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Record W2793474205 · doi:10.18806/tesl.v34i3.1277

Improving English Learners’ Productive Collocation Knowledge: The Effects of Involvement Load, Spacing, and Intentionality

2018· article· en· W2793474205 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueTESL Canada Journal · 2018
Typearticle
Languageen
FieldPsychology
TopicSecond Language Acquisition and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyIntentionalityCollocation (remote sensing)VerbOperationalizationCognitive loadLinguisticsCognitionCognitive psychologyComputer science

Abstract

fetched live from OpenAlex

This article reports on a classroom-based experiment that tested the effects of threevocabulary teaching constructs (involvement load, spacing, and intentionality)on the learning of English verb-noun collocations—for example, “shelve a plan.”Laufer and Hulstijn’s (2001) “involvement load” predicts that the higher themotivational-cognitive load of a task, the more effectively it promotes word retention.“Spacing” refers to the advantage of spreading out learning opportunitiesfor words as opposed to massing them. “Intentionality” comprises two word processingmodes: intentional learning (posttest announced) and incidental learning(posttest unannounced), where the former is claimed to outperform the latter.The constructs were integrated into an intervention study with 59 adolescent L1Swedish learners of English in within- and between-subjects designs. Learnersprocessed target items three times when performing tasks that operationalized theconstructs. Three posttests of productive knowledge of target items were administered.Statistical analyses of gain scores show that neither involvement load norspacing had a significant positive impact on learning gains. Significant effectswere found on three measures for intentional learning when compared to incidentallearning. The findings are discussed in relation to previous research and theirimplications for English language teaching (ELT).Cet article rend compte d’une expérience basée en salle de classe et qui évalueles effets de trois concepts pédagogiques en enseignement du vocabulaire (charged’implication, espacement et intentionnalité) sur l’apprentissage d’expressions figéesen anglais consistant d’un verbe et d’un nom. La « charge d’implication » deLaufer et Hulstijn (2001) prédit que plus la charge motivationnelle et cognitived’une tâche est élevée, plus elle favorise la rétention des mots. « L’espacement »fait référence à l’avantage de répartir les occasions d’apprentissage des mots plutôtque de les rassembler. « L’intentionnalité » comprend deux modes de traitementdes mots: l’apprentissage intentionnel (post-test annoncé) et l’apprentissage accidentel(post-test pas annoncé), le premier supposément donnant de meilleursrésultats que le second. Ces concepts ont été intégrés dans une étude d’interventionimpliquant 59 adolescents suédois qui apprenaient l’anglais et suivant un modèle àmesures répétées et un modèle inter-sujets. Les participants ont traité des élémentscibles trois fois en accomplissant des tâches qui opérationnalisaient les concepts.Trois post-test portant sur des connaissances productives des éléments cibles ont eulieu. Des analyses statistiques des notes ont indiqué que ni la charge d’implicationni l’espacement n’avaient eu un impact positif significatif sur les notes. Trois mesures de l’apprentissage intentionnel comparé à l’apprentissage accidentel ont révélé des effets significatifs. Nous discutons des résultats par rapport à la recherche antérieure et en fonction de leurs incidences sur l’enseignement de l’anglais.

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 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 score0.373
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0180.000

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 head0.009
GPT teacher head0.263
Teacher spread0.254 · 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