Is There A Relationship Between Motivational Components in Foreign Language Learning?
Bibliographic record
Abstract
Motivation is the main determining factor to ensure successful achievement in foreign language acquisition. Ever since influential Canadian Psychologists, Gardner and Lambert introduced the term motivation in foreign and second language learning in 1959, numerous researchers investigated the nature of motivation, however, the area on relationships between motivational components is still understudied until now, especially in foreign language acquisition area. A random sampling of 131 participants from a public university in Malaysia responded to the survey. This quantitative study attempted to explore the relationships between motivational components, which have been identified as value components, expectancy components and affective components. Motivational scale by Pintrich & De Groot (1990) is used to compose the questionnaire, which examined the students’ motives in learning foreign language. The learners answered four sections, consist of Demographic Profile, Value Components, Expectancy Components and Affective Components by using a 5-point Likert scale survey. Analysis by using SPSS has been done to discover results in the form of mean scores and correlations scores. The findings revealed that the three motivational components have strong relations with students’ motivation. Additionally, the correlation analyses revealed interesting discoveries; Value and expectancy components showed favourable correlations, whereas there were negative correlations between expectancy and affective components and also between value and affective components. These findings are useful for teachers and curriculum writers since contribution of this study will clearly provide teachers and curriculum writers the foundation ideas to design and produce authentic lesson plans. The implementation of ideas from this study will motivate the students to learn foreign language skills based on value and expectancy components.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".