Systematic Literature Mapping: Studies Related to ESL/EFL Oral Communication Skills (2018-2022)
Bibliographic record
Abstract
In our interconnected world, English has become the language used for communication in different contexts. It is used as the language employed to facilitate exchange of information in different fields such as business, academia, science, technology, and culture, among others. This paper describes the development of a systematic literature mapping (SLM) using 68 studies from the Scopus and WoS databases from 2018 to 2022 related to English language oral communication with the purpose of analyzing recent publications on the topic, the thematic lines that researchers have focused on, the methodology and tools used to carry out their research, the contexts in which investigations take place, the journals that publish these articles, and the recommendations for future studies. The results show the interest in EFL/ESL oral communication in different environments, the strategies used by teachers and learners, some of the cognitive and affective processes that impact oral proficiency, as well as the use of technology and how it contributes to the development of these investigations. The search for articles was limited to articles written in the English language that referred to oral communication in English as a second or foreign language. This work is of value for researchers and teachers interested in exploring the trends in this topic.
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 | 0.044 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.088 | 0.060 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".