In an increasingly data-driven world, a core set of data literacy skills are needed to enable individuals and society collectively to make informed decisions. In this experience report we present the Toolkit for Educators of Data Science, a novel combination of hardware and software that empowers children in K-12 to experience the data science lifecycle by collecting, analysing, and visualising their own local environmental data. Through exploratory studies in two classrooms involving five teachers and 100 children aged 7 to 11, we experienced the following. (1) Collecting live data from the local environment with our tools provided children with high levels of agency, engagement and focus, (2) Educators stated how using the technology gave the data purpose and gave students a new perception and experience of computing; data started to have meaning because of the real world application across the curriculum. (3) The offline, standalone design of our toolkit empowers teachers by overcoming practical issues often associated with delivering data science lessons in schools.

Generative AI technologies demand new practical and critical competencies, which call on design to respond to and foster these. We present an exploratory study guided by Research-through-Design, in which we partnered with a primary school to develop a constructionist curriculum centered on students interacting with a generative AI technology. We provide a detailed account of the design of and outputs from the curriculum and learning materials, finding centrally that the reflexive and prolonged ‘hands-on’ approach led to a co-development of students’ practical and critical competencies. From the study, we contribute guidance for designing constructionist approaches to generative AI technology education; further arguing to do so with ‘critical responsivity.’ We then discuss how HCI researchers may leverage constructionist strategies in designing interactions with generative AI technologies; and suggest that Research-through-Design can play an important role as a ‘rapid response methodology’ capable of reacting to fast-evolving, disruptive technologies such as generative AI.

This paper presents the ongoing development of the clip:bit, a clipboard-style micro:bit V2 extension for biodiversity monitoring in schools and community groups. The clip:bit is one component in a wider system to enable schools to engage in longitudinal studies of biodiversity in school grounds and local areas, monitoring the impact of landscape management and children's ecological activities, such as the introduction wildflower meadows or planting hedgerows. The clip:bit was designed in response to needs identified during fieldwork in schools. We describe how the design addresses these needs and point to the way the clip:bit fits into a wider plan for data management and data visualisation. We also discuss the application of an existing micro:bit peripheral, the Kitronik Game Controller, for biodiversity monitoring and compare their use for different cases.

Providing a good understanding to children and educators on the Internet of Things (IoT) means to make them aware about where the data goes, how it is stored, and what it is stored on. In this perspective many commercial IoT systems have been shown to be unsuitable for this purpose especially when used in an educational context. They do not create user centric data collection opportunities; many of their IoT sensors that send data offsite to an online cloud create gaps in knowledge; the sensors themselves are not transparent: it's not clear what data they are collecting and how, and they are not easily compatible with school networks. Classroom Cloudlet addresses this issue. This demonstration presents an end-to-end IoT like system that includes a mobile, tangible, and transparent classroom cloudlet. Classroom Cloudlet aims to allow data from multiple devices to be easily shared, collated and analysed without using the Internet, but while still educating students about IoT and cloud concepts. The classroom cloudlet aims to be a physical representation of a cloud in an IoT system; visualise the movement of data around the system; provide a web front-end for students to view and create custom visualisations of their data. Classroom Cloudlet aims to gives the educator and the children full control and ownership of their data.

In this paper we unpack the theory and application of a pedagogy for environmental care and the place of digital technologies and interaction design within this pedagogy. The paper illustrates its application using exemplar projects, primarily drawn from primary education. We draw attention to the role of digital education in a place-based curriculum and discuss how teaching through local environments can contribute to understanding of global environmental issues and active participation locally and globally.

Should programming languages use natural-language-like syntax? Under what circumstances? What sorts of errors do novice programmers make? Does using a natural- language-like programming language lead to user errors? In this study, we read the entire online interactions of sixteen children who issued a total of 35,047 commands on MOOSE Crossing, an educational MUD for children, We counted and categorized the errors made. A total d 2,970 errors were observed. We define natural-language errors as those errors in which the user failed to distinguish between English and code, issuing an incorrect command that was more English-like than the correct one. A total of 314 natural-language errors were observed. In most of those errors, the child was able to correct the problem either easily (41.1% of the time) or with some effort (20.7%). Natural-language errors were divided into five categories. In order from most to least frequent, they are: syntax errors, guessing a command name by supplying an arbitrary English word, literal interpretation of metaphor, assuming the system is keeping more state information than is actually the case, and errors of operator precedence and combination. We believe that these error rates are within acceptable limits, and conclude that leveraging users natural-language knowledge is for many applications an effective strategy for designing end-user-programming languages.