Academic cheating poses a significant challenge to conducting fair online assessments. One common way is collusion, where students unethically share answers during the assessment. While several researchers proposed solutions, there is lack of clarity regarding the specific types they target among the different types of collusion. Researchers have used statistical techniques to analyze basic attributes collected by the platforms, for collusion detection. Only few works have used machine learning, considering two or three attributes only; the use of limited features leading to reduced accuracy and increased risk of false accusations. In this work, we focus on In-Parallel Collusion, where students simultaneously work together on an assessment. For data collection, a quiz tool is improvised to capture clickstream data at a finer level of granularity. We use feature engineering to derive seven features and create a machine learning model for collusion detection. The results show: 1) Random Forest exhibits the best accuracy (98.8%), and 2) In contrast to less features as used in earlier works, the full feature set provides the best result; showing that considering multiple facets of similarity enhance the model accuracy. The findings provide platform designers and teachers with insights into optimizing quiz platforms and creating cheat-proof assessments.
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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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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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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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The range of studies that has been conducted on the role of gossip in organizations suggests that gossip in the workplace plays a variety of important roles in organisational processes. However, relatively few studies have explored its role in intercultural situations. This is surprising given how organisations are becoming increasingly diverse. This paper addresses this gap in the literature. It reports on an exploratory project that sought to determine how perceptions of organisational gossip vary between members of different cultural groups. Using a sensemaking, interpretative approach, we showed two gossip scenarios to 8 Chinese, 8 German and 8 Dutch first year students, and conducted semi structured interviews, asking them how they perceived the nature of the gossip, the gossiper and the object of gossip (i.e., the person being gossiped about). After analysing the data with ATLAS.ti, we observed certain patterns emerging. For example, while all students condemned a manager’s bad behaviour, the Chinese students seemed to expect it more than did their Dutch or German counterparts. Moreover, we found that the relationship and amount of trust that exists between gossiper, listener and object of gossip greatly influenced how the gossiper and object of gossip were perceived. After reflecting on our research methodology, this study sets the stage for the next phase of our research on the role of gossip in intercultural situations. LinkedIn: https://www.linkedin.com/in/dominiquedarmon/
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Social media platforms such as Facebook, YouTube, and Twitter have millions of users logging in every day, using these platforms for commu nication, entertainment, and news consumption. These platforms adopt rules that determine how users communicate and thereby limit and shape public discourse.2 Platforms need to deal with large amounts of data generated every day. For example, as of October 2021, 4.55 billion social media users were ac tive on an average number of 6.7 platforms used each month per internet user.3 As a result, platforms were compelled to develop governance models and content moderation systems to deal with harmful and undesirable content, including disinformation. In this study: • ‘Content governance’ is defined as a set of processes, procedures, and systems that determine how a given platform plans, publishes, moder ates, and curates content. • ‘Content moderation’ is the organised practice of a social media plat form of pre-screening, removing, or labelling undesirable content to reduce the damage that inappropriate content can cause.
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This curriculum on social mentoring is the result of a collaboration of a diverse group of academics, practitioners and students from around the world and was developed and facilitated as part of the Erasmus Plus Project “Mentoring for Social Inclusion in Europe: Sharing Knowledge and Building Capacity” (Ment4EU). It was implemented for the first time in Europe as a cross-organisational effort with a transdisciplinary approach as a blended intensive program with 30 participants from the partner countries joined by a further 220 students, practitioners and academics from NHL Stenden and the Netherlands for plenary sessions. The intended group of learners for this course are students, lecturers, researchers, academics and practitioners (mentors and program managers/coordinators of mentoring programs) interested in learning about mentoring for social inclusion and who are active in the fields of social work, youth work, sociology, health care, community work, management and organization, and related fields of practice and study programs. The weight of the program is 5 ECTS. This curriculum was developed as part of the Erasmus Plus Cooperation Partnership in Higher Educatoin “Mentoring for Social Inclusion in Europe: Sharing Knowledge and Building Capacity” (Ment4EU), as part of WP3_A2 Training Capacity in higher education institutions, project number 2023-1-AT01-KA220-HED-000158214
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Ethnographic fieldwork is a balancing act between distancing and immersing. Fieldworkers need to come close to meaningfully grasp the sense-making efforts of the researched. In methodological textbooks on ethnography, immersion tends to be emphasized at the expense of its counterpart. In fact, ‘distancing’ is often ignored as a central tenet of good ethnographic conduct. In this article we redirect attention away from familiarization and towards ‘defamiliarization’ by suggesting six estrangement strategies (three theoretical and three methodological) that allow the researcher to develop a more detached viewpoint from which to interpret data. We demonstrate the workings of these strategies by giving illustrations from Machteld de Jong’s field- and text-work, conducted among Moroccan-Dutch students in an institution of higher vocational education.
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Youths in Bolgatanga municipality in the Upper East Region in the rural north of Ghana suffer health and social problems that are caused by their premarital and unsafe sexual behaviour. This study provides more knowledge of and insight into the youths’ conceptions, motives and practices concerning premarital sex in the specific cultural and social context of Bolgatanga municipality. The results of this study can contribute to the development of more effective sexual and reproductive health (SRH) programmes. Interviews with 33 youths and 27 key respondents were carried out. Four repertoires were constructed to present the dynamics wherein the youths’ premarital sexual behaviour takes place. The dominant ideology of abstaining from premarital sex contrasts with the counter ideology of allowing premarital sex, influenced by increasing modernization. SRH programmes should take into account the increasing influence of modernity, gender differences and the compelling influence of peer groups, all of which contribute to youths engaging in premarital sex, with health and social problems as possible consequences. (Afr J Reprod Health 2013; 17[4]: 93-106).
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Introduction: Nowadays the Western mental health system is in transformation to recovery-oriented and trauma informed care in which experiential knowledge becomes incorporated. An important development in this context is that traditional mental health professionals came to the fore with their lived experiences. From 2017 to 2021, a research project was conducted in the Netherlands in three mental health organizations, focussing on how service users perceive the professional use of experiential knowledge. Aims: This paper aims to explore service users’ perspectives regarding their healthcare professionals’ use of experiential knowledge and the users’ perceptions of how this contributes to their personal recovery. Methods: As part of the qualitative research, 22 service users were interviewed. A thematic analysis was employed to derive themes and patterns from the interview transcripts. Results: The use of experiential knowledge manifests in the quality of a compassionate user-professional relationship in which personal disclosures of the professional’s distress and resilience are embedded. This often stimulates users’ recovery process. Conclusions: Findings suggest that the use of experiential knowledge by mental health professionals like social workers, nurses and humanistic counselors, demonstrates an overall positive value as an additional (re)source.
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