Feelings Towards Beginning, Intermediate, and Advanced Mixed Spanish Classes Containing Different Types of Learners
Bibliographic record
Abstract
Previous research has shown that positive emotions can facilitate language learning (e.g., Alrabai, 2022), while negative emotions can hinder it (e.g., Seligman, 2011). Therefore, the current study employs a sentiment analysis to determine how different types of learners feel about mixed Spanish courses, or courses that contain early second language learners (EL2), late second language learners (LL2), heritage learners (HLL) and/or native speakers (NS). Students were enrolled in beginning, intermediate, or advanced Spanish courses at a large public university in Western Canada and completed an online questionnaire. The findings indicate support for mixed classes at all levels by HLLs and NSs and mixed support by LL2s and EL2 at the beginner and intermediate level and by LL2s at the advanced level. The sentiment analysis revealed positive emotions for learners who supported mixed courses and mixed emotions for learners who supported separate courses. This study has implications for teachers and learners in mixed classes involving the mitigation of negative feelings by learners.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".