The Role of Implicit Theories of Intelligence and Need for Cognition in Second Language Acquisition
Bibliographic record
Abstract
Theorists and researchers working in the area of second language (L2) learning and motivation have recently begun to consider the relevance of variables from other areas of general and educational psychology to the language learning context (for a review, see Dornyei, 1998). The purpose of this research project was to test a causal model of the relations among a number of individual difference variables (implicit theories of intelligence, beliefs about language aptitude, and need for cognition), self-determination, language learning strategy use, and L2 proficiency. Student volunteers (N= 126) enrolled in language courses (French, German, and Spanish) at the University of Saskatchewan completed a questionnaire assessing the above constructs. Results of structural equation modeling analyses showed that (1) need for cognition was positively related to both self-determination and cognitive strategies, (2) self-determination positively predicted cognitive, compensation, metacognitive, and social strategies, and (3) social strategies were positively related to self-ratings of L2 competence. (Self-ratings of L2 proficiency were substituted for objective ratings of achievement in the model due to an insufficient number of available final course grades (n = 65)). Post hoc modification of the model included adding a correlation between implicit theories of intelligence and need for cognition. A hierarchical regression analysis was used to examine the relations between final course grades and the other study variables. Results showed that cognitive, affective, and metacognitive strategies functioned as suppressor variables. Specifically, cognitive strategies showed a stronger positive relation with achievement when affective and metacognitive strategies were partialed out, and both affective and metacognitive strategies showed a negative relation with achievement when cognitive strategies were partialed out. The results show that integrating constructs from different motivational frameworks continues to enhance our understanding of both L2 motivation in particular and educational motivation in general, and that further research is necessary to clarify the relations between language learning strategies and L2 achievement.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".