This paper introduces and contextualises Climate Futures, an experiment in which AI was repurposed as a ‘co-author’ of climate stories and a co-designer of climate-related images that facilitate reflections on present and future(s) of living with climate change. It converses with histories of writing and computation, including surrealistic ‘algorithmic writing’, recombinatory poems and ‘electronic literature’. At the core lies a reflection about how machine learning’s associative, predictive and regenerative capacities can be employed in playful, critical and contemplative goals. Our goal is not automating writing (as in product-oriented applications of AI). Instead, as poet Charles Hartman argues, ‘the question isn’t exactly whether a poet or a computer writes the poem, but what kinds of collaboration might be interesting’ (1996, p. 5). STS scholars critique labs as future-making sites and machine learning modelling practices and, for example, describe them also as fictions. Building on these critiques and in line with ‘critical technical practice’ (Agre, 1997), we embed our critique of ‘making the future’ in how we employ machine learning to design a tool for looking ahead and telling stories on life with climate change. This has involved engaging with climate narratives and machine learning from the critical and practical perspectives of artistic research. We trained machine learning algorithms (i.e. GPT-2 and AttnGAN) using climate fiction novels (as a dataset of cultural imaginaries of the future). We prompted them to produce new climate fiction stories and images, which we edited to create a tarot-like deck and a story-book, thus also playfully engaging with machine learning’s predictive associations. The tarot deck is designed to facilitate conversations about climate change. How to imagine the future beyond scenarios of resilience and the dystopian? How to aid our transition into different ways of caring for the planet and each other?
Adversarial thinking is essential when dealing with cyber incidents and for finding security vulnerabilities. Capture the Flag (CTF) competitions are used all around the world to stimulate adversarial thinking. Jeopardy-style CTFs, given their challenge-and-answer based nature, are used more and more in cybersecurity education as a fun and engaging way to inspire students. Just like traditional written exams, Jeopardy-style CTFs can be used as summative assessment. Did a student provide the correct answer, yes or no. Did the participant in the CTF competition solve the challenge, yes or no. This research project provides a framework for measuring the learning outcomes of a Jeopardy-style CTF and applies this framework to two CTF events as case studies. During these case studies, participants were tested on their knowledge and skills in the field of cybersecurity and queried on their attitude towards CTF education. Results show that the main difference between traditional written exam and a Jeopardy-style CTF is the way in which questions a re formulated. CTF education is stated to be challenging and fun because questions are formulated as puzzles that need to be solved in a gamified and competitive environment. Just like traditional written exams, no additional insight into why the participant thinks the correct answer is the correct answer has been observed or if the participant really did learn anything new by participating. Given that the main difference between a traditional written exam and a Jeopardy-style CTF is the way in which questions are formulated, learning outcomes can be measured in the same way. We can ask ourselves how many participants solved which challenge and to which measurable statements about “knowledge, skill and attitude” in the field of cybersecurity each challenge is related. However, when mapping the descriptions of the quiz-questions and challenges from the two CTF events as case studies to the NICE framework on Knowledge, Skills and Abilities in cybersecurity, the NICE framework did not provide us with detailed measurable statements that could be used in education. Where the descriptions of the quiz-questions and challenges were specific, the learning outcomes of the NICE framework are only formulated in a quite general matter. Finally, some evidence for Csíkszentmihályi’s theory of Flow has been observed. Following the theory of Flow, a person can become fully immersed in performing a task, also known as “being in the zone” if the “challenge level” of the task is in line with the person’s “skill level”. The persons mental state towards a task will be different depending on the challenge level of the task and required skill level for completing it. Results show that participants state that some challenges were difficult and fun, where other challenges were easy and boring. As a result of this9 project, a guide / checklist is provided for those intending to use CTF in education.
University teacher teams can work toward educational change through the process of team learning behavior, which involves sharing and discussing practices to create new knowledge. However, teachers do not routinely engage in learning behavior when working in such teams and it is unclear how leadership support can overcome this problem. Therefore, this study examines when team leadership behavior supports teacher teams in engaging in learning behavior. We studied 52 university teacher teams (281 respondents) involved in educational change, resulting in two key findings. First, analyses of multiple leadership types showed that team learning behavior was best supported by a shared transformational leadership style that challenges the status quo and stimulates team members’ intellect. Mutual transformational encouragement supported team learning more than the vertical leadership source or empowering and initiating structure styles of leadership. Second, moderator analyses revealed that task complexity influenced the relationship between vertical empowering team leadership behavior and team learning behavior. Specifically, this finding suggests that formal team leaders who empower teamwork only affected team learning behavior when their teams perceived that their task was not complex. These findings indicate how team learning behavior can be supported in university teacher teams responsible for working toward educational change. Moreover, these findings are unique because they originate from relating multiple team leadership types to team learning behavior, examining the influence of task complexity, and studying this in an educational setting. https://www.scienceguide.nl/2021/06/leren-van-docentteams-vraagt-om-gezamenlijk-leiderschap/
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Door producten en diensten inclusief te ontwerpen kunnen ontwerpers een belangrijke bijdrage leveren aan een inclusievere samenleving, waarin iedereen op eigen wijze kan participeren. In AID gaan negen mkb-ontwerpbureaus Afdeling Buitengewone Zaken (A/BZ), theRevolution, Design Innovation Group, Greenberry, Ideate, Keen Public, Muzus, Netrex Internet Solutions (Leer Zelf Online) en Vrienden van verandering) die rijke maar uiteenlopende ervaring hebben met inclusief ontwerpen op zoek naar antwoorden op de vraag hoe hun vermogen voor inclusief ontwerpen kan worden versterkt. Ze doen dit middels actie-onderzoek in hun eigen beroepspraktijk en door hun ervaringen te delen met onderzoekers, docenten en co-ontwerpers in een ‘learning community’.
Studenten in het beroepsonderwijs leren op de werkplek om een goede beroepsuitoefenaar te worden. Beoordeling van het werkplekleren gebeurt vaak op de werkplek en door de werkplek. Dit promotieonderzoek wil in kaart brengen hoe werkplekopleiders de student beoordelen.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
