Measuring Digital Competence and ICT Literacy: An Exploratory Study of In-Service English Language Teachers in the Context of Saudi Arabia
Bibliographic record
Abstract
The purpose of this research is to measure in-service English language teachers' digital competence, particularly for the enhancement of teaching English as a second/foreign language in schools in Saudi Arabia. Information and communication technology (ICT) knowledge is currently considered as a vital skill for foreign language teachers in addition to their linguistic competence. Recently, there has been a focus on digital competence, since it can be regarded as a gateway for enriching knowledge, economies, societies and individuals. There is also a massive need for teachers to assess their own digital competence according to non-conventional norms (i.e., having the ability to share content and manage information). In light of this rationale, this paper investigates the following research question: to what extent are English language teachers in Saudi Arabia digitally competent and in what aspects? This study used a standardized questionnaire that was constructed using a validated comprehensive framework. This instrument was designed to assess the professional capability of English language teachers in terms of their willingness and readiness to use ICTs along with their current digital competence used throughout their teaching and educational practices. The research included a diverse range of participants who come from various backgrounds, genders and experiences. The study was concluded with a presentation of useful recommendations and key research questions for future research.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".