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Record W1990667134 · doi:10.5539/elt.v3n1p200

ESP at the Tertiary Level: Current Situation, Application and Expectation

2010· article· en· W1990667134 on OpenAlexvenueno aff
AbdulMahmoud Idrees Ibrahim

Bibliographic record

VenueEnglish Language Teaching · 2010
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsExploitEnglish for specific purposesPsychologyFunction (biology)Tertiary levelField (mathematics)Mathematics educationSubject (documents)PedagogyHigher educationComputer sciencePolitical scienceLibrary science

Abstract

fetched live from OpenAlex

English for Specific Purposes is an obligatory subject for the first two levels at the Sudanese Universities. It is taught as a university requirement. Accordingly, the students obsess is how to pass the examination not achieve any development in the language field. Even the teachers concentrate on the content rather than the skills, which the students ought to gain. This paper addresses the issue of English for Specific Purposes (ESP). It defines ESP with brief glimpse of its history and it attempts to highlight the line of demarcation of ESP and AEP. Moreover, it will endeavor the objectives of core course of ESP at the tertiary level in Sudanese Universities.Furthermore, how we should mobilize all the efforts to overcome the difficulties to promote the students competency in English language in their very field of specialization. As technology has created change in all aspects of society, it is also changing our expectations of what students must learn in order to function effectively. We should exploit the modern technologies effectively to radically change from teacher-centered approach to student-centered approach in teaching ESP. Consequently, the availability of computer and its utilization in different fields of specialization will be very facilitative and motivating for at least the contemporary generation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.648
Threshold uncertainty score0.778

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.260
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2010
Admission routes1
Has abstractyes

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