Peer reviewed paper op SEFI Engineering Education congress 2009 In engineering programs an important part of the learning process takes place in practical assignments like capstone projects, internships and co-op assignments in industry. The assignments are very divers. Students have different roles, work in different environments and the learning outcomes are not uniform. So how can the individual learning outcomes or growth competencies of the assignments be determined? To cope with this question the authors developed and implemented a method to monitor and assess the individual learning outcomes of the assignments. The method can be used to match a student to his next assignment in such a way that he can build his individual learning track. The method defines three aspects of an assignment: the role of the engineer (i.e. project leader, designer, researcher), the domain(s) of the assignment (i.e. user interface, software engineering) and a general results matrix that describes results and the level required to produce them. To manage the process learning outcomes are defined as products so project management methods can be used to plan, monitor and assess learning outcomes. Key aspects of the method are: 1. A general results matrix for engineering assignments 2. Learning outcomes that are defined as results in the matrix and these results can be assessed. 3. The results have levels so the learning outcomes can grow during the programme. 4. The method can be used to match, monitor and assess students on one assignment. 5. The method can be used to match, monitor and assess students for the entire programme. 6. The tools that are developed are based on an industry standard for project management.
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This paper reports on a case study investigating learning outcomes at the individual and organisational level of a cross-institutional innovation project based on the SOAP approach. SOAP integrates Schooling of teachers, Organisational development of schools, Action- and development-oriented research, and Professional development of teachers. The innovation project was aimed at combining teachers, student teachers, and teacher educators in an alliance to design and develop new competence-based vocational educational arrangements for pupils. An inductive qualitative analysis of 37 semi-structured interviews among the participants revealed seven main categories of individual learning outcomes: attitudes, project design and management, collaboration, action theory, teaching practice, educational principles, and developments within secondary vocational education. Three main categories of organisational learning outcomes were identified: institution-level learning, project-level learning, and combining institution-level and project-level learning. A tension was identified between the participants' individual interests in learning and personal development, and the need for organisational learning aimed at improving organisational processes.
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Integrating internationalisation in learning outcomes and assessment has long been known to be a key issue in higher education. However, getting buy-in from academics and incorporating learning outcomes into a programme’s larger internationalisation goals can present a challenge. LinkedIn: https://www.linkedin.com/in/josbeelen/
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Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. Therefore, the purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system. EquiMoves is based on the state-of-the-art inertial- and orientational-sensor system ProMove-mini from Inertia Technology B.V., a partner in this proposal. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions. Target applications and follow-ups include: - Improving horse and (disabled) rider interaction for riders of all skill levels; - Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events; - Identifying biomechanical irregularities for detecting and/or preventing injuries of horses. Topic-wise, the project is connected to “Smart Technologies and Materials”, “High Tech Systems & Materials” and “Digital key technologies”. The core consortium of Saxion University of Applied Sciences, Rosmark Consultancy and Inertia Technology will receive feedback to project progress and outcomes from a panel of international experts (Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish University of Agricultural Sciences), combining a strong mix of expertise on horse and rider biomechanics, veterinary medicine, sensor hardware, data analysis and AI/machine learning algorithm development and implementation, all together presenting a solid collaborative base for derived RAAK-mkb, -publiek and/or -PRO follow-up projects.
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.
The Northern Netherlands (NN) finds itself at the junction of all the big transitions. Digitalisation is essential to follow through with these. Considering this, our region has the potential to make sizeable progress if it can successfully roll out widespread digitalisation. As a hardcore transition economy, the NN may even join the European frontrunners and act as an example for other regions. It is from this challenge that the NN will start with the European Digital Innovation Hub (EDIH NN). We have chosen to specialise in the area of Autonomous Systems, which includes multiple digital technologies that are relevant for the four transitions in the NN: (1) Smart Agro, (2) Smart Manufacturing, (3) Life Science and Health and (4) Utilities, Built Environment and Mobility. In the first three-year EDIH NN wants to support more than 750 companies and lay the foundation for long-term support of all companies. The following building blocks for EDIH NN are: • A Brokerage network that will identify issues regarding digitalisation and relay these to Solution Providers (high TRL) and knowledge providers (low TRL). • A Test Before Invest network (test and demo facilities) comprising 20+ organisations that will invest in Autonomous Systems within their domain, and collaborate towards becoming a European testing ground. • A Smart Factory Accelerator to strengthen the digital maturity of companies. • An Empowerment programme to strengthen companies in the areas of DEP Technologies: Cyber Security and Artificial Intelligence. • An approach based on High Performance Computing to make digitalisation more accessible. • The Smart Makers Academy: A programme aimed at matching supply and demand around digital skills, based on individual learning outcomes. • A Funding Readiness programme to help companies that need to invest for their digitalisation strategy, in finding funding opportunities. • A network to stimulate supply and demand around Autonomous Systems
