Investment in Learning English as an Additional Language
Bibliographic record
Abstract
Learning additional languages is an experience and a process influenced by numerous aspects. This article presents a qualitative case study focused on the aspects influencing the investment of seven learners of English as an additional language, all of whom are pre-service teachers pursuing bachelor’s degrees in English education. The data under consideration is part of a larger intervention-based action research study that explored the language learning experiences of twenty pre-service teachers enrolled in a teacher preparation program at a public university in Colombia. The study is grounded in Darvinand Norton’s (2015) concept of investment and highlights the significance of communities of practice (Wenger, 2011). We analyzed open-ended interviews using Saldaña’s (2016) coding framework. The findings suggested that the investment of additional language learners can be influenced by their teachers’ pedagogical practices, the learning communities created by teachers, and the learners’ own imagined selves or identities. We discussed the implications of these findings for language teachers, language centers or institutes, and teacher education programs.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 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".