The Direct Method in English Language Instruction for Primary School Students in Santarém-PA: An Experience of TESOL Undergraduates
Bibliographic record
Abstract
The Direct Method, a pedagogical approach for teaching additional languages through immersion, emphasizes oral interaction and everyday language usage to facilitate language acquisition. This method discourages reliance on translation and minimizes emphasis on grammatical rules. However, a fundamental question arises about the effectiveness of TESOL programmes in non-English dominant countries in equipping prospective teachers to adopt a monolingual approach. This paper explores the results of a case study aimed at understanding the experiences of two TESOL degree candidates from a university in Northern Brazil. They participated in an outreach project employing the Direct Method to teach English to Year 1 students on the outskirts of Santarém-PA. Data, gathered through proficiency assessments, interviews, and video recordings, were analysed using Content Analysis and video analysis principles in qualitative research. The results revealed challenges faced by the participants due to limited pedagogical knowledge but also unveiled adaptive strategies, collaborative work practices, introspection, and an acknowledgment of the importance of integrating elements from students’ physical and social contexts into lessons. Despite challenges, the study underscores the beneficial potential and viability of the Direct Method in non-English dominant contexts, advocating for its critical consideration and adaptation within TESOL programmes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".