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Record W2070963214 · doi:10.1080/08975930.2011.653746

Implementation of Service-Learning in Business Education: Issues and Challenges

2011· article· en· W2070963214 on OpenAlexaff
Patrick Poon, Tsang Sing Chan, Lianxi Zhou

Bibliographic record

VenueJournal of Teaching in International Business · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicService-Learning and Community Engagement
Canadian institutionsBrock University
Fundersnot available
KeywordsService-learningExperiential learningService (business)General partnershipActive learning (machine learning)Business educationBusiness ethicsTest (biology)Knowledge managementHigher educationPsychologySociologyPublic relationsPedagogyBusinessComputer scienceMarketingPolitical science

Abstract

fetched live from OpenAlex

This paper examines the issues and challenges in the implementation of service-learning in undergraduate business education. It also provides an assessment of the students' learning efficacy and outcomes over time through the service-learning participation. Service-learning is a pedagogical approach that integrates academic learning and community service. It involves partnership among various stakeholders like students, faculty members, community members, and social agencies. There are challenges for various stakeholders to implement service-learning in business courses. The findings of a survey of a group of business students participated in service-learning projects show that the students have an increased level of sense of social responsibility, ethical and moral behavior after the participation in service-learning projects. Nevertheless, no significant difference is found for learning outcomes between the pre-test and post-test. The paper addresses the critical success factors and constraints of the integration of service-learning and provides recommendations for designing experiential service-learning projects in business education.

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)0.062
metaresearch head score (Gemma)0.060
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.062
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0620.060
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0060.008
Scholarly communication0.0170.011
Open science0.0050.011
Research integrity0.0060.007
Insufficient payload (model declined to judge)0.0060.002

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 head0.079
GPT teacher head0.374
Teacher spread0.295 · 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

Citations36
Published2011
Admission routes1
Has abstractyes

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