Systematic Literature Review on Evaluation of English Language Textbooks: A Decade of Research
Bibliographic record
Abstract
Textbooks, essential for teaching and learning, have become a burgeoning research subject in education. However, textbook evaluation has not garnered adequate attention in the context of English Language Teaching (ELT). This hinders the identification of key characteristics, focused learning themes, and gaps encountered within the EFL/ESL educational landscape. This systematic literature review, employing the ROSES framework, explores key characteristics and learning themes within English language textbook evaluation in the ELT context, identifying suggestions for future evaluations. The review involves searching, screening, evaluating, and synthesizing pertinent articles published in the last decade, from 2014 to 2023, across Scopus and Web of Science databases. Out of 2304 articles identified through a search of keywords including “textbook” and “evaluation” alongside their synonyms, 30 studies meeting the inclusion criteria are shortlisted after the quality appraisal using MMAT. The review finds that questionnaires, surveys and checklists were the most common methods used for ELT textbooks. Based on the findings from the review, this paper discusses a wide range of indicators or criteria involved in evaluating ELT textbooks, particularly in the evaluation of culture and pragmatics. Our research has revealed a need for in-depth exploration using qualitative and mixed-methods approaches, emphasizing the necessity for broader comparative studies and a more diverse range of perspectives in educational assessments to bridge knowledge gaps. This study suggests that further research on textbook evaluation in the ELT context is still necessary.
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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 | 0.054 | 0.186 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.019 | 0.021 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".