Zoekresultaten

Expertise and Deceptive Movements in Sport

Background: Deceptive movements occur when an actor seeks to fake, hide or delay kinematic information about their true movement outcomes. The purpose of deceptive movements is to impair the perception of opponents (the ‘observer’) to gain an advantage over them. We argue though that a lack of conceptual clarity has led to confusion about what deception is and in understanding the different approaches by which an actor can deceive their opponent. The aim of this article is to outline a conceptual framework for understanding deceptive movements in sport. Main body: Adopting Interpersonal Deception Theory from the field of communication, we define deception as when an actor deliberately alters their actions in an attempt to impair the ability of an observer to anticipate their true action outcomes. Further, deception can be achieved either by what we define as deceit, the act of providing false information, or disguise, the act of concealing the action outcome. Skilled athletes often have actions that are difficult to anticipate, but an action is only classified as containing deception if the actor has explicit intent to deceive an observer. Having outlined the conceptual framework, we then review existing empirical findings on the skilled perception of deceptive movements considering the framework. This approach includes a critical evaluation of the mechanisms known to facilitate the perceptual ability to prevent being deceived, including a consideration of visual search strategies, confidence, the contribution of visual and motor experiences, and the influence of response biases and action capabilities on perceptual performance. Conclusion: The distinction between deceit and disguise particularly helps to show that most research has examined deceit, with little known about how an actor can more effectively disguise their action, or about how an observer can improve their ability to anticipate the outcome of disguised actions. The insights help to identify fruitful areas for future research and outline implications for skill acquisition and performance enhancement.

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Effectiveness of educational approaches for elementary school pupils (11 or 12 years old) to combat fake news

The purpose of this literature study is to obtain information about educational approaches to teaching 11 to 12 years old children focusing on how to distinguish between real news and fake news. With this purpose we studied 16 academic papers about learning activities to make primary school children media-literate and able to recognise fake news. What we found is that having children create their own news messages seems to be the most effective approach. News messages that they create can be text messages as well as videos, audios, pictures and animations. Based on this conclusion, students from The Hague University of Applied Sciences Teacher Training Institute (PABO) have been asked to develop a set of learning materials that can be used for instruction in primary schools. The effectiveness of those materials is currently being tested at an elementary school in Rijswijk. The results of the literature and the field study will be shared in the Dutch centre of expertise for media literacy education, Mediawijzer.net.

Empirical Investigation of Values Affected by AI Systems for Fire Services

Fire fighters operate in a dangerous, dynamic, and complex environment. Artificial Intelligence (AI) systems can contribute to improve fire fighters’ situation awareness and decision-making. However, the introduction of AI systems needs to be done responsibly, taking (human) values into account, especially as the situation in which fire fighters operate is uncertain and decisions have a big impact. In this research, we investigate values that are affected by the introduction of AI systems for fire services by conducting several semi-structured focus group sessions with (operational) fire service personnel. The focus group outcomes are qualitatively analyzed and key values are identified and discussed. This research is a first step in an iterative process towards a generic framework of ethical aspects for the introduction of AI systems in first response, which will give insight into the relevant ethical aspects to take into account when developing AI systems for first responders.

Creating news

The purpose of this research is to find evidence for the assumption that allowing children to create their own news messages is an effective approach to teach them how to distinguish between reliable news and fake news. Three students of the primary teacher training programme of The Hague University of Applied Sciences developed five lessons concerning fake news and five Kahoot! quizzes for each of those lessons. They taught the lessons they developed under the supervision of a primary school teacher and one of their lecturers from the university. A Friedman test on the scores of the Kahoot! quizzes indicate that the children made progress over the course of the study. In addition, it appears that the children appreciated the lessons and that they have learned how news is created and how fake news can be recognised. The outcomes of this study have prompted a larger, international Erasmus+ project. Schools and libraries in three countries will investigate similar innovative blended-learning approaches for pupils between ages 12 to 15.

A comprehensive approach for mitigating impersonation in online assessment

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.