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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".