Third Language Learning: Insights from MA Students Through the L2 Motivational Self-System & Attribution Theory Lenses
Bibliographic record
Abstract
This qualitative study uses a semi-structured interview to investigate why Saudi learners stop learning a third language and whether these reasons are permanent or temporary. The participants were six female master’s degree students who had experience learning a third language outside of formal education or informal settings. This study identifies the most popular foreign languages learned as a third language (L3) by female postgraduate students in Saudi Arabia. It examines their attitudes and motivation towards learning these languages, explores the reasons why they stop learning them, and draws implications for foreign language teaching and learning in Saudi Arabia. The findings indicated that most participants who stopped learning had temporary reasons, such as lack of time and being busy with work or life responsibilities, even though they had the motivation to learn at the beginning. The study also revealed the profound influence of social media and the internet on the participants’ learning process, underscoring the role of technology in foreign language learning.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".