Digital Competence Training of EFL Primary Pre-Service Teachers: A Systematic Review of the Spanish Context
Bibliographic record
Abstract
Competence frameworks are becoming a priority in education as reflected in the projects and reports by international institutions and organizations. The focus is on students’ competency acquisition and also on teachers’ competencies that should be considered essential for educators of the 21st century. In this study, digital and linguistic competences are highlighted as particularly relevant for pre-service teachers as many of the competences overlap categories. Over the last seven years, the publications in this field has increased, demonstrating the importance of digital and linguistic competence for EFL Primary pre-service teachers. Consequently, the aim of this systematic review, using the PRISMA model, is to analyze the published research on the simultaneous development of digital and linguistic competences of EFL Primary pre-service teachers to examine the current situation in the Spanish educational context. Although the limited number of studies, the analysis shed light on digital tools and applications that can be useful for digital and linguistic competence development of EFL primary pre-service teachers, contributing to improve their future praxis as teachers. However, some inconveniences are highlighted, such as the unfamiliarity with digital tools. According to the analysis, in order to respond to the 21st century educational demands, EFL pre-service teachers must be digitally and linguistically competent. This supports the need for specific training in competency development by taking advantage of the areas where competences overlap. Therefore, Higher Education Institutions (HEIs) need to develop curricula to provide competency gain and guarantee a wide range of skills to EFL Primary pre-service teachers for their future praxis.
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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 | 0.013 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".