohort
Record W2104092063 · doi:10.5539/ijel.v4n3p59

The Effect of Evaluation Factor on the Incidental Vocabulary Acquisition through Reading

2014· article· en· W2104092063 on OpenAlexvenueno aff
Chao Wang, Kun Xu, Zuo Ying

Bibliographic record

VenueInternational Journal of English Linguistics · 2014
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsVocabularyReading (process)Task (project management)BeijingVariance (accounting)PsychologyFactor (programming language)Mathematics educationComputer scienceIncidental learningVocabulary learningCognitive psychologyChinaLinguisticsEngineering

Abstract

fetched live from OpenAlex

This empirical study investigates the respective effectiveness of three factors (need, search and evaluation) included in task-induced involvement load on the EFL vocabulary learning and retention. Three tasks with the same amount of involvement load but containing different factors are assigned to 108 non-English majors at Beijing Institute of Petrol-chemical Technology in China. After these reading tasks, the participants are given an unannounced immediate posttest. One week later, the participants are given the delayed posttest. A 3 × 2 analysis of variance (ANOVA) is employed to process the scores and identify the relationship between the EFL incidental vocabulary learning and the three factors contained in the involvement loads. The results are assumed to show that the Evaluation factor is more decisive and crucial than the other two factors (need and search). Learners benefit more by using the target words in their original contexts. That means vocabulary instruction should focus on tasks that require high degrees of evaluation.

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)0.002
metaresearch head score (Gemma)0.061
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.061
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.343
Teacher spread0.327 · 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.

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

Citations5
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of English LinguisticsSame topicSecond Language Acquisition and LearningFrench-language works237,207