Speak Beyond Borders: A Systematic Review of Task-Based Language Teaching for EFL Speaking Proficiency
Bibliographic record
Abstract
Task-Based Language Teaching (TBLT) has drawn much interest in recent years. This study conducted a thorough analysis of 38 articles from 2014 to 2023 that applied the TBLT approach to enhance English as a Foreign Language (EFL) speaking proficiency, utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) from the Web of Science (WoS) database. These articles were selected based on specific inclusion and exclusion criteria. The findings highlight a growing focus on integrating TBLT with technological tools such as Digital Storytelling (DST) and mobile-supported tasks in various EFL contexts, particularly in higher education. The studies are predominantly underpinned by sociocultural theory, cognitive psychology, and constructivism, assessing speaking proficiency through the Common European Framework of Reference (CEFR). Quasi-experimental and mixed methods design using convenience and purposive sampling are common. Data collection frequently involves observations, interviews, and tests. The systematic review reveals TBLT's significant effects on students’ speaking proficiency, engagement, risk-taking, linguistic complexity, and motivation, offering essential implications and recommendations for future research and educational practices.
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".