This paper discusses challenges in assessing design students within studio model education. It reflects on the assessment methods used in the M.Sc. Digital Design, Amsterdam University of Applied Sciences, with input from an online survey targeting former students and assessors of the programme. Building on the particularities they see in this assessment process and its perceived advantages and disadvantages; we reflect on the extent to which these methods respond to the intentions for their development. Lastly, we discuss these issues in relation to the literature with the purpose of providing input to others that, like us, are in search of improved assessment tools for studio-based education.
MULTIFILE
OBJECTIVES: The INTERMED Self-Assessment questionnaire (IMSA) was developed as an alternative to the observer-rated INTERMED (IM) to assess biopsychosocial complexity and health care needs. We studied feasibility, reliability and validity of the IMSA within a large and heterogeneous international sample of adult hospital in- and outpatients, and its predictive value for health care utilization (HCU) and quality of life (QoL).METHODS: 850 participants aged 17 to 90 from 5 countries completed the IMSA and were evaluated with the IM. The following measurement properties were determined: feasibility by percentages of missing values; reliability by Cronbach's alpha; interrater agreement by intraclass correlation coefficients (ICCs); convergent validity of IMSA scores with mental health (SF-36 emotional well-being subscale and HADS), medical health (CIRS) and QoL (EQ-5D) by Spearmans rank correlations; predictive validity of IMSA scores with HCU and QoL by (generalized) linear mixed models.RESULTS: Feasibility, face validity and reliability (Cronbach's alpha 0.80) were satisfactory. ICC between IMSA and IM total scores was .78 (95% CI .75-.81). Correlations of the IMSA with the SF-36, HADS, CIRS and EQ-5D (convergent validity) were -.65, .15, .28 and -.59, respectively. The IMSA significantly predicted QoL and also HCU (emergency room visits, hospitalization, outpatient visits, and diagnostic exams) after 3 and 6 months follow-up. Results were comparable between hospital sites, in- and outpatients, and age groups.CONCLUSION: The IMSA is a generic and time-efficient method to assess biopsychosocial complexity and to provide guidance for multidisciplinary care trajectories in adult patients, with good reliability and validity across different cultures.
Assessment in higher education (HE) is often focused on concluding modules with one or more tests that students need to pass. As a result, both students and teachers are primarily concerned with the summative function of assessment: information from tests is used to make pass/fail decisions about students. In recent years, increasing attention has been paid to the formative function of assessment and focus has shifted towards how assessment can stimulate learning. However, this also leads to a search for balance between both functions of assessment. Programmatic assessment (PA) is an assessment concept in which their intertwining is embraced to strike a new balance. A growing number of higher education programmes has implemented PA. Although there is consensus about the theoretical principles that form the basis for the design of PA, programmes make various specific design choices based on these principles, fitting with their own context. This paper provides insight into the design choices that programmes make when implementing PA and into the considerations that play a role in making these design choices. Such an overview is important for research purposes because it creates a framework for investigating the effects of different design choices within PA.
Receiving the first “Rijbewijs” is always an exciting moment for any teenager, but, this also comes with considerable risks. In the Netherlands, the fatality rate of young novice drivers is five times higher than that of drivers between the ages of 30 and 59 years. These risks are mainly because of age-related factors and lack of experience which manifests in inadequate higher-order skills required for hazard perception and successful interventions to react to risks on the road. Although risk assessment and driving attitude is included in the drivers’ training and examination process, the accident statistics show that it only has limited influence on the development factors such as attitudes, motivations, lifestyles, self-assessment and risk acceptance that play a significant role in post-licensing driving. This negatively impacts traffic safety. “How could novice drivers receive critical feedback on their driving behaviour and traffic safety? ” is, therefore, an important question. Due to major advancements in domains such as ICT, sensors, big data, and Artificial Intelligence (AI), in-vehicle data is being extensively used for monitoring driver behaviour, driving style identification and driver modelling. However, use of such techniques in pre-license driver training and assessment has not been extensively explored. EIDETIC aims at developing a novel approach by fusing multiple data sources such as in-vehicle sensors/data (to trace the vehicle trajectory), eye-tracking glasses (to monitor viewing behaviour) and cameras (to monitor the surroundings) for providing quantifiable and understandable feedback to novice drivers. Furthermore, this new knowledge could also support driving instructors and examiners in ensuring safe drivers. This project will also generate necessary knowledge that would serve as a foundation for facilitating the transition to the training and assessment for drivers of automated vehicles.
Manual labour is an important cornerstone in manufacturing and considering human factors and ergonomics is a crucial field of action from both social and economic perspective. Diverse approaches are available in research and practice, ranging from guidelines, ergonomic assessment sheets over to digitally supported workplace design or hardware oriented support technologies like exoskeletons. However, in the end those technologies, methods and tools put the working task in focus and just aim to make manufacturing “less bad” with reducing ergonomic loads as much as possible. The proposed project “Human Centered Smart Factories: design for wellbeing for future manufacturing” wants to overcome this conventional paradigm and considers a more proactive and future oriented perspective. The underlying vision of the project is a workplace design for wellbeing that makes labor intensive manufacturing not just less bad but aims to provide positive contributions to physiological and mental health of workers. This shall be achieved through a human centered technology approach and utilizing advanced opportunities of smart industry technologies and methods within a cyber physical system setup. Finally, the goal is to develop smart, shape-changing workstations that self-adapt to the unique and personal, physical and cognitive needs of a worker. The workstations are responsive, they interact in real time, and promote dynamic activities and varying physical exertion through understanding the context of work. Consequently, the project follows a clear interdisciplinary approach and brings together disciplines like production engineering, human interaction design, creative design techniques and social impact assessment. Developments take place in an industrial scale test bed at the University of Twente but also within an industrial manufacturing factory. Through the human centered design of adaptive workplaces, the project contributes to a more inclusive and healthier society. This has also positive effects from both national (e.g. relieve of health system) as well as individual company perspective (e.g. less costs due to worker illness, higher motivation and productivity). Even more, the proposal offers new business opportunities through selling products and/or services related to the developed approach. To tap those potentials, an appropriate utilization of the results is a key concern . The involved manufacturing company van Raam will be the prototypical implementation partner and serve as critical proof of concept partner. Given their openness, connections and broad range of processes they are also an ideal role model for further manufacturing companies. ErgoS and Ergo Design are involved as methodological/technological partners that deal with industrial engineering and ergonomic design of workplace on a daily base. Thus, they are crucial to critically reflect wider applicability and innovativeness of the developed solutions. Both companies also serve as multiplicator while utilizing promising technologies and methods in their work. Universities and universities of applied sciences utilize results through scientific publications and as base for further research. They also ensure the transfer to education as an important leverage to inspire and train future engineers towards wellbeing design of workplaces.
Human kind has a major impact on the state of life on Earth, mainly caused by habitat destruction, fragmentation and pollution related to agricultural land use and industrialization. Biodiversity is dominated by insects (~50%). Insects are vital for ecosystems through ecosystem engineering and controlling properties, such as soil formation and nutrient cycling, pollination, and in food webs as prey or controlling predator or parasite. Reducing insect diversity reduces resilience of ecosystems and increases risks of non-performance in soil fertility, pollination and pest suppression. Insects are under threat. Worldwide 41 % of insect species are in decline, 33% species threatened with extinction, and a co-occurring insect biomass loss of 2.5% per year. In Germany, insect biomass in natural areas surrounded by agriculture was reduced by 76% in 27 years. Nature inclusive agriculture and agri-environmental schemes aim to mitigate these kinds of effects. Protection measures need success indicators. Insects are excellent for biodiversity assessments, even with small landscape adaptations. Measuring insect biodiversity however is not easy. We aim to use new automated recognition techniques by machine learning with neural networks, to produce algorithms for fast and insightful insect diversity indexes. Biodiversity can be measured by indicative species (groups). We use three groups: 1) Carabid beetles (are top predators); 2) Moths (relation with host plants); 3) Flying insects (multiple functions in ecosystems, e.g. parasitism). The project wants to design user-friendly farmer/citizen science biodiversity measurements with machine learning, and use these in comparative research in 3 real life cases as proof of concept: 1) effects of agriculture on insects in hedgerows, 2) effects of different commercial crop production systems on insects, 3) effects of flower richness in crops and grassland on insects, all measured with natural reference situations
