The Factors Affecting Learning Achievement in English Language Studies of The Students at North Bangkok University
Bibliographic record
Abstract
Background and Aims: Research emphasizes the critical role that learners' attitudes and behaviors play in determining their success in acquiring the English language, emphasizing the relationship that exists between mindset and actions. Achieving proficiency objectives and optimizing language learning outcomes requires a focus on the development of constructive attitudes and efficient learning behaviors. Thus, the study of the factors affecting learning achievement in English language studies of the students at North Bangkok University aims to investigate the factors affecting learning achievement in English language studies of the students at North Bangkok University. Methodology: The sample comprises students enrolled in the ENG 101 course, totaling 283 individuals, selected through stratified random sampling and simple random sampling methods. The research methodology involves utilizing a questionnaire as the primary data collection instrument. Statistical analyses employed in the study include percentages, means, standard deviations, Pearson correlation coefficients, and stepwise multiple regression analysis. Results: The results found that the factors of students, teachers, family, and school can collectively predict the factors affecting learning achievement in English language studies of students at North Bangkok University. This predictive set comprises the factors related to students (x1), teachers (x2), and family (x3). These predictor variables can predict the factors that affect the learning achievement in English language studies of the students at North Bangkok University with an accuracy of 42.77%. This can be written as a prediction equation as follows; (1) The prediction equation in raw score format can be expressed as follows: Y = .386+.282X1 +.174X2 + .151X3. (2) The prediction equation in standardized score format can be expressed as follows: ZY = .341X1 +.228X2 + .163X3 Conclusion: According to the study, family dynamics, instructors, and students all have an impact on how well students learn English at North Bangkok University. The accuracy of these predictor variables, which include factors related to students, teachers, and families, in predicting learning outcomes in English language studies is 42.77%.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".