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Record W2990027582 · doi:10.5430/ijhe.v9n1p89

A Remedial English Course from the Freshmen EFL Learners’ Viewpoints and Expectations at the Hashemite University in Jordan

2019· article· en· W2990027582 on OpenAlexvenueno aff
Mohammed M. Obeidat

Bibliographic record

VenueInternational Journal of Higher Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in MENA
Canadian institutionsnot available
FundersHashemite University
KeywordsSyllabusRemedial educationViewpointsMathematics educationCurriculumPsychologySet (abstract data type)Course (navigation)Medical educationPedagogyComputer scienceEngineeringMedicine

Abstract

fetched live from OpenAlex

The higher education system in Jordan requires freshmen students in each university to set for an English aptitude exam. If the student fails in this exam, s/he should register in the remedial English 99 course which is regarded as a prerequisite for English 101. The purpose of this study is to explore students’ expectations of this course and whether these expectations are met. The study adopts a qualitative design. Data was collected through a questionnaire interview from 97 students registered in two sections in the Language Center at the university. The findings of the study revealed that students expected the course would improve their communication skills. Later, they discovered that this course benefited them more from the grammatical and structural aspects of language. The findings can be valuable for EFL curriculum developers, syllabus designers and administrators to understand students’ needs better and to take their viewpoints into account.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.300
Teacher spread0.288 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2019
Admission routes1
Has abstractyes

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