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Record W4388198456 · doi:10.15406/ahoaj.2023.05.00206

Retrieval, repetition, and retention: unveiling vocabulary acquisition strategies for ESL learners

2023· article· en· W4388198456 on OpenAlexaff
Brian Strong

Bibliographic record

VenueArts & Humanities Open Access Journal · 2023
Typearticle
Languageen
FieldPsychology
TopicEducational Strategies and Epistemologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsVocabularyMemorizationComputer scienceMetacognitionRepetition (rhetorical device)Perspective (graphical)RecallMathematics educationMultimediaPsychologyArtificial intelligenceLinguisticsCognitive psychologyCognition

Abstract

fetched live from OpenAlex

This paper explores the practical implementation of retrieval practice techniques for improving English as a Second Language (ESL) vocabulary acquisition. Effective strategies for fostering long-term retention and comprehension of vocabulary are of utmost importance in the field of ESL education. Three key retrieval practice techniques, namely, The Brain Dump, Low-Stakes Quizzes, and Flashcards, were examined, and their advantages, drawbacks, and potential impact on ESL learners were discussed. Additionally, the integration of digital technology with traditional retrieval practice tools is discussed, highlighting the evolving landscape of language learning strategies. This discussion emphasizes the significance of retrieval practice in enhancing ESL vocabulary acquisition, offering educators and learners valuable tools to reinforce memory traces, engage in active recall, and promote metacognition. However, challenges, such as time constraints, test anxiety, and rote memorization, must be carefully considered in their implementation. Overall, this paper provides insights into the practical utilization of retrieval practice techniques, offering educators a nuanced perspective on improving ESL learners' long-term vocabulary retention and comprehension.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0060.003
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.269
GPT teacher head0.462
Teacher spread0.193 · 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 designTheoretical or conceptual
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

Citations3
Published2023
Admission routes1
Has abstractyes

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