EFL Teaching in the Amazon Region of Ecuador: A Focus on Activities and Resources for Teaching Listening and Speaking Skills
Bibliographic record
Abstract
Research on teaching listening and speaking skills has been conducted at many levels. The purpose of this study was to analyze the current implementation of classroom and extracurricular activities, as well as the use of educational resources for teaching both skills in public senior high schools in the Amazon region of Ecuador, particularly in the towns of Zamora and Yantzaza. The participants included 188 students between the ages of 15 and 18 years, and 10 English teachers in the area. This study was conducted using a mixed-method (quantitative and qualitative) approach. The instruments to collect information were students’ and teachers’ questionnaires, teacher’s interviews, and observation sheets. In addition, the Cambridge English Unlimited Placement Test was applied in order to determine the students’ English proficiency level of listening and speaking skills. The findings revealed that the completion of exercises based on a listening passage and dialogues were the most frequent activities used by students to practice listening in the classroom; meanwhile, dialogues and repetition drills were the most frequent activities applied to develop speaking. With respect to extracurricular activities, listening to English music with lyrics, watching videos, discussions, and conversations with native speakers were most frequently suggested for enhancing listening and speaking. Among the resources, audio CDs and textbooks were the most frequent aids to prompt listening and speaking activities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".