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Record W2584951375 · doi:10.5539/ies.v10n2p84

The Psychological Effect of Errors in Standardized Language Test Items on EFL Students’ Responses to the Following Item

2017· article· en· W2584951375 on OpenAlexvenueno aff
Saman Khaksefidi

Bibliographic record

VenueInternational Education Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicStudent Assessment and Feedback
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyTest (biology)Affect (linguistics)Social psychologyStratified samplingTest of English as a Foreign LanguageMathematics educationPersonalityLanguage assessmentStatistics

Abstract

fetched live from OpenAlex

This study investigates the psychological effect of a wrong question with wrong items on answering to the next question in a test of structure. Forty students selected through stratified random sampling are given 15 questions of a standardized test namely a TOEFL structure test in which questions number 7 and number 11are wrong and their answers to the next question is being analyzed. Three way ANOVA determines whether the total scores of the students, also their age, and gender affect the students’ responses to the upcoming question or it is just a psychological factor affecting students’ responses. The results showed that only 20 percent provided true answers; among them were only very proficient and proficient students. Age and gender were not considered significant factors in this regard. The correct answers were attributed to their full mastery over the structure and their personality type rather than the psychological factor. Most of the students were psychologically affected by a wrongly given item. Thus, it is suggested that test givers and teachers ignore the wrongly given question and also the upcoming one in order to prevent the psychological factor that makes students provide unsure and false answers to the question.

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)0.002
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.073
GPT teacher head0.547
Teacher spread0.474 · 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 designObservational
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

Citations1
Published2017
Admission routes1
Has abstractyes

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