Exploring Obstacles and Effectiveness of Task-Based Language Teaching (TBLT) Approach in EFL Speaking Instruction: Insights from an Adult EFL Classroom
Bibliographic record
Abstract
The use of speaking skills in adult English as a Foreign Language (EFL) instruction is complex and multifaceted. Task-based language teaching (TBLT) offers an opportunity to improve EFL speaking abilities. However, more research is needed on the practical challenges of incorporating TBLT in adult EFL settings. The current study aimed to (1) identify obstacles in implementing the Task-Based Language Teaching (TBLT) approach in adult EFL speaking instruction, (2) explore learners’ perceptions of difficulties, (3) assess the effectiveness of TBLT methods, and (4) provide recommendations to improve the use of TBLT in adult EFL speaking classes. The study utilized qualitative research methods where 58 participants from an adult EFL speaking class in Dhaka, Bangladesh, were selected through purposive sampling. Data was collected through 90-day observations in an EFL-speaking classroom, audio and video recordings, and semi-structured interviews with 10 students and one instructor. The study found that students struggled with understanding English instructions and relied heavily on their first language (L1) during group discussions. Task-based activities and traditional language exercises were crucial for effective learning, but obstacles like nervousness, lack of confidence, and grammatical faults hindered the implementation of Task-Based Language Teaching (TBLT). Adult EFL learners recognized TBLT’s effectiveness in improving speaking skills, emphasizing the importance of regular practice and a supportive learning environment. In conclusion, the study emphasizes the need to address obstacles like lack of confidence, language difficulties, and inadequate instructional assistance in adult EFL instruction to improve speaking abilities and effectively achieve language learning objectives.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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 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".