MétaCan
Menu
Back to cohort
Record W4410358712 · doi:10.1080/29984475.2025.2501997

Protocol for a Systematic Review of the Impact of Test Preparation Practices on L2 Test Performance and Language Proficiency

2025· review· en· W4410358712 on OpenAlexaff
Laura Stansfield, Anne-Marie Sénécal, Shanshan He, Ruslan Suvorov

Bibliographic record

VenueResearch Synthesis in Applied Linguistics · 2025
Typereview
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsUniversité de SherbrookeWestern University
Fundersnot available
KeywordsProtocol (science)Test (biology)Computer scienceTest preparationMedicineEngineeringBiologyManufacturing engineering

Abstract

fetched live from OpenAlex

Every year, millions of second language (L2) learners worldwide invest substantial time and resources in preparing for high-stakes language proficiency tests, the outcomes of which significantly influence crucial decisions regarding their educational pursuits, career opportunities, and immigration prospects. Learners’ performance on such tests is intrinsically linked to their test preparation practices which are affected by various factors; however, the efficacy of these practices remains a subject of debate and requires a systematic investigation. To examine the characteristics of test preparation influencing its effectiveness and to determine the extent to which test preparation practices affect L2 learners’ language test performance and language proficiency, we will conduct a systematic review of 24 primary studies that have investigated these issues. Guided by four review questions, this systematic review will follow the PRISMA guidelines to provide a comprehensive examination of the research methodologies and key findings of the primary studies. By carrying out this investigation, we hope to offer valuable insights that can potentially inform more effective test preparation practices, test design considerations, and future directions for research in this domain.

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)0.009
metaresearch head score (Gemma)0.211
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.356
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.211
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.164
GPT teacher head0.584
Teacher spread0.421 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueResearch Synthesis in Applied LinguisticsSame topicStudent Assessment and FeedbackFrench-language works237,207