Although learning analytics benefit learning, its uptake by higher educational institutions remains low. Adopting learning analytics is a complex undertaking, and higher educational institutions lack insight into how to build organizational capabilities to successfully adopt learning analytics at scale. This paper describes the ex-post evaluation of a capability model for learning analytics via a mixed-method approach. The model intends to help practitioners such as program managers, policymakers, and senior management by providing them a comprehensive overview of necessary capabilities and their operationalization. Qualitative data were collected during pluralistic walk-throughs with 26 participants at five educational institutions and a group discussion with seven learning analytics experts. Quantitative data about the model’s perceived usefulness and ease-of-use was collected via a survey (n = 23). The study’s outcomes show that the model helps practitioners to plan learning analytics adoption at their higher educational institutions. The study also shows the applicability of pluralistic walk-throughs as a method for ex-post evaluation of Design Science Research artefacts.
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Deze whitepaper is een vervolg op deze eerdere reeks over People Analytics en bespreekt de nieuwste trends. Inhoud: • Inleiding 1. Medewerkerswelzijn krijgt meer aandacht 2. HR Analytics wordt People Analytics 3. Het inzicht groeit dat People Analytics geen kant-en-klare oplossingen levert 4. De kloof tussen de vaardigheden en ambities wordt minder groot 5. Analyticsteams herbergen steeds meer expertise 6. Meer data worden gekwantificeerd 7. Steeds meer data worden van buiten de organisatie betrokken 8. Kunstmatige intelligentie kan voor onverwachte inzichten zorgen 9. Het aantal interne databronnen neemt toe 10. Er komt meer aandacht voor privacybescherming • Conclusie
Physical activity is crucial in human life, whether in everyday activities or elite sports. It is important to maintain or improve physical performance, which depends on various factors such as the amount of physical activity, the capability, and the capacity of the individual. In daily life, it is significant to be physically active to maintain good health, intense exercise is not necessary, as simple daily activities contribute enough. In sports, it is essential to balance capacity, workload, and recovery to prevent performance decline or injury.With the introduction of wearable technology, it has become easier to monitor and analyse physical activity and performance data in daily life and sports. However, extracting personalised insights and predictions from the vast and complex data available is still a challenge.The study identified four main problems in data analytics related to physical activity and performance: limited personalised prediction due to data constraints, vast data complexity, need for sensitive performance measures, overly simplified models, and missing influential variables. We proposed end investigated potential solutions for each issue. These solutions involve leveraging personalised data from wearables, combining sensitive performance measures with various machine learning algorithms, incorporating causal modelling, and addressing the absence of influential variables in the data.Personalised data, machine learning, sensitive performance measures, advanced statistics, and causal modelling can help bridge the data analytics gap in understanding physical activity and performance. The research findings pave the way for more informed interventions and provide a foundation for future studies to further reduce this gap.
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Electronic Sports (esports) is a form of digital entertainment, referred to as "an organised and competitive approach to playing computer games". Its popularity is growing rapidly as a result of an increased prevalence of online gaming, accessibility to technology and access to elite competition.Esports teams are always looking to improve their performance, but with fast-paced interaction, it can be difficult to establish where and how performance can be improved. While qualitative methods are commonly employed and effective, their widespread use provides little differentiation among competitors and struggles with pinpointing specific issues during fast interactions. This is where recent developments in both wearable sensor technology and machine learning can offer a solution. They enable a deep dive into player reactions and strategies, offering insights that surpass traditional qualitative coaching techniquesBy combining insights from gameplay data, team communication data, physiological measurements, and visual tracking, this project aims to develop comprehensive tools that coaches and players can use to gain insight into the performance of individual players and teams, thereby aiming to improve competitive outcomes. Societal IssueAt a societal level, the project aims to revolutionize esports coaching and performance analysis, providing teams with a multi-faceted view of their gameplay. The success of this project could lead to widespread adoption of similar technologies in other competitive fields. At a scientific level, the project could be the starting point for establishing and maintaining further collaboration within the Dutch esports research domain. It will enhance the contribution from Dutch universities to esports research and foster discussions on optimizing coaching and performance analytics. In addition, the study into capturing and analysing gameplay and player data can help deepen our understanding into the intricacies and complexities of teamwork and team performance in high-paced situations/environments. Collaborating partnersTilburg University, Breda Guardians.
Globalization has opened new markets to Small and Medium Enterprise (SMEs) and given them access to better suppliers. However, the resulting lengthening of supply chains has increased their vulnerability to disruptions. SMEs now recognize the importance of reliable and resilient supply chains to meet customer requirements and gain competitive advantage. Data analytics play a crucial role in developing the insights needed to identify and deal with disruptions. At the company level, this entails the development of data analytic capability, a complex socio-technical process consisting of people, technology, and processes. At the supply chain level, the complexity is compounded by the fact that multiple actors are involved, each with their own resources and capabilities. Each company’s data analytic capability, in combination with how they work together to share information and thus create visibility in the supply chain will affect the reliability and resilience of the supply chain. The proposed study therefore examines how SMEs can leverage data analytics in a way that fits with their available resources and capabilities to improve the reliability and resilience of their supply chain. The consortium for this project consists of Breda University of Applied Sciences (BUas), Logistics Community Brabant (LCB), Transport en Logistiek Nederland (TLN), Logistiek Digitaal, Kennis Transport, Smink and Devoteam. Together, the partners will develop a tool to benchmark SMEs’ progress towards developing data analytic capability that enhances the reliability of their supply chain. Interviews will be conducted with various actors of the supply chain to identify the enablers and inhibitors of using data analytics across the supply chain. Finally, the findings will be used to conduct action research with the two SMEs partners, Kennis and Smink to identify which technological tools and processes companies need to adopt to develop the use of data analytics to enhance their resilience in case of disruptions.
The value of data in general has become eminent in recent times. Autonomous vehicles and Connected Intelligent Transport Systems (C-ITS), in particular, are rapidly emerging fields that rely a lot on “big data”. Data acquisition has been an important part of automotive research and development for years even before the advent of Internet of Things (IoT). Most datalogging is done using specialized hardware that stores data in proprietary formats on traditional hard drives in PCs or dedicated managed servers. The use of Artificial Intelligence (AI) throughout the world and specifically in the automotive sector is largely reliant on the data for the development of new and reliable technologies. With the advent of IoT technologies, the reliability of data capture could be enhanced and can improve ease of real-time analytics for analysis/development of C-ITS services and Autonomous systems using vehicle data. Data acquisition for C-ITS applications requires putting together several different domains ranging from hardware, software, communication systems, cloud storage/processing, data analytics, legal and privacy aspects. This requires expertise from different domains that small and medium scale businesses usually lack. This project aims at investigating requirements that have to be met in order to collect data from vehicles. Furthermore, this project also aims at laying foundations required for the development of a unified guidelines required to collect data from vehicles. With these guidelines, businesses that intend to use vehicle data for their applications are not only guided on the technical aspects of data collection but also equally understand how data from vehicles could be harvested in a secure, efficient and responsible manner.
