Causal attribution for success in English learning: students` self-assessment
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.725 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".