Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through a coach dashboard. The aim of this paper is to describe the design of a coach dashboard based on scientific knowledge, user requirements, and (sensor) data to support decision making of coaches for athlete development in cyclic sports. The design process involved collaboration with coaches, embedded scientists, researchers, and IT professionals. A classic design thinking process was used to structure the research activities in five phases: empathise, define, ideate, prototype, and test phases. To understand the user requirements of coaches, a survey (n = 38), interviews (n = 8) and focus-group sessions (n = 4) were held. Design principles were adopted into mock-ups, prototypes, and the final coach dashboard. Designing a coach dashboard using the co-operative research design helped to gain deep insights into the specific user requirements of coaches in their daily training practice. Integrating these requirements, scientific knowledge, and functionalities in the final coach dashboard allows the coach to make data-informed decisions on training prescription and optimise athlete development.
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De meest gebruikte opbouw in business intelligence, predictive analitics en analytics modellen is de moeilijkheidsgraad: 1) descriptive, 2) diagnostic, 3) predictive en 4) prescriptive. Deze schaal vertelt iets over de volwassenheid van het gebruik van data door de organisatie. Een model dat niet op zichzelf staat en een achterliggende methode kent is de data driehoek van EDM (Figuur 1), welke in dit artikel zal worden toegelicht.
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A promising contribution of Learning Analytics is the presentation of a learner's own learning behaviour and achievements via dashboards, often in comparison to peers, with the goal of improving self-regulated learning. However, there is a lack of empirical evidence on the impact of these dashboards and few designs are informed by theory. Many dashboard designs struggle to translate awareness of learning processes into actual self-regulated learning. In this study we investigate a Learning Analytics dashboard based on existing evidence on social comparison to support motivation, metacognition and academic achievement. Motivation plays a key role in whether learners will engage in self-regulated learning in the first place. Social comparison can be a significant driver in increasing motivation. We performed two randomised controlled interventions in different higher-education courses, one of which took place online due to the COVID-19 pandemic. Students were shown their current and predicted performance in a course alongside that of peers with similar goal grades. The sample of peers was selected in a way to elicit slight upward comparison. We found that the dashboard successfully promotes extrinsic motivation and leads to higher academic achievement, indicating an effect of dashboard exposure on learning behaviour, despite an absence of effects on metacognition. These results provide evidence that carefully designed social comparison, rooted in theory and empirical evidence, can be used to boost motivation and performance. Our dashboard is a successful example of how social comparison can be implemented in Learning Analytics Dashboards.
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Dashboard met data van de BIZ inventarisatie
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ABSTRACT Purpose: This short paper describes the dashboard design process for online hate speech monitoring for multiple languages and platforms. Methodology/approach: A case study approach was adopted in which the authors followed a research & development project for a multilingual and multiplatform online dashboard monitoring online hate speech. The case under study is the project for the European Observatory of Online Hate (EOOH). Results: We outline the process taken for design and prototype development for which a design thinking approach was followed, including multiple potential user groups of the dashboard. The paper presents this process's outcome and the dashboard's initial use. The identified issues, such as obfuscation of the context or identity of user accounts of social media posts limiting the dashboard's usability while providing a trade-off in privacy protection, may contribute to the discourse on privacy and data protection in (big data) social media analysis for practitioners. Research limitations/implications: The results are from a single case study. Still, they may be relevant for other online hate speech detection and monitoring projects involving big data analysis and human annotation. Practical implications: The study emphasises the need to involve diverse user groups and a multidisciplinary team in developing a dashboard for online hate speech. The context in which potential online hate is disseminated and the network of accounts distributing or interacting with that hate speech seems relevant for analysis by a part of the user groups of the dashboard. International Information Management Association
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Athlete development depends on many factors that need to be balanced by the coach. The amount of data collected grows with the development of sensor technology. To make data-informed decisions for training prescription of their athletes, coaches could be supported by feedback through a coach dashboard. The aim of this paper is to describe the design of a coach dashboard based on scientific knowledge, user requirements, and (sensor) data to support decision making of coaches for athlete development in cyclic sports. The design process involved collaboration with coaches, embedded scientists, researchers, and IT professionals. A classic design thinking process was used to structure the research activities in five phases: empathise, define, ideate, prototype, and test phases. To understand the user requirements of coaches, a survey (n = 38), interviews (n = 8) and focus-group sessions (n = 4) were held. Design principles were adopted into mock-ups, prototypes, and the final coach dashboard. Designing a coach dashboard using the co-operative research design helped to gain deep insights into the specific user requirements of coaches in their daily training practice. Integrating these requirements, scientific knowledge, and functionalities in the final coach dashboard allows the coach to make data-informed decisions on training prescription and optimise athlete development.
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Dit project poogt een bijdrage te leveren aan het versterken van “de kennisketen van de gastvrijheidseconomie” middels de volgende projectdoelstellingen: • SWOT-analyse van huidige situatie, vanuit verschillende stakeholderperspectieven: kijkend vanuit de ontwikkelopgaves die men ziet, aan welke data over de customer journey is behoefte (inventarisatie)? Wat zijn de bijbehorende sterktes, zwaktes, kansen en bedreigingen (analyse)? • Versterken van de kennisketen via: hoe kunnen we kennisketen versterken met nieuwe technieken en door slim organiseren? • Een overzicht van strategische opties: welke strategische opties zijn er om 1.) sterktes te benutten om kansen te pakken en bedreigingen af te wenden en 2.) zwaktes op te lossen door kansen te pakken en gevaren te voorkomen die met bedreigingen meekomen • Input leveren voor 2.0 versie van het manifest van Gastvrij Overijssel en de beoogde oprichting van een “Data Hub” (waarvoor nog geen officiële werktitel) In de opvolgende hoofdstukken en paragrafen gaan we in op de aanpak (hoofdstuk 2) en de uitkomsten (hoofdstuk 3).
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The SDG Barometer monitors the extent to which organizations in the Netherlands are aware of, adopt and implement the UN Sustainable Development Goals and integrate them into their strategies. This Dashboard is meant to provide open access to the survey results in an interactive way, so that students, academics and organizations can benefit and learn from its insights.
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We use a randomised experiment to study the effect of offering half of 556 freshman students a learning analytics dashboard and a weekly email with a link to their dashboard, on student behaviour in the online environment and final exam performance. The dashboard shows their online progress in the learning management systems, their predicted chance of passing, their predicted grade and their online intermediate performance compared with the total cohort. The email with dashboard access, as well as dashboard use, has positive effects on student behaviour in the online environment, but no effects are found on student performance in the final exam of the programming course. However, we do find differential effects by specialisation and student characteristics.
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