Collaborative and Self-directed Learning Strategies to Promote Fluent EFL Speakers
Bibliographic record
Abstract
Speaking English with fluency is one of the most demanding challenges students and teachers face in many educational communities, and it has been claimed that fluency problems can derive from lack of practice during independent study. This research article reports on a mixed-methods study that analyzed the effects of using collaborative and self-directed learning strategies through speaking tasks aimed at developing oral fluency. This study was carried out with a group of 10 students with a pre-intermediate level (CEFR A2) in English at a Colombian university. Qualitative data from students’ reflections, compiled through a survey, and the teacher’s classroom observations was analyzed through the grounded theory approach. Quantitative analysis was aided by a protocol in which frequency counts of words and hesitations per minute for each speaking task were registered. The results suggest that fluency can be acquired collaboratively when learning from others and by making mistakes. Additionally, working collaboratively increases learners’ confidence not only because they feel they are not being judged but because they learn to see that their mistakes are not just theirs. Thus, collaboration is positively influenced by self-directed learning, in that it encourages students to make personal reflections on their weaknesses and strengths, thereby involving them in decision-making processes that identify what is not working properly and what they should do to succeed.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".