Enhancing Intrinsic Motivation to Learn in Adults: Empowering ESL Students
Bibliographic record
Abstract
The apex in the acquisition of a second language is dependent upon motivation and attitude coupled and nurtured in a formal language learning milieu. Adult learners of English as a Second Language (ESL) can be motivated intrinsically in numerous ways. The types of motivation, whether intrinsic or extrinsic, are not categorically different; instead, they lie in the same transition of self-determination. Direction relative to one’s surroundings serves as a long-range goal and sustains students’ motivation to learn a second language. The decision to learn in many cases comes from within. Internalizing the reasons for language learning are internalized, students feel more insistency, comfort, and ease. Teachers should strive to promote positive self-talk and guided self-evaluation within students, along with emphasizing mastery of specific goals. Setting higher but attainable goals for an ESL learner activates endeavors and influences performance. Instructors should reveal intrinsic attractions latent in every subject as the appeal-laden lessons have a high quality to attract most students. Knowing and assimilating a target language’s culture contributes positively to a learner’s development. An ESL classroom should be a welcoming, positive place where communication needs are met and where language anxiety is minimum, and the student seems to enjoy many of the practice opportunities. Instructors should create a stimulating, supportive ambiance that fosters self-esteem and interest in learning. Repeated readings in small groups develop expertise in verbal skills. Further, it encourages curiosity, which is of paramount significance for creating motivation and making ESL learning fun. Present research will be a harbinger of further research in Middle Eastern educational milieu and will open new vistas for academicians and practitioners involved in teaching as a foreign language.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".