How Tanzanian Universities Can Respond to Increasing Market Demands of Specialized Forms of English Language Learning and Communication Skills
Bibliographic record
Abstract
English language and communications skills have undergo tremendous changes in the recent years globally.Increasingly, people realise that they need English language and communication skills not only in employment butalso in full participation in social and political discourses, and Information and Communication Technology. Studieshave shown that while learners have the motivation and innate ability to learn English, there is lack of requisiteopportunities to learn and practice the language. This is because many English language learning proficiencyprogrammes especially in Tanzania are unstructured and not tailored to learners’ particular interests; this is inaddition to using methodologies that emphasize on teaching about language instead of teaching language use. Thecurrent study therefore sought to explore the roles in which universities in Tanzania could play to address the risingmarket demand of English language proficiency programmes with the aim of providing outreach services andgenerate revenue. Face to face interviews, telephone conversations, focus group discussion, questionnaires, anddocumentary review were carried out during data collection. The findings show that the demand for Englishlanguage proficiency programmes in Tanzania is strong. Similarly, the assessment of motivation and expectationindicates that availability of professional teachers and practical sessions, fair fee structure, and learners’ passion forlearning the language were key drivers behind attending the programme. This implies that English languageproficiency is a potential niche market which Tanzanian universities could exploit to meet the rising languagedemands and at the same time generate the much required income.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".