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Record W2115056161 · doi:10.5539/jedp.v1n1p162

A Qualitative Analysis on the Occurrence of Learned Helplessness among EFL Students

2011· article· en· W2115056161 on OpenAlexvenueno aff
Liwei Hsu

Bibliographic record

VenueJournal of Educational and Developmental Psychology · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyNeuroticismOpenness to experienceLearned helplessnessExtraversion and introversionBig Five personality traitsAgreeablenessPersonalitySocial psychologyAmotivationTraitIntrinsic motivationDevelopmental psychology

Abstract

fetched live from OpenAlex

This study seeks to provide some comprehensive information on EFL students’ learned helplessness whenlearning English, by exploring the causal relationship between three variables: failure to learn English,personality traits and intrinsic/extrinsic motivation. Eighteen students and two English teachers participated inthis study with a gatekeeper’s consent. This was done mainly through focus group interviews. The data waselicited from the NEO-FI Personality Trait Inventory with a cross analysis done on the in-depth interviews. Thefindings indicate that students with different personality traits do regard their failure to learn English in differentways. This in turn, leads to various influences that decrease their intrinsic motivation. The intrinsic motivationfor all of the participants is inevitably affected by failure, but learners with a neuroticism trait are the mostsensitive to failure. Therefore, these are the students who are most susceptible to experiencing learnedhelplessness when learning English, while students with traits of openness, extraversion and agreeableness canreactivate their extrinsic motivation when appealed to by external incentives. However, this study alsodiscovered that there was a mismatch between a teacher’s judgment on student amotivation and the student’sself-assessment of his/her motivation to learn English.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.604
Threshold uncertainty score0.997

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.194
GPT teacher head0.442
Teacher spread0.247 · 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

Citations9
Published2011
Admission routes1
Has abstractyes

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