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Record W4224290853 · doi:10.1111/lang.12499

Incidental Learning of Collocations in an Academic Lecture Through Different Input Modes

2022· article· en· W4224290853 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

VenueLanguage Learning · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicSubtitles and Audiovisual Media
Canadian institutionsWestern University
Fundersnot available
KeywordsPsychologyElaborationVocabularyActive listeningReading (process)Reading comprehensionLinguisticsListening comprehensionNonverbal communicationControl (management)Vocabulary developmentCognitive psychologyMathematics educationTeaching methodCommunicationComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract In this quasi‐experimental study, 165 learners of English for academic purposes at a university in China were randomly assigned to five experimental groups and a control group. Each experimental group encountered 19 target collocations in the same academic lecture in one of the following input modes: (a) reading, (b) listening, (c) reading while listening, (d) viewing, and (e) viewing with captions. The control group did not receive any treatment. The results revealed that reading, viewing, and viewing with captions led to learning at the form recognition level, but no significant differences were found in the learning gains across these modes. Nonverbal elaboration, type of vocabulary, and type of verbal elaboration affected learning, but frequency of occurrence, strength of association, comprehension, and prior knowledge of general vocabulary did not. This study provides further evidence supporting the use of academic lectures for incidental learning of collocations as well as expanding on the multimedia learning theory.

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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.029
GPT teacher head0.288
Teacher spread0.259 · 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