Attitudes and Motivation toward Learning the English Language among Students from Islamic Education System Background: Exploring the Views of Teachers
Bibliographic record
Abstract
Research in the field of attitudes and motivation has increasingly investigated the nature and role of motivation in L2 learning process, and many are inspired by Canadian psychologists, Robert Gardner and Wallace Lambert [1]. However, in Malaysia, there has been only a meagre number of research that investigates teachers' perceptions on attitudes and motivation of students from religious school background. It is of great significance to explore the attitudes and motivation of these groups of students because the students appeared to be weak in the English language and they also held negative perceptions toward the language [2, 3]. The present study is needed to attain authentic information about possible connections between teachers' personal experiences, their perspectives and their practices regarding teaching and learning of students from the aforementioned background. This qualitative research study contains in-depth teacher interviews that document their personal perceptions, ways of dealing with students in the specified setting, and their suggestions on improving the attitudes and motivation of learning English for students from religious school background. Findings are presented according to the research questions intended for the study and several conclusions were drawn from the data.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".