Towards a Comprehensive Framework of Motivation to Learn: a Validation Study
Bibliographic record
Abstract
Motivation has been an important construct in second language acquisition and received extensive attention on how it affects learning and performance. The aims of this current review paper are multifaceted. 1) It aims to provide a comprehensive overview of the major motivation theories in the past decades. 2) Motivation theories specific to second/foreign learning context are to be included and explain the current state of the different domains of motivation theories. 3) It provides a critical evaluation of the rich body of motivation theories. 4) it also gives directions to propose a framework for motivation to learn a second/foreign language based on the major theories and approaches developed in the past. With this proposed comprehensive framework for motivation to learn a second/foreign language, it is hoped that a fuller picture of how different aspects and factors can be of significance to a learner’s motivation to learn. For educators, this framework can shed light on the pathways to effective teaching and learning by understanding what affects a learner’s motivation to learn English. For researchers, this paper would like to offer a future research direction for effectuation, validation, and modification of this proposed comprehensive framework.
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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 | 0.054 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".