“Can we revive Arabic in an Arab country?” Tracing the Dynamic Motivational Trajectory of Non-Arab learners living in the United Arab Emirates
Bibliographic record
Abstract
This study takes on a cross-disciplinary approach and draws on concepts from Dörnyei's Second Language Motivational Self System (Dörnyei, 2005) and a complex dynamic system theory (Larsen-Freeman & Cameron, 2008a) to examine non-Arab learners' (n=100) experience and motivations in learning Arabic as a second language (L2) in the United Arab Emirates.Specifically, the investigation explores the immediate environment around the language learner to capture the evolving nature of L2 motivation and to trace the dynamic trajectory for existing language learning outcomes.The use of such rarely employed methodologies as concept mapping (Kane & Trochim, 2007) and retrodictive qualitative modeling have identified a motivational system (along with its causal mechanisms) shared by these learners and probed the voices of educators (n=25) to help explain the learners' views.The results also point to the varying roles of selfefficacy, coping, and early exposure as core constructs in the development of learner motivational profiles.Additionally, they provide empirical evidence for educators and educational systems alike to recognize mechanisms that may be key in not only determining a learner's success in L2 learning, but also assuring it.Dr. Raywat Deonandan, my first mentor since my undergraduate years, cheering on me and believing in my academic and non-academic projects -thank you for everything -the brainstorming, support letters, listening, and all of the support.Dr. Stephane Aris-Brosou, thank you for introducing the love of research by allowing me to work on your project as a young undergraduate.The self-discipline and ability to organize and research have been a crucial part of my doctorate journey.I owe it to you for believing in me back then and continuing to support my interdisciplinary undertaking years later, with the same belief
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".