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Record W3212839575 · doi:10.5539/elt.v14n12p55

Parents’ Involvement in High-Stakes Language Assessment A Review of Test Impact on Parent Behavior

2021· review· en· W3212839575 on OpenAlexvenueno aff
Jing Zhang

Bibliographic record

VenueEnglish Language Teaching · 2021
Typereview
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyContext (archaeology)Test (biology)Developmental psychologySocial psychologyMedical educationPedagogyMedicine

Abstract

fetched live from OpenAlex

This paper reviews a total of 20 empirical research studies concerning parents’ behavior under the context of high-stakes language assessment, aiming to reveal the impact of the assessment on parents’ behavior. The results show that (1) parents are typically involved in high-stakes language assessment process; (2) their involvement practice includes general (such as hiring tutors for children) and extreme involvement behavior (such as participating in movement against high-stakes testing); (3) no unanimous conclusion is reached concerning the effectiveness of parents’ involvement in high-stakes language assessment; (4) multiple factors that affect parents’ involvement in high-stakes language assessment are identified, including parents’ perceptions of tests, their educational background, and the time they spend with their children. This study concludes that tests might influence the ways parents are involved in children’s education. However, not all parents might be influenced by testing, and testing might have a positive impact on some parents but a negative impact on others. This synthesis has several practical implications. Firstly, it indicates that parents’ involvement behavior in the context of high-stakes language assessment deserves to be further investigated. Secondly, it points that various intervention programs should be provided for parents to help them better support their children’s learning and test preparation. The paper also offers several suggestions for future research.  

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.760
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.070
GPT teacher head0.453
Teacher spread0.383 · 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 designOther design
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

Citations2
Published2021
Admission routes1
Has abstractyes

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