In dit onderzoek is de online readiness van rijscholen in 2013 in kaart gebracht. In totaal hebben 115 rijscholen deelgenomen aan het onderzoek. Het onderzoek is uitgevoerd door het lectoraat Online Ondernemen samen met studenten van de minor Marketing Tomorrow van de Hogeschool van Amsterdam.
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The recent advancement in the field of information technology has resulted in the proliferation of online education all over the world. Much like traditional classroom education, assessments are an integral component of online education. During the online assessment, evaluation of the learning outcomes presents challenges mainly due to academic dishonesty among students. It results in unfair evaluations that raises questions about the credibility of online assessments. There exist several types of dishonesty in online assessments including exploiting the available Internet for finding solutions (Internet-as-a-Forbidden-Aid), illicit collaboration among students (Collusion) and third-party persons taking assessment on behalf of the genuine student (Impersonation). Several researchers have proposed solutions for addressing dishonesty in online assessments. These solutions include strategies for designing assessments that are resistant to cheating, implementing proctoring and formulating integrity policies. While these methods can be effective, their implementation is often resource-intensive and laborious, posing challenges. Other studies propose the use of Machine Learning (ML) for automated dishonesty detection. However, these approaches often lack clarity in selecting appropriate features and classifiers, impacting the quality of results. The lack of training data further leads to poorly tuned models. There is a need to develop robust ML models to detect different types of dishonesty in online assessments. In this thesis, we focus on Multiple Choice Questions (MCQ)-based assessments. We consider three types of dishonesty: (1) Internet-as-a-Forbidden-Aid, (2) Collusion, and (3) Impersonation prevalent in MCQ-based assessments. We developed individual ML models to detect students involved in each type of dishonesty during the assessment. The results also facilitate understanding the test-taking pattern of students and providing recommendations for cheat-proof assessment design. Finally, we present an Academic Dishonesty Mitigation Plan (ADMP) that addresses the diverse forms of academic dishonesty and provides integrity solutions for mitigating dishonesty in online assessments.
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In 2023 bleek dat accountantskantoren KPMG en Deloitte de afgelopen jaren hebben gefraudeerd met verplichte examens. Het leidde onder andere tot het aftreden van partners. Andere accountskantoren gaan op verzoek van de AFM intern onderzoek doen of de examenfraude ook bij hen voorkomt. Wat dat onderzoek ook oplevert, in ieder geval laat de sector zien dat ze haar maatschappelijke functie uit het oog verliest. Het gedachtegoed van de ethicus Alasdair MacIntyre kan volgens Gert de Jong richtlijnen geven om de sector weer op het juiste spoor te krijgen.
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