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Record W7064583956

Causal attribution for success in English learning: students` self-assessment

2019· dissertation· en· W7064583956 on OpenAlexaboutno aff

Bibliographic record

VenueUniversitas Sanata Dharma Repository (Universitas Sanata Dharma) · 2019
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicMagnetic confinement fusion research
Canadian institutionsnot available
Fundersnot available
KeywordsAttributionStatisticCausality (physics)Qualitative propertyIndonesianCausal modelQualitative research
DOInot available

Abstract

fetched live from OpenAlex

Evaluating learning achievement can be a solution to increase students' academic performance.In the process of learning, students sometimes encounter difficulties to find precise reasons why they face their current learning performance.This makes students unable to find some aspects they need to improve.Nevertheless, some researchers in learning attribution have formulated some causal attributional factors.The attributional theorist have formulated some detailed factors or aspects that students can assess easily by themselves.This research aims to investigate students' causal attributional factors, and also reveal the relationship of students' self-efficacy toward causal attribution in English learning.Attribution dimensional classification by Vispoel & Austin (1995), and selfefficacy theories from Bandura (1997) were used as the grand theories of this research.There are some previous studies about causal attribution and self-efficacy, which employed quantitative study from various countries, such as those in Korea, Spain, and Canada.This survey research employed mixed-method to discover the phenomena from the deeper lenses of Indonesian students' attribution in English learning.Both descriptive statistic (quantitative) and descriptive qualitative methods were mixed and used as the data analysis.The quantitative data was gathered from 6-likert-scale questionnaire.The quantitative data is required for observing the condition of the class.Besides, the qualitative data was gathered from open-ended questionnaire and interview.The qualitative data is needed to discover the deeper truth behind the numbers.The participants were 35 ELESP students of Pertiwi University (a pseudonym).The result of this research demonstrate that family influence becomes the highest causal attributional factor (5.05).It happens because the participants receive major influence from parents, such as receiving motivation, support (material and immaterial), and sharing the same visions.In addition, attribution and self-efficacy influence students' GPA by 15%.It means, although attribution and self-efficacy do not give the major influence, but those help students to perform better learning.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.7250.000

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 head0.007
GPT teacher head0.280
Teacher spread0.272 · 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 designQualitative
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

Citations0
Published2019
Admission routes1
Has abstractyes

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