“I Cheat” or “We Cheat?” The Structure and Psychological Correlates of Individual vs. Collective Examination Dishonesty

Koscielniak, Maciej; Enko, Jolanta; Gąsiorowska, Agata

doi:10.1007/s10805-024-09514-x

J Acad Ethics

2024

DOI: 10.1007/s10805-024-09514-x

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“I Cheat” or “We Cheat?” The Structure and Psychological Correlates of Individual vs. Collective Examination Dishonesty

Maciej Koscielniak,

Jolanta Enko,

Agata Gąsiorowska

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Cited by 2 publications

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How to Best Measure Academic Dishonesty in Students

Fritz,

González Cruz,

Janke

et al. 2024

European Journal of Psychological Assessment

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Addressing a pervasive problem in educational institutions, investigations into academic dishonesty by students have produced a vast body of empirical research, mostly based on self-report measures. However, the literature repeatedly points to inconsistencies in assessment methods and unclear measurement quality. We conducted a preregistered systematic review to compare and evaluate self-report assessments (including past cheating behavior and cheating intentions). Of 256 instruments in 231 studies, 70% were unique self-report measures and 38.7% of studies did not investigate or report psychometric quality criteria. As such, the academic dishonesty literature is no exception in questionable measurement practices, where reports often lack key measurement information, such as the full item list (missing in 32%) or the time frames for behavioral assessment (missing in 50.4%). Our findings illustrate the threat to interpretability and comparability due to a lack of psychometric understanding and inconsistent measurement approaches. Contrary to aiming at a unified measure, we propose a focus on best practices in measurement construction and reporting to address diverse contexts, populations, and research questions. To guide future research, we provide a selection of psychometrically validated scales, recommendations for analysis, operationalization, and reporting, and discuss methodological considerations to enhance the psychometric foundation of academic dishonesty research.

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How to Best Measure Academic Dishonesty in Students

Fritz,

González Cruz,

Janke

et al. 2024

European Journal of Psychological Assessment

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IA como Herramienta Precursora del Plagio en la Era Digital y su Impacto en el Ámbito Educativo

Garza-García,

Camacho-Rodríguez,

Chavarría-Alvarado

2024

VinculaTégica

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La inteligencia artificial es una disciplina científica que configura máquinas para que sean inteligentes (Ayuso 2022), ha emergido como una herramienta de efecto dual en el ámbito educativo planteando desafíos éticos y académicos, facilitando tanto el aprendizaje como el plagio. Los procesos académicos se ven afectados por los malos hábitos que tienen los estudiantes de copiar documentos académicos sin darle crédito a los autores, disminuyendo la efectividad de su aprendizaje. El objetivo de este estudio es investigar si la Inteligencia Artificial es una herramienta digital precursora del plagio académico y su impacto en el ámbito educativo. La metodología, es de enfoque mixto, diseño no experimental, corte transversal, y alcance descriptivo a partir de una muestra no probabilística de 108 estudiantes del ITSSPC y 51 de la Universidad Tecnológica del Oriente de Michoacán. El instrumento de recogida fue un cuestionario, con 26 ítems con alfa de Cronbach de 0.906 procesado en SPSS. En los resultados, se afirma que la IA ha aumentado el plagio en el ámbito educativo, genera dependencia, estrés y remordimiento impidiendo pensamiento crítico. Se establecen conclusiones sobre la importancia de promover una cultura de integridad académica en las instituciones.

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“I Cheat” or “We Cheat?” The Structure and Psychological Correlates of Individual vs. Collective Examination Dishonesty

Cited by 2 publications

References 57 publications

How to Best Measure Academic Dishonesty in Students

How to Best Measure Academic Dishonesty in Students

IA como Herramienta Precursora del Plagio en la Era Digital y su Impacto en el Ámbito Educativo

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