Higher education is tasked with preparing students for a culturally diverse and globalizing world. Additionally, western nations have an increasingly diverse student population and know the success of their students will depend in part on being able to navigate diversity. There is therefore good reason for institutions of higher learning to promote and facilitate the development of ‘global citizens’ – people who can work and relate across borders and boundaries, both real and perceived. However, teachers are not necessarily equipped to foster this learning. Many teachers are used to a reproductive way of teaching while the learning that is needed here is identity learning, directed at dialogue, internally as well as externally. This chapter proposes the potential of creative, expressive and reflective writing as a way in which personal development – a form of a reflexive internal dialogue – can be fostered to promote cultural healing and global citizenship. The writing method will be described and a case study on cultural healing in the context of Canada’s reconciliation efforts with Aboriginal people will be used to illustrate the learning process involved. The processes of writing the self and re-narrating identity has several promising benefits for both students and teachers in higher education. First it allows us to learn more about ourselves and what blocks our learning (i.e. promotes self-reflection). Second, it allows us to change our story and our identifications and therefore choose differently (i.e. self-direction). Third, it is a companion on the road of life where we literally learn to talk and listen to ourselves and articulate the tacit knowledge that can be unearthed through narrative, journal, and poetic writing. Fourth, the method is playful and creative and although tears are frequently shed in the process, students report a great enjoyment in writing and sharing their stories with others. It is a meaningful dialogue about experience and also has the potential of promoting cultural (Lengelle, Jardine, & Bonnar, 2018) healing in the context of a very diverse student body (Banks, 2015). It also has the potential for creating new bonds in the classroom and allows teachers in higher education to engage in the difficult work of facilitating global citizenship learning. The internal dialogue described here also allows us to ‘clean up’ judgements and become aware of the need to reach out to others. Not only the actual sharing of vulnerable writing in a class or online setting shows us we are not alone, but ‘writing the self’ focuses deliberately on where we have become fearful about our own and others’ identities and allows us a learning process to unearth those things, heal them in order to reach out to others.
Self-organisation in environmental service delivery is increasingly being promoted as an alternative to centralised service delivery. This article argues that self-organised environmental service delivery must be understood in the context of legal rules, especially environmental legislation. The article’s aim is twofold: first, to understand the changing relationship between the government and citizens in self-organised service delivery, and second, to explore how self-organised environmental service delivery complies with environmental quality requirements stipulated in legislation. The empirical study focuses on wastewater management in Oosterwold, the largest Dutch urban development that experimented with self-organisation. The results show that while individual wastewater management was prioritised and implemented at scale, the applicable legal rules were not adequately considered and integrated. Consequently, the experiment led to a deterioration of water quality. The article concludes that the success or failure of self-organisation in delivering environmental services such as wastewater management critically hinges on ensuring compliance with environmental legislation.
The design of a spatial distribution structure is of strategic importance for companies, to meet required customer service levels and to keep logistics costs as low as possible. Spatial distribution structure decisions concern distribution channel layout – i.e. the spatial layout of the transport and storage system – as well as distribution centre location(s). This paper examines the importance of seven main factors and 33 sub-factors that determine these decisions. The Best-Worst Method (BWM) was used to identify the factor weights, with pairwise comparison data being collected through a survey. The results indicate that the main factor is logistics costs. Logistics experts and decision makers respectively identify customer demand and service level as second most important factor. Important sub-factors are demand volatility, delivery time and perishability. This is the first study that quantifies the weights of the factors behind spatial distribution structure decisions. The factors and weights facilitate managerial decision-making with regard to spatial distribution structures for companies that ship a broad range of products with different characteristics. Public policy-makers can use the results to support the development of land use plans that provide facilities and services for a mix of industries.
In the last decade, the automotive industry has seen significant advancements in technology (Advanced Driver Assistance Systems (ADAS) and autonomous vehicles) that presents the opportunity to improve traffic safety, efficiency, and comfort. However, the lack of drivers’ knowledge (such as risks, benefits, capabilities, limitations, and components) and confusion (i.e., multiple systems that have similar but not identical functions with different names) concerning the vehicle technology still prevails and thus, limiting the safety potential. The usual sources (such as the owner’s manual, instructions from a sales representative, online forums, and post-purchase training) do not provide adequate and sustainable knowledge to drivers concerning ADAS. Additionally, existing driving training and examinations focus mainly on unassisted driving and are practically unchanged for 30 years. Therefore, where and how drivers should obtain the necessary skills and knowledge for safely and effectively using ADAS? The proposed KIEM project AMIGO aims to create a training framework for learner drivers by combining classroom, online/virtual, and on-the-road training modules for imparting adequate knowledge and skills (such as risk assessment, handling in safety-critical and take-over transitions, and self-evaluation). AMIGO will also develop an assessment procedure to evaluate the impact of ADAS training on drivers’ skills and knowledge by defining key performance indicators (KPIs) using in-vehicle data, eye-tracking data, and subjective measures. For practical reasons, AMIGO will focus on either lane-keeping assistance (LKA) or adaptive cruise control (ACC) for framework development and testing, depending on the system availability. The insights obtained from this project will serve as a foundation for a subsequent research project, which will expand the AMIGO framework to other ADAS systems (e.g., mandatory ADAS systems in new cars from 2020 onwards) and specific driver target groups, such as the elderly and novice.
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.
Automated driving nowadays has become reality with the help of in-vehicle (ADAS) systems. More and more of such systems are being developed by OEMs and service providers. These (partly) automated systems are intended to enhance road and traffic safety (among other benefits) by addressing human limitations such as fatigue, low vigilance/distraction, reaction time, low behavioral adaptation, etc. In other words, (partly) automated driving should relieve the driver from his/her one or more preliminary driving tasks, making the ride enjoyable, safer and more relaxing. The present in-vehicle systems, on the contrary, requires continuous vigilance/alertness and behavioral adaptation from human drivers, and may also subject them to frequent in-and-out-of-the-loop situations and warnings. The tip of the iceberg is the robotic behavior of these in-vehicle systems, contrary to human driving behavior, viz. adaptive according to road, traffic, users, laws, weather, etc. Furthermore, no two human drivers are the same, and thus, do not possess the same driving styles and preferences. So how can one design of robotic behavior of an in-vehicle system be suitable for all human drivers? To emphasize the need for HUBRIS, this project proposes quantifying the behavioral difference between human driver and two in-vehicle systems through naturalistic driving in highway conditions, and subsequently, formulating preliminary design guidelines using the quantified behavioral difference matrix. Partners are V-tron, a service provider and potential developer of in-vehicle systems, Smits Opleidingen, a driving school keen on providing state-of-the-art education and training, Dutch Autonomous Mobility (DAM) B.V., a company active in operations, testing and assessment of self-driving vehicles in the Groningen province, Goudappel Coffeng, consultants in mobility and experts in traffic psychology, and Siemens Industry Software and Services B.V. (Siemens), developers of traffic simulation environments for testing in-vehicle systems.
