The Impacts of Blended Learning on English Language Proficiency in Higher Education: A Systematic Literature Review
Bibliographic record
Abstract
Blended learning, integrating traditional and online instruction, has emerged as a significant approach to enhancing English proficiency (listening, speaking, reading, writing) among non-native university students. This study conducted a PRISMA 2020-guided systematic review of 52 articles (2020-2024) from Web of Science, Scopus, EBSCOhost, and ERIC, with 30 meeting MMAT quality criteria. Using the PICO framework, it analyzed blended learning’s impact on language skills. Findings indicated notable improvements across all four competencies, attributed to methods like timely feedback, task/project-based learning, and self-paced modules, alongside strategies such as flipped classrooms, multimodal resources, mobile technologies, and collaborative activities. These approaches enhanced flexibility, interactivity, and personalized learning while providing rich resources. The integration of online and offline phases, combined with structured peer/instructor interaction, was critical for skill development. Results underscore blended learning’s potential to inform instructional design, policy-making, and quality improvements in higher education language programs, addressing globalization-driven demands for advanced English proficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".