Online Education and Assessment: Profiling EFL Teachers' Competency in Saudi Arabia
Bibliographic record
Abstract
The expanding digital era, emerging geopolitical dynamics, and the birth of the new ‘normal’ that the world has witnessed in the recent times have made the urgency of revamping the academic arena obvious to all. These developments have also made it essential for teachers to be technologically and pedagogically ready to cater to individual needs by being adaptive. This calls for identification of gaps between current pedagogical practices and best practices for the new age classrooms based on the premise that certain competencies in the teachers are essential to ensure the achievement of projected learning objectives in the paradigm of the online learning process. The study uses Ally’s (2019) Competency Profile for the Digital Teacher (CPDT) to determine the level of competency of the English Language Instructors at the ELI, Jazan University, Saudi Arabia in the online teaching-learning and assessment process. The study is quantitative in nature using a questionnaire with thirty-five items factoring to nine major themes for online teaching and eight assessment strategies (Best, 2020) that are seen by experts as competencies that teachers will need by the year 2030. The participants are 67 EFL teachers affiliated to the English language Institute, Jazan University, Saudi Arabia. An exhaustive 35 item questionnaire with a section each devoted to teachers’ general, digital, and assessment competency. Results indicate that EFL teachers at the ELI, Jazan University are competent, digital literate and use online assessment at high levels. The study found only one significant difference attributed to teachers' use of technology across gender. The study recommends EFL teachers at the ELI, the university to cope with the new and emerging needs of the digital learners.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".