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Exploring embedded computational thinking in STEM teacher education

Computational thinking (CT) has become a necessity in many professional domains. As such, scholars argue that the acquisition of CT and application should be embedded in existing school subjects. Within the CT literature, a tax-onomy distinguishes CT practices in STEM education into four categories: data related, systems thinking, modeling & simulation and computational problem solving (CPSP). Practical applications of these different categories are still limited. This paper presents three examples in which edu-cators of science teachers integrate CT within STEM con-tent knowledge using the above mentioned taxonomy. The first example applies to CPSP and data practices, the sec-ond to CPSP exclusively, the final to systems thinking and modeling & simulation. The examples provide practical insight that makes the use of CT in STEM education more tangible for practitioners.

Building a Taxonomy of Keys: a new way of analysing and creating video games

Onderzoeksrapport DELA

Onderzoek naar een passende oplossing voor de mondkapjesproblematiek waar DELA sinds het uitbreken van de Coronacrisis mee kampt.

Motives, drivers and barriers to urban upcycling: Insights from furniture upcycling in the Netherlands

With growing environmental concerns, upcycling has become an important theme in literature and practice. Upcycling can help slow and close resource cycles through product life-extension. Cities offer opportunities for upcycling initiatives and seek to tackle challenges in urban solid waste management by encouraging entrepreneurs to create value from local waste streams in urban resource centres and circular crafts centres. However, little is known about what drives urban upcycling and which barriers and drivers occur. This study explores urban upcycling in the context of the Dutch furniture industry, since The Netherlands positions itself as a ‘circular economy hotspot’ and furniture offers promising opportunities and best practices for upcycling. The analysis of 29 semi-structured interviews with experts engaged in urban upcycling reveals personal motives, drivers and barriers. Personal motives include (1) a personal purpose to ‘do good’, (2) an urge to challenge the status quo and (3) learning and inspiring by doing. Key drivers entail opportunities to (1) engage in collaborative experimentation, (2) participate in cross-sectoral local networks, (3) develop resource-based adaptive competences, (4) respond to increasing demand for upcycled products and (5) make social business activities financially viable. Key barriers perceived by upcycling experts include (1) limitations in resource availability, (2) increasing capacity requirements, (3) negative public quality perception, (4) limited marketing competences and (5) an unequal playing field. This study contributes with a comprehensive definition of urban upcycling and a structured overview of key factors that drive and constrain urban upcycling.

Charging station hogging

With a growing number of electric vehicles (EVs) on the road and charging infrastructure investments lagging, occupation of installed charging stations is growing and available charging points for EV drivers are becoming scarce. Installing more charging infrastructure is problematic from both a public(tax payers money, parking availability) and private (business case) perspective. Increasing the utilization of available charging stations is one of the solutions to satisfy the growing charging need of EV drivers and managing other stakeholders interests. Currently, in the Netherlands only 15-25% of the time connected to a public charging station is actually used for charging. The longest 4% of all sessions account for over 20% of all time connected while barely using this time for actually charging. The behaviour in which EV users stay connected to a charging station longer than necessary to charge their car is called “charging station hogging”. Using a large dataset (1.3 million sessions) on publiccharging infrastructure usage, this paper analyses the inefficient use of charging stations along three axes: where the hogging takes place (spatial), by whom (the characteristics of the user) and during which time frames (day, week and year). Using the results potential solutions are evaluated and assessed including their potential and pitfalls.

Results of the Workshop on Algorithmic Affordances in Recommender Interfaces

Algorithmic affordances are defined as user interaction mechanisms that allow users tangible control over AI algorithms, such as recommender systems. Designing such algorithmic affordances, including assessing their impact, is not straightforward and practitioners state that they lack resources to design adequately for interfaces of AI systems. This could be amended by creating a comprehensive pattern library of algorithmic affordances. This library should provide easy access to patterns, supported by live examples and research on their experiential impact and limitations of use. The Algorithmic Affordances in Recommender Interfaces workshop aimed to address key challenges related to building such a pattern library, including pattern identification features, a framework for systematic impact evaluation, and understanding the interaction between algorithmic affordances and their context of use, especially in education or with users with a low algorithmic literacy. Preliminary solutions were proposed for these challenges.