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Record W4407368651 · doi:10.1111/lang.12705

How Do Different Forms of Note‐Taking Affect Second Language Vocabulary Learning?

2025· article· en· W4407368651 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLanguage Learning · 2025
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyAffect (linguistics)LinguisticsVocabularyVocabulary developmentLanguage acquisitionVocabulary learningCognitive psychologyMathematics educationCommunication

Abstract

fetched live from OpenAlex

Abstract The present study compared learning gains at both form recall and meaning recall levels across three learning conditions: viewing without note‐taking, viewing with conventional note‐taking, and viewing with guided note‐taking. A total of 134 Chinese learners of English were assigned to three experimental groups and a no‐treatment control group. Results showed that (a) guided note‐taking contributed to greater vocabulary learning than conventional note‐taking on the form recall test, (b) both guided and conventional note‐taking contributed to significant vocabulary gains on the meaning recall test, and (c) viewing without note‐taking did not contribute to significant learning gains. The analyses also revealed that writing unknown words in notes, the inclusion of target words in the lecture slides, and learners’ prior vocabulary knowledge affected learning, but frequency of occurrence, word length, and learners’ level of viewing comprehension did not.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0350.000

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 head0.007
GPT teacher head0.303
Teacher spread0.296 · 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