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Record W4410415841 · doi:10.5430/wjel.v15n6p163

A University Without Walls: Connecting the Traditional Classroom to the Community Through Sustainable Service-Learning

2025· article· en· W4410415841 on OpenAlexvenueno aff
Mounir Ben Zid, Azza Al-Kendi

Bibliographic record

VenueWorld Journal of English Language · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsnot available
Fundersnot available
KeywordsService-learningService (business)Computer scienceBusinessSociologyPedagogyMarketing

Abstract

fetched live from OpenAlex

Many worldwide initiatives demonstrate how education has reoriented to address sustainability, encourage university professors to leave their ivory towers, and connect the traditional classroom to civic development by providing society with services. Significant strides have been made to support and optimize service-learning for several academic disciplines in the humanities. However, the education for sustainable development in literary studies in the English department at Sultan Qaboos University in Oman has remained more a dead letter than a radical shift in education. Since a growing body of research evidence has demonstrated that the pedagogical practices relying on traditional teaching are not always effective in engaging students in active participation in learning, the study promotes a novel educational framework as a learning process whereby the wall between the university and the community is broken down, English literary studies are paired with sustainable service-learning, and knowledge is created through the transformation of students’ experiences. Within this framework, the pairing of service-learning and literary studies is an innovative way to teach course concepts, expand students’ vision of the society, and empower citizens with the knowledge, skills, values, and attitudes to achieve sustainable development in Oman and set the example for other Gulf countries.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.461
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.028
GPT teacher head0.285
Teacher spread0.258 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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