Is There A Relationship Between Motivational Components in Foreign Language Learning?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it