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Enregistrement W4405514026 · doi:10.58932/mula0015

Addressing Contract Cheating in Pakistani Higher Education: Strategies for Upholding Academic Integrity

2023· article· en· W4405514026 sur OpenAlexaboutno aff
Anjum Zia, Md. Ariful Anwar Khan

Notice bibliographique

RevueJournal of Professional Research in Social Sciences · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueAcademic integrity and plagiarism
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAcademic integrityCheatingHigher educationPolitical scienceBusinessSociologyMathematics educationEngineering ethicsPsychologyLawSocial psychologyEngineering

Résumé

récupéré en direct d'OpenAlex

Contract cheating has strained the higher education system worldwide, Pakistan is no exception. However, unlike Canada, UK, USA, Pakistan has limited research related to the issue under discussion. The presence of contract cheating has undermined the academic integrity and academic credibility of higher education in Pakistan. This paper sheds light on the role of Quality Assurance Cells in higher educational institutions including University of Management and Technology, University of the Punjab, Lahore College for Women University and Minhaaj University in addressing this issue and proposes recommendations to foster academic integrity. This paper tries to investigate this complex issue, offering strategies to the institutions for addressing it and fostering a culture of academic integrity. In a pursuit to comprehend contract cheating and devise strategies to eradicate it, this study draws upon Albert Bandura's Social Cognitive Theory. The main assumption of this theory is that behavior is learned through observational learning, personal agency, and self-regulation, influenced by both individual and environmental factors. Applying Social Cognitive Theory to the context of contract cheating allows for a comprehensive understanding of the issue, the factors contributing to it, and the strategies that universities can utilize to mitigate its occurrence. The current study was qualitative in nature. The researchers conducted in-depth interviews of Directors of Quality Assurance s of the universities mentioned above. The responses were thematically analyzed. The participants were asked about the various reasons that compelled the students to engage in contract cheating and how universities can combat it. As per the majority, to combat contract cheating, institutions must adopt a comprehensive framework. This paper recommends five pivotal areas for Pakistani higher education institutions to anticipate when devising strategies against contract cheating. Institutions must design a policy that prevents, detects, and intervenes in contract cheating. This strategy should align with Pakistan's unique cultural and academic landscape. Existing policies/guidelines need to be reviewed to clearly tackle contract cheating. Policies should highlight the consequences of such actions and strengthen institutional commitment to maintain academic integrity. Comprehending students' motivations for contract cheating is very important. Institutions should create platforms for open discussions and offer support services that address academic pressures, fostering ethical behaviors. Assessment methods must evaluate students' understanding and critical thinking instead of simple information replication. Applying various assessment formats like presentations and group work can discourage contract cheating. Educators significantly influence academic integrity. Offering professional development opportunities equips them to detect and prevent contract cheating, cultivating a culture of academic integrity. The issue of contract cheating necessitates collaborative efforts from Pakistani higher education institutions to inculcate a culture of academic integrity. Moreover, approval and implementation of HEC’s draft policy concerning the prevailing issues are proved to be a turning point in Higher Education. By concentrating on multidimensional strategies, institutions can not only address contract cheating concerns but also nurture ethical scholars and professionals.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,050
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies, Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,319
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0500,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0020,001
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0010,006
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,588
Tête enseignante GPT0,636
Écart entre enseignants0,048 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2023
Routes d'admission1
Résumé présentoui

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