Exploring the Characteristic Features of PhD Programs in ELT among Sudanese Universities (Study at Nile Valley University and SUST 2021- 2022)
Bibliographic record
Abstract
The motion of holding a PhD Degree among Sudanese EFL learners is increased widely throughout Sudan. Most of PhD candidates prefer to study courses followed by complementary research. This study aims to explore students’ views on the PhD in ELT programs by courses and complementary research at some Sudanese universities.  The study was conducted to investigate these programs and an attempt to achieve the following objectives: 1- To identify the most prevalent characteristic features of each of these programs. 2- To explore whether the contents of the program satisfy the needs of students or not. 3- To what extent the syllabuses in the programs help to qualify the candidates to become qualified teachers of English language. The study also serves as a needs analysis which was a very important point for the betterment of every ongoing program. The researcher applied the descriptive research method to achieve the study objectives. A questionnaire was used for the data collection. The participants in the study were students enrolled in PhD in ELT programs and graduates who had already graduated from these programs. Then, the data were statistically analysed to evaluate the PhD in ELT programs. The results showed that most participants agree that the contents of the program satisfy the needs of students. Many participants agree that the syllabuses offered in the programs help to qualify students to become qualified teachers of English language.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".