Abstract The huge and ever-increasing amount of publicly available data is shaping the data-driven society that citizens are encouraged to tame. It requires future generations, i.e., current learners, to acquire data literacy skills to make informed decisions. Towards this direction, we present a data literacy workshop involving more than $$\varvec{150}$$ 150 high school learners focused on co-creating Open Data via a digital environment and authoring data stories while evaluating their engagement and learning. Results show that participants are, on average, engaged during the in-person stages of the workshops, independently by gender, and they are mainly interested in collaborative activities, hands-on, and public presentations. This experiment confirm that learning is positively correlated with engagement, which aligns with the literature. However, further efforts should be invested in letting learners master data literacy skills while increasing their interest.

In this Work-in-Progress paper we present a preliminary study and the design of a taxonomy of Tools, Languages and Environments (TLE) employed in K-12 Computing Education, in schools and outreach programs. The research provides an analytical classification model based on a survey of TLEs and on previous related works. It also outlines the collaborative protocol that will allow researchers to share the results of the taxonomy on a public repository. An analysis of the most common platforms where contributors can work collaboratively is presented to show the qualitative process of identification and choice of GitHub as the most reliable one.

Summary The cloud computing paradigm has emerged as the backbone of modern price‐aware scalable computing systems. Many cloud service models are competing to become the leading doorway to access the computational power of cloud providers. Recently, a novel service model, called function‐as‐a‐service (FaaS), has been proposed, which enables users to exploit the cloud computational scalability, left out the configuration and management of huge computing infrastructures. This article discloses Fly, a domain‐specific language, which aims at reconciling cloud and high‐performance computing paradigms adopting a multicloud strategy by providing a powerful, effective, and pricing‐efficient tool for developing scalable workflow‐based scientific applications by exploiting different and at the same time FaaS cloud providers as computational backends in a transparent fashion. We present several improvements of the Fly language, as well as a new enhanced version of a source‐to‐source compiler, which currently supports Symmetric Multiprocessing, Amazon AWS, and Microsoft Azure backends and translation of functions in Java, JavaScript, and Python programming languages. Furthermore, we discuss a performance evaluation of Fly on a popular benchmark for distributed computing frameworks, along with a collection of case studies with an analysis of their performance results and costs.

This paper describes Distributed MASON, a distributed version of the MASON agent-based simulation tool. Distributed MASON is architected to take advantage of well known principles from Parallel and Discrete Event Simulation, such as the use of Logical Processes (LP) as a method for obtaining scalable and high performing simulation systems. We first explain data management and sharing between LPs and describe our approach to load balancing. We then present both a local greedy approach and a global hierarchical approach. Finally, we present the results of our implementation of Distributed MASON on an instance in the Amazon Cloud, using several standard multi-agent models. The results indicate that our design is highly scalable and achieves our expected levels of speed-up.

Simulation models are becoming an increasingly popular tool for the analysis and optimization of complex real systems in different fields. Finding an optimal system design requires performing a large parameter sweep. In this paper, we present the design of SOF (Simulation Optimization and exploration Framework on the cloud), a framework which exploits the computing power of a cloud computational environment in order to realize effective and efficient simulation optimization strategies. SOF offers several attractive features: SOF requires "zero configuration" as it does not require any additional software installed on the remote node, SOF is transparent to the user, since the user is totally unaware that system operates on a distributed environment, SOF is highly customizable and programmable, since it enables the running of different simulation optimization scenarios on different simulation toolkits. The tool has been fully developed and is available on a public repository under the Apache public licence.

Agent-Based Models (ABMs) are a class of models which, by simulating the behavior of multiple agents (i.e., ndependent actions, interactions and adaptation), aim to emulate and/or predict complex phenomena. One of the general features of ABM simulations is their experimental capacity, that requires a viable and reliable infrastructure to interact with a running simulation, monitoring its behaviour, as it proceeds, and applying changes to the configurations at run time, (the computational steering) in order to study "what if" scenarios.