L2 Anxiety, Self-Regulatory Strategies, Self-Efficacy, Intended Effort and Academic Achievement: A Structural Equation Modeling Approach
Bibliographic record
Abstract
Factors that contribute to learning achievement have always been a primary research concern in the field of education. In the field of second/foreign language (L2) learning, researchers have been trying to explore many important factors that are linked to successful learning and how these factors may predict the success of language learning. With respect to the factors contributing to language proficiency, many researchers endeavor themselves to the exploration of assisting the learners. The present study aims to explore whether or not the following factors would influence learners’ academic achievement: the process of goal-setting, the L2 anxiety, the effort the learners put into, self-efficacy together with self-regulatory strategies. A total number of 356 senior high school students who were learning English as a Foreign Language participated in the study. A new questionnaire was developed to measure and collect the participants’ responses in respect to the above-mentioned learning factors. In order to investigate the relationships among these factors and the learners’ academic performance, the structural equation modeling (SEM) was used to identify the best fit model. It was found that self-efficacy, L2 anxiety, together with goal-setting processes, are prerequisites for the application of effective self-regulatory strategies, which in turn play an important role in affecting the intended efforts the learners make, and consequently influence the learners’ achievement. According to the findings, we suggest the teacher elevate the students’ self-efficacy, lower the L2 anxiety, help set their learning goals, cultivate their capability of employing strategies and increase their intended effort.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".