The Relationship between Senior High School Students’ Personality Traits and English Academic Achievement: The Mediating Effects of Directed Motivational Currents and Academic Emotions
Bibliographic record
Abstract
As a stable individual difference factor, personality traits significantly influence English achievement. Directed Motivational Currents, an emerging concept in L2 motivation research, are not only affected by personality traits but also closely associated with related academic emotions. However, few studies have explored the complex interactions among personality traits, DMC, academic emotions, and English achievement. Based on Whole-Trait Theory, Flow Theory, and Control-Value Theory, this study examined the relationships among these variables through questionnaires and interviews with 419 second-year high school students in Liaoning Province, China. The research addressed three questions: 1) What are the current levels of senior high school students’ personality traits, DMC, and academic emotions? 2) How are personality traits, DMC, academic emotions, and English achievement interrelated? 3) Do DMC and academic emotions mediate the relationship between personality traits and English achievement? If so, what types of mediating effects do they have? The findings indicate that: 1) Students showed moderate levels of personality traits, DMC, and enjoyment; anxiety was relatively high, while boredom was low. 2) Personality Traits were positively correlated with DMC, English achievement, and positive emotions. DMC was positively correlated with enjoyment, and English achievement, but negatively correlated with anxiety and boredom. Positive emotions were positively correlated with English achievement and negatively correlated with negative emotions. 3) Both academic emotions and DMC served as independent mediators between personality traits and English achievement, and also formed a chain-mediating effect. The mediating effect is 79.48%. Pedagogical implications for senior high school English teaching are discussed, along with research limitations and future directions.
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How this classification was reachedexpand
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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.008 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".