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DTSTART;TZID=Europe/Amsterdam:20220405T080000
DTEND;TZID=Europe/Amsterdam:20220705T170000
DTSTAMP:20260414T162237
CREATED:20220315T094648Z
LAST-MODIFIED:20220315T095420Z
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SUMMARY:ECDA Leadership Challenge Higher Education (April pilot edition)
DESCRIPTION:This edition is FULLY BOOKED! Join our September edition instead! \n\n\n\nThe use of data and application of analytics and artificial intelligence (AI) will without any doubt change the way we design and operate our Higher Education. As a matter of fact\, today it is already changing our educational institutions. But what is needed to make analytics and AI valuable parts of the way we organize our education? Many experts believe that the successful transformation of our Higher Education hinges on five pillars: strategy\, hr and culture\, organisation\, governance and compliance\, ICT. \n\n\n\nThis insight will require a whole new set of skills and ways of working. Understanding and working with new technologies for (big) data collection\, analysis and prediction will not create only huge opportunities\, but also ethical\, legal\, privacy and technical issues concerning every part of the organization. It will influence the relationship with our students\, redefine how new programs and services are developed\, change how operations are managed\, and provide the basis for new service offerings. It will demand a data-driven focus of everyone involved in the organization. \n\n\n\nThis training programme combines the science of business\, data\, and societal perspectives. Participants – who usually join with a team of 3 to 5 persons – acquire a broad knowledge and diverse skills related to data analytics\, which may lead to new insights that drive new value creation opportunities in the context of higher education. Such learning by doing manifests itself along two dimensions: across multiple levels (individual\, group) and across multiple functions
URL:https://ecda.eur.nl/event/ecda-leadership-challenge-higher-education-april-pilot-edition/
LOCATION:SURF Offices Hoog Overborch (Hoog Catharijne)\, Moreelsepark 48\, Utrecht\, South Holland\, 3511 EP\, Netherlands
CATEGORIES:ECDA Leadership Challenge Higher Education
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ORGANIZER;CN="Erasmus Centre for Data Analytics":MAILTO:ecda@rsm.nl
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DTSTART;TZID=Europe/Amsterdam:20220601T080000
DTEND;TZID=Europe/Amsterdam:20220601T170000
DTSTAMP:20260414T162237
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LAST-MODIFIED:20220315T093436Z
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SUMMARY:ECDA Insights Psychology of AI
DESCRIPTION:The expert practice of this ECDA Insights is Psychology of AI and the topic englobes decision-driven analytics.
URL:https://ecda.eur.nl/event/xp-spotlight-psychology-of-ai/
LOCATION:TBA
CATEGORIES:ECDA Insights
ORGANIZER;CN="Erasmus Centre for Data Analytics":MAILTO:ecda@rsm.nl
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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220613T080000
DTEND;TZID=Europe/Amsterdam:20220613T170000
DTSTAMP:20260414T162237
CREATED:20211109T165314Z
LAST-MODIFIED:20220119T204806Z
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SUMMARY:ECDA Insights Virtual & Augmented Reality
DESCRIPTION:The expert practice presenting this ECDA Insights is Virtual & Augmented Reality and the topic englobes business opportunities of VR/AR.
URL:https://ecda.eur.nl/event/xp-spotlight-virtual-augmented-reality/
CATEGORIES:ECDA Insights
ORGANIZER;CN="Erasmus Centre for Data Analytics":MAILTO:ecda@rsm.nl
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220615T120000
DTEND;TZID=Europe/Amsterdam:20220615T130000
DTSTAMP:20260414T162237
CREATED:20220530T091932Z
LAST-MODIFIED:20220530T091941Z
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SUMMARY:AI education for ICT dummies?
DESCRIPTION:A webinar on teaching AI to non-ICT students (MBO\, HBO\, WO) \n\n\n\n\n\nArtificial intelligence is increasingly present in society and consequently in the curricula of numerous programmes\, including the non-technical. But how do you convey a technological subject to students who have not chosen for an ICT programme? How do you inspire and motivate a public that has no intrinsic interest in AI? In this webinar João Ferreira Goncalves will tackle these questions and guide teachers who are involved in teaching AI and data technology to non-ICT students. \n\n\n\nThis webinar is organised by the AI\, data & digitalisation Convergence group of the Erasmus University Rotterdam. It is a contribution to the month of AI in education and aims to engage teachers with the topic of AI. \n\n\n\nAbout Convergence AI\, data & digitalisation:\n\n\n\nTU Delft\, Leiden University\, Erasmus MC and Erasmus University Rotterdam join forces in ConvergenceOpens external. The urgent and complex societal challenges of our time ask for fundamentally new forms of cooperation\, whereby the boundaries between institutes and disciplines are crossed to create new perspectives and solutions.
URL:https://ecda.eur.nl/event/ai-education-for-ict-dummies/
LOCATION:Online
CATEGORIES:Webinar
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BEGIN:VEVENT
DTSTART;TZID=Europe/Amsterdam:20220616T160000
DTEND;TZID=Europe/Amsterdam:20220616T170000
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SUMMARY:ECDA Insights Social Artificial Intelligence #6
DESCRIPTION:Presenter: Max Welz  \n\n\n\nTitle: “Identifying Periods of Careless Responding in Surveys: A Deep Learning Approach” \n\n\n\nAbstract: Rating-scale datasets collected from surveys are paramount to empirical research. However\, due to deficiencies in survey design or lack of motivation\, some respondents may not comply with the instructions of the survey’s questions. This phenomenon is known as careless responding. Careless responding has been identified as a major threat to the internal validity of survey-based studies and should therefore be screened for (Huang et al.\, 2015). Existing methods for detecting careless responses are designed to identify respondents who respond carelessly throughout the survey. However\, recent work suggests that the longer a survey takes\, the higher the likelihood that a large proportion of all respondents will eventually start responding carelessly (Bowling et al.\, 2021). Thus\, we are interested in identifying when a respondent becomes careless (if at all) rather than trying to detect respondents who respond carelessly throughout the survey. Correspondingly\, we propose a novel method for identifying the periods of carelessness (or a lack thereof) of each respondent. The proposed method uses the deep learning technique of auto-associative neural networks (autoencoders) in combination with response times. By means of extensive numerical experiments\, we find that our proposed method achieves high reliability in correctly identifying periods of careless responding and discriminates well between careless and regular respondents. Our method seems to perform particularly well in long surveys\, which are common in psychology and health sciences\, where it is likely that a large proportion of all respondents eventually respond carelessly due to fatigue.
URL:https://ecda.eur.nl/event/ecda-insights-social-artificial-intelligence-6/
LOCATION:Hybrid. Y1-10
CATEGORIES:ECDA Insights
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