Editorial: Impact of Education 4.0 for Second Language Learning and Teaching
Bibliographic record
Abstract
The change in worldwide mobility is a natural and inevitable feature today as we are in the digital era now. Education 4.0 is driven by the commencement of Industry 4.0, which is a present trend of digitalization and automation of industries. Technology integrated teaching and learning (Education 4.0) is useful for effective education. The concept of Education 4.0 has gained popularity in recent days. Education 4.0 methodologies have prompted areas of scholarly inquiry, new communicative practices, increasing opportunities for intercultural engagement, supporting new instructional formats and changing the day-to-day communication and learning practices of both teachers and students.Without a doubt, there is no exemption for Second Language Learning and Teaching too. The adoption of Education 4.0 helps support the acquisition of Second Language Learning and Teaching easier. Therefore, this Special Issue aims to publish articles that focus on second language learning and teaching concerning digital integration and online methodologies (Education 4.0).This Special Issue intends to include a range of studies that consider diverse theoretical and instructional practices for second language learning and teaching using technologies. This special issue is not only limited to empirical research but also welcomes conceptual articles that discuss multiple research approaches to investigations of digital writing and examine relevant theoretical frameworks and constructs, which guide empirical research and research-informed pedagogies for second language learning and teaching. Besides, it also welcomes articles related to linguistics/literary discourse.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 0.013 |
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".