This paper is a case report of why and how CDIO became a shared framework for Community Service Engineering (CSE) education. CSE can be defined as the engineering of products, product-service combinations or services that fulfill well-being and health needs in the social domain, specifically for vulnerable groups in society. The vulnerable groups in society are growing, while fewer people work in health care. Finding technical, interdisciplinary solutions for their unmet needs is the territory of the Community Service Engineer. These unmet needs arise in local niche markets as well as in the global community, which makes it an interesting area for innovation and collaboration in an international setting. Therefore, five universities from Belgium, Portugal, the Netherlands, and Sweden decided to work together as hubs in local innovation networks to create international innovation power. The aim of the project is to develop education on undergraduate, graduate and post-graduate levels. The partners are not aiming at a joined degree or diploma, but offer a shared short track blended course (3EC), which each partner can supplement with their own courses or projects (up to 30EC). The blended curriculum in CSE is based on design thinking principles. Resources are shared and collaboration between students and staff is organized at different levels. CDIO was chosen as the common framework and the syllabus 2.0 was used as a blueprint for the CSE learning goals in each university. CSE projects are characterized by an interdisciplinary, human centered approach leading to inter-faculty collaboration. At the university of Porto, EUR-ACE was already used as the engineering education framework, so a translation table was used to facilitate common development. Even though Thomas More and KU Leuven are no CDIO partner, their choice for design thinking as the leading method in the post-Masters pilot course insured a good fit with the CDIO syllabus. At this point University West is applying for CDIO and they are yet to discover what the adaptation means for their programs and their emerging CSE initiatives. CDIO proved to fit well to in the authentic open innovation network context in which engineering students actively do CSE projects. CDIO became the common language and means to continuously improve the quality of the CSE curriculum.
DOCUMENT
Both Software Engineering and Machine Learning have become recognized disciplines. In this article I analyse the combination of the two: engineering of machine learning applications. I believe the systematic way of working for machine learning applications is at certain points different from traditional (rule-based) software engineering. The question I set out to investigate is “How does software engineering change when we develop machine learning applications”?. This question is not an easy to answer and turns out to be a rather new, with few publications. This article collects what I have found until now.
LINK
The traditional way of educating nature management students, in which students are trained to solve relatively simple and technical problems, is no longer sufficient. Societies are changing towards a network society, which makes nature management more complex. This asks for new competences and new learning strategies in nature management education. Therefore, VHL University of Applied Sciences started two pilots in 2012. The goal of these pilots was to create a network of lecturers and students, nature conservationists and local stakeholders to create sustainable and innovative nature management strategies withina local context. Network learning was the leading learning strategy in both these pilots. In this paper we use these pilots for an evaluation of network learning as an educational principle for higher education. The pilot will be assessed on criteria based on three perspectives: 1) the changing society, 2) educational theories and 3) a theory on learning networks. The paper results in recommendations for further use of network learning as an educational principle in general.
DOCUMENT
At the department of electrical and electronic engineering of Fontys University of Applied Sciences we are defining a real-life learning context for our students, where the crossover with regional healthcare companies and institutes is maximized. Our innovative educational step is based on openly sharing electronic designs for health related measurement modalities as developed by our students. Because we develop relevant reference designs, the cross fertilization with society is large and so the learning cycle is short.
DOCUMENT
The past two years I have conducted an extensive literature and tool review to answer the question: “What should software engineers learn about building production-ready machine learning systems?”. During my research I noted that because the discipline of building production-ready machine learning systems is so new, it is not so easy to get the terminology straight. People write about it from different perspectives and backgrounds and have not yet found each other to join forces. At the same time the field is moving fast and far from mature. My focus on material that is ready to be used with our bachelor level students (applied software engineers, profession-oriented education), helped me to consolidate everything I have found into a body of knowledge for building production-ready machine learning (ML) systems. In this post I will first define the discipline and introduce the terminology for AI engineering and MLOps.
LINK
The ever increasing technological developments and greater demands from our society for qualitative better, safer, sustainable products, processes and systems are pushing the boundaries of what is possible from an engineer’s perspective. Besides the (local) grand challenges in energy, sustainability, health and mobility the world is getting smaller due to advances in communication and digitalization. The exponential increase of complexity and data driven systems (big data) which are integrated and connected to different networks calls for rethinking and inventing new business models [1]. To stay competitive in the world OEM’s and SME’s have to develop breakthrough technological, innovative and advanced systems and processes. These changes have a major impact on engineering education. The industry needs engineers with different competences and skills to fulfil the challenges and demands mentioned earlier. Universities should follow up on these changes and can only deliver and prepare the engineers of the future by close collaboration with the high tech industry. Fontys University is fully aware of this and developed a Centre of Expertise in High Tech Systems & Materials (CoE HTSM) to close the gap between the university and industry. This CoE is a public-private cooperation where applied research, projects and educational programs for different curricula are being developed and executed. By making the industry partner and giving them a role within the university, the engineering education programs and the future engineering profile can be better aligned in a faster and more structural way.
DOCUMENT
Industrial Design Engineering [Open] Innovation (IDE) is a 3-year, English taught, VWO entry-level, undergraduate programme at The Hague University of Applied Sciences (THUAS). The IDE curriculum focuses on the fuzzy front end of (open) innovation, sustainable development, and impact in the implementation phase of product-service design. The work field of Industrial Design Engineering and Open Innovation, like many other domains, is growing increasingly more complex (Bogers, Zobel, Afuah, Almirall, Brunswicker, Dahlander, Frederiksen, Gawer, & Gruber, 2017). Not only have the roles of designers changed considerably in the last decades, they continue to do so at increasing speed. Therefore, industrial design engineering students need different and perhaps more competencies as young professionals in order to deal with this new complexity. Moreover, in our transitional society, lifelong learning takes a central position (Reekers, 2017). Students need to give their learning path direction autonomously, in accordance with their talents and interests. IDE’s Quality & Curriculum Committee (QCC) realized in 2015 there is too much new knowledge to address in a 3-year programme. Instead, IDE students need to learn how to become temporary experts in an array of topics, depending on the characteristics of each new project they do (see Textbox 1). The QCC also concluded that more than just incremental changes to the current curriculum were needed; thus, the idea for a flexible, choice-based semester approach in the curriculum was born: ‘Curriculum M’ (Modular). A co-creational approach was applied, in which teaching staff, students, alumni, prospective students, industry (including the (international) social profit sector), and educational advisors collaborated to develop a curriculum that would allow students to become not just T-shaped (wide basis, one expertise) professionals, but U- or W-shaped professionals, with strong links to other disciplines.
DOCUMENT
A B S T R A C T Background: Approximately 4 years ago a new concept of learning in practice called the ‘Learning and Innovation Network (LIN)’ was introduced in The Netherlands. To develop a definition of the LIN, to identify working elements of the LIN in order to provide a preliminary framework for evaluation, a concept analysis was conducted. Method: For the concept analysis, we adopted the method of Walker and Avant. We searched for relevant publications in the EBSCO host portal, grey literature and snowball searches, as well as Google internet searches and dictionary consults. Results: Compared to other forms of workplace learning, the LIN is in the centre of the research, education and practice triangle. The most important attributes of the LIN are social learning, innovation, daily practice, reflection and co-production. Often described antecedents are societal developments, such as increasing complexity of work, and time and space to learn. Frequently identified consequences are an attractive workplace, advancements of expertise of care professionals, innovations that endorse daily practice, improvement of quality of care and the integration of education and practice. Conclusions: Based on the results of the concept analysis, we describe the LIN as ‘a group of care professionals, students and an education representatives who come together in clinical practice and are all part of a learning and innovation community in nursing. They work together on practice-based projects in which they combine best practices, research evidence and client perspectives in order to innovate and improve quality of care and in which an integration of education, research and practice takes place’. We transferred the outcomes of the concept analysis to an input-throughput-output model that can be used as a preliminary framework for future research.
DOCUMENT
NL samenvatting: In dit verkennend onderzoek werden social engineering-aanvallen bestudeerd, vooral de aanvallen die mislukten, om organisaties te helpen weerbaarder te worden. Fysieke, telefonische en digitale aanvallen werden uitgevoerd met behulp van een script volgens de 'social engineering-cyclus'. We gebruikten het COM-B model van gedragsverandering, verfijnd door het Theoretical Domains Framework, om door middel van een enquête te onderzoeken hoe Capability, Motivational en vooral Opportunity factoren helpen om de weerbaarheid van organisaties tegen social engineering-aanvallen te vergroten. Binnen Opportunity leek sociale invloed van extra belang. Werknemers die in kleine ondernemingen werken (<50 werknemers) waren succesvoller in het weerstaan van digitale social engineering-aanvallen dan werknemers die in grotere organisaties werken. Een verklaring hiervoor zou een grotere mate van sociale controle kunnen zijn; deze medewerkers werken dicht bij elkaar, waardoor ze in staat zijn om onregelmatigheden te controleren of elkaar te waarschuwen. Ook het installeren van een gespreksprotocol over hoe om te gaan met buitenstaanders was een maatregel die door alle organisaties werd genomen waar telefonische aanvallen faalden. Daarom is het moeilijker voor een buitenstaander om toegang te krijgen tot de organisatie door middel van social engineering. Dit artikel eindigt met een discussie en enkele aanbevelingen voor organisaties, bijvoorbeeld met betrekking tot het ontwerp van de werkomgeving, om hun weerbaarheid tegen social engineering-aanvallen te vergroten. ENG abstract: In this explorative research social engineering attacks were studied, especially the ones that failed, in order to help organisations to become more resilient. Physical, phone and digital attacks were carried out using a script following the ‘social engineering cycle’. We used the COM-B model of behaviour change, refined by the Theoretical Domains Framework, to examine by means of a survey how Capability, Motivational and foremost Opportunity factors help to increase resilience of organisations against social engineering attacks. Within Opportunity, social influence seemed of extra importance. Employees who work in small sized enterprises (<50 employees) were more successful in withstanding digital social engineering attacks than employees who work in larger organisations. An explanation for this could be a greater amount of social control; these employees work in close proximity to one another, so they are able to check irregularities or warn each other. Also, having a conversation protocol installed on how to interact with outsiders, was a measure taken by all organisations where attacks by telephone failed. Therefore, it is more difficult for an outsider to get access to the organisation by means of social engineering. This paper ends with a discussion and some recommendations for organisations, e.g. the design of the work environment, to help increase their resilience against social engineering attacks. https://openaccess.cms-conferences.org/publications/book/978-1-958651-29-2/article/978-1-958651-29-2_8 DOI: 10.54941/ahfe1002203
DOCUMENT
Objectives Most complex healthcare interventions target a network of healthcare professionals. Social network analysis (SNA) is a powerful technique to study how social relationships within a network are established and evolve. We identified in which phases of complex healthcare intervention research SNA is used and the value of SNA for developing and evaluating complex healthcare interventions. Methods A scoping review was conducted using the Arksey and O’Malley methodological framework. We included complex healthcare intervention studies using SNA to identify the study characteristics,level of complexity of the healthcare interventions, reported strengths and limitations, and reported implications of SNA. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews 2018 was used to guide the reporting. Results Among 2466 identified studies, 40 studies were selected for analysis. At first, the results showed that SNA seems underused in evaluating complex intervention research. Second, SNA was not used in the development phase of the included studies. Third, the reported implications in the evaluation and implementation phase reflect the value of SNA in addressing the implementation and population complexity. Fourth, pathway complexity and contextual complexity of the included interventions were unclear or unable to access. Fifth, the use of a mixed methods approach was reported as a strength, as the combination and integration of a quantitative and qualitative method clearly establishes the results. Conclusion SNA is a widely applicable method that can be used in different phases of complex intervention research. SNA can be of value to disentangle and address the level of complexity of complex healthcare interventions. Furthermore, the routine use of SNA within a mixed method approach could yield actionable insights that would be useful in the transactional context of complex interventions.
DOCUMENT
