Relations Among L2 Learning Motivation, Language Learning Anxiety, Self-efficacy and Family Influence: A Structual Equation Model
Bibliographic record
Abstract
The L2 Motivational Self System (L2MSS) has been widely researched and used to explain L2 learners’ motivational behaviors. However, important factors such as language learning anxiety, self-efficay and possible family influence need to be scrutizized in relation to L2MSS in order to expand our understanding of the second language learning process. This study used a structural eauation modeling approach to test a hypothesized model that contained the L2 Motivational Self System, family influence, L2 learning anxiety, and self-efficacy as the latent variables in a context of foreign language learning. A total of 473 Taiwanese high school students participated in the study. With Amos version 22.0, the current study analyzed the proposed model. The results assured the validity of the hyposized model among these students. This model revealed that family influence played a significant role in affecting learners’ future self guides. Self guides served as a good predictor for the L2 learners’ self-efficacy. In addition, the ought-to L2 self contributed to a higher level of anxiety while the ideal L2 self and L2 learning experience both lowered the level of English learning anxiety.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".