The COVID-19 pandemic led to an accelerated implementation of digital solutions, such as online proctoring. In this paper we discuss how the use of an ethical matrix may influence the way in which digital solutions are applied. To initiate an ethical discussion, we conducted an online workshop with educators, examiners, controllers, and students to identify risks and opportunities of online proctoring for various stakeholders. We used the Ethical Matrix to structure the meeting. We compared the outcome of the workshop with the outcomes of a proctoring software pilot by examiners. We found that the two approaches led to complementary implementation criteria. The ethical session was less focused on making things work and more on transparency about conditions, processes, and rights. The ethical session also concentrated more on the values of all involved rather than on fraud detection effectiveness
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Surveillancesoftware benadeelt mensen van kleur, blijkt uit onderzoek. Waarom gebruikt de UvA het dan nog, vragen Naomi Appelman, Jill Toh en Hans de Zwart.
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Digital innovation in education – as in any other sector – is not only about developing and implementing novel ideas, but also about having these ideas effectively used as well as widely accepted and adopted, so that many students can benefit from innovations improving education. Effectiveness, transferability and scalability cannot be added afterwards; it must be integrated from the start in the design, development and implementation processes, as is proposed in the movement towards evidence-informed practice (EIP). The impact an educational innovation has on the values of various stakeholders is often overlooked. Value Sensitive Design (VSD) is an approach to integrate values in technological design. In this paper we discuss how EIP and VSD may be combined into an integrated approach to digital innovation in education, which we call value-informed innovation. This approach not only considers educational effectiveness, but also incorporates the innovation’s impact on human values, its scalability and transferability to other contexts. We illustrate the integrated approach with an example case of an educational innovation involving digital peer feedback.
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Digitization of activities in hospitals receives more attention, due to Covid-19 related regulations. The use of e-health to support patient care is increasing and efficient ways to implement digitization of processes and other technological equipment are needed. We constructed a protocol for implementation and in this study, we evaluate this protocol based on a case to implement a device in the OR. We used various data sources to evaluate this protocol: semi-structured interviews, questionnaires, and project documents. Based on these findings, this protocol, including identified implementation activities and implementation instructions can be used for implementations of other devices. Implementation activities include setting up a project plan, organizational and technological preparation, maintenance, and training. In future research, these activities and instructions need to be evaluated in more complex projects and a flexible tool needs to be developed to select relevant activities and instructions for implementations of information systems or devices.
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De impact van COVID-19 op het beroepsonderwijs is enorm, zo blijkt uit recente consultaties en studies van de Inspectie van het Onderwijs, het Nederlands Jeugdinstituut, Cedefop, Samenwerkingsorganisatie Beroepsonderwijs Bedrijfsleven en uit onderzoek onder Nederlandse studenten en docenten in het mbo en hbo (Grijpstra-Hanekamp et al., 2020; Rutten, 2020; Custers et al., 2020). Het beroepsonderwijs (mbo en hbo) is sterk verweven met de beroepspraktijk. Beroepspraktijkvorming, stages, leerbedrijven, werkplekleren en uiteenlopende leeromgevingen op de grens tussen school en beroepspraktijk vormen een belangrijk deel van een beroepsopleiding. Daar ontstaan nu problemen: er zijn minder stageplekken beschikbaar, praktijkleren is lastig omdat moeilijk afstand kan worden gehouden. Dit roept de vraag op: zijn er online en op 1,5 meter afstand nog wel voldoende mogelijkheden voor leren handelen in een complexe en dynamische beroepspraktijk?
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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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Dit Trendrapport Open Education 2014 beschrijft de trends op het gebied van open education in binnen- en buitenland, geschreven vanuit de context van het Nederlandse hoger onderwijs. Dat gebeurt aan de hand van acht artikelen van Nederlandse experts op het gebied van open en online onderwijs en acht korte intermezzo’s.
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Dit rapport bevat de resultaten van een kwalitatief onderzoek, waarvoor meerdere docenten van Inholland werden geïnterviewd naar de wijze waarop zij zich geprofessionaliseerd hebben in het verzorgen (en ontwerpen) van onderwijs tijdens de coronapandemie. Aan hand van de gesprekken kunnen we hierin twee fasen onderscheiden: voor de zomer van 2020 en daarna. In het rapport beschrijven we zowel deze fasen als de voor- en nadelen die docenten ervaren hebben. Daarnaast gaven docenten aan wat zij nodig hebben om een combinatie van fysiek en online onderwijs mogelijk te maken. Om blended learning (verder) te ontwikkelen, beschrijven we in het rapport ook een aantal overwegingen die hierbij van belang zijn.
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This white paper is presented by the Ethics Working Group of the uNLock Consortium This white paper presents findings of the Ethics Working Group, from the conceptual phase of investigation into the ethical issues of the uNLock solution, providing identity management solutions for sharing and presentation of medical COVID-19 credentials (test results) in the context of healthcare institutions. We have provided an outline of direct and indirect stakeholders for the uNLock solution and mapped values, benefits, and harms to the respective stakeholders. The resulting conceptual framework has allowed us to lay down key norms and principles of Self Sovereign Identity (SSI) in the specific context of uNLock solution. We hope that adherence to these norms and principles could serve as a groundwork for anticipatory mitigation of moral risk and hazards stemming from the implementation of uNLock solution and similar solutions. Our findings suggest that even early stage of conceptual investigation in the framework of Value Sensitive Design (VSD), reveals numerous ethical issues. The proposed implementation of the uNLock app in the healthcare context did not proceed further than prototype stage, thus our investigation was limited to the conceptual stage, and did not involve the practical implementation of VSD method involving translation of norms and values into engineering requirements. Nevertheless, our findings suggest that the implementation of VSD method in this context is a promising approach that helps to identify moral conflicts and risks at a very early stage of technological development of SSI solutions. Furthermore, we would like to stress that in the light of our findings it became painfully obvious that hasty implementation of medical credentials system without thorough ethical assessment, risks creating more ethical issues rather than addressing existing ones.
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The security of online assessments is a major concern due to widespread cheating. One common form of cheating is impersonation, where students invite unauthorized persons to take assessments on their behalf. Several techniques exist to handle impersonation. Some researchers recommend use of integrity policy, but communicating the policy effectively to the students is a challenge. Others propose authentication methods like, password and fingerprint; they offer initial authentication but are vulnerable thereafter. Face recognition offers post-login authentication but necessitates additional hardware. Keystroke Dynamics (KD) has been used to provide post-login authentication without any additional hardware, but its use is limited to subjective assessment. In this work, we address impersonation in assessments with Multiple Choice Questions (MCQ). Our approach combines two key strategies: reinforcement of integrity policy for prevention, and keystroke-based random authentication for detection of impersonation. To the best of our knowledge, it is the first attempt to use keystroke dynamics for post-login authentication in the context of MCQ. We improve an online quiz tool for the data collection suited to our needs and use feature engineering to address the challenge of high-dimensional keystroke datasets. Using machine learning classifiers, we identify the best-performing model for authenticating the students. The results indicate that the highest accuracy (83%) is achieved by the Isolation Forest classifier. Furthermore, to validate the results, the approach is applied to Carnegie Mellon University (CMU) benchmark dataset, thereby achieving an improved accuracy of 94%. Though we also used mouse dynamics for authentication, but its subpar performance leads us to not consider it for our approach.
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