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Record W3095919386 · doi:10.1111/modl.12671

How Effective Are Intentional Vocabulary‐Learning Activities? A Meta‐Analysis

2020· article· en· W3095919386 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueModern Language Journal · 2020
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
Fundersnot available
KeywordsRecallMeaning (existential)PsychologyModerationVocabularyWord (group theory)Vocabulary learningCognitive psychologyMeta-analysisLinguisticsSocial psychologyMedicine

Abstract

fetched live from OpenAlex

Abstract The present meta‐analysis aimed to summarize the extent to which second language vocabulary is learned from the most frequently researched word‐focused activities: flashcards, word lists, writing, and fill‐in‐the‐blanks. One hundred effect sizes from 22 studies were included in meta‐regression analyses and administered separately for the observations measured with meaning‐recall and form‐recall tests. The results revealed that the average percentage learning gains were 60.1% and 58.5% on meaning‐recall and form‐recall immediate posttests. These gains dropped to 39.4% and 25.1% on delayed meaning‐ and form‐recall tests, respectively. These results suggest that learning through word‐focused tasks is far from guaranteed. Moreover, the percentage learning gains among the different activities ranged from 18.4% to 77.0% on immediate posttests and from 23.9% to 73.4% on delayed posttests indicating that there is much variation in efficacy among the activities. Moderator analyses revealed that learners’ place of study and direction of learning affected learning.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0810.000

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 head0.029
GPT teacher head0.303
Teacher spread0.273 · 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