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Record W4405514026 · doi:10.58932/mula0015

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

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

Bibliographic record

VenueJournal of Professional Research in Social Sciences · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
Fundersnot available
KeywordsAcademic integrityCheatingHigher educationPolitical scienceBusinessSociologyMathematics educationEngineering ethicsPsychologyLawSocial psychologyEngineering

Abstract

fetched live from 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.

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)0.050
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.319
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0500.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.006
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 head0.588
GPT teacher head0.636
Teacher spread0.048 · 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 designTheoretical or conceptual
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
Published2023
Admission routes1
Has abstractyes

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