In today's digital world, computing education offers critical opportunities, yet systemic inequities exclude under-represented communities, especially in rural, under-resourced regions. Early engagement is vital for building interest in computing careers and achieving equitable participation. Recent work has shown that the use of sensor-enabled tools and block-based programming can improve engagement and self-efficacy for students from under-represented groups, but these findings lack replication in diverse, resource-constrained settings. This study addresses this gap by implementing sensor-based programming workshops with rural students in Sri Lanka. Replicating methods from the literature, we conduct a between-group study (sensor vs. non-sensor) using Scratch and real-time environmental sensors. We found that students in both groups reported significantly higher confidence in programming in Scratch after the workshop. In addition, average changes in both self-efficacy and outcome expectancy were higher in the experimental (sensor) group than in the control (non-sensor) group, mirroring trends observed in the original study being replicated. We also found that using the sensors helped to enhance creativity and inspired some students to express an interest in information and communications technology (ICT) careers, supporting the value of such hands-on activities in building programming confidence among under-represented groups.

Productive Failure (PF) is a learning approach where students initially tackle novel problems targeting concepts they have not yet learned, followed by a consolidation phase where these concepts are taught. Recent application in STEM disciplines suggests that PF can help learners develop more robust conceptual knowledge. However, empirical validation of PF for programming education remains under-explored. In this paper, we investigate the use of PF to teach Python lists to undergraduate students with limited prior programming experience. We designed a novel PF-based learning activity that incorporated the unobtrusive collection of real-time heart-rate data from consumer-grade wearable sensors. This sensor data was used both to make the learning activity engaging and to infer cognitive load. We evaluated our approach with 20 participants, half of whom were taught Python concepts using Direct Instruction (DI), and the other half with PF. We found that although there was no difference in initial learning outcomes between the groups, students who followed the PF approach showed better knowledge retention and performance on delayed but similar tasks. In addition, physiological measurements indicated that these students also exhibited a larger decrease in cognitive load during their tasks after instruction. Our findings suggest that PF-based approaches may lead to more robust learning, and that future work should investigate similar activities at scale across a range of concepts.

Technology integration in educational settings has led to the development of novel sensor-based tools that enable students to measure and interact with their environment. Although reports from using such tools can be positive, evaluations are often conducted under controlled conditions and short timeframes. There is a need for longitudinal data collected in realistic classroom settings. However, sustained and authentic classroom use requires technology platforms to be seen by teachers as both easy to use and of value. We describe our development of a sensor-based platform to support science teaching that followed a 14-month design process. We share insights from this design and development approach, and report findings from a six-month large-scale evaluation involving 35 schools and 1245 students. We share lessons learnt, including that technology integration is not an educational goal per se and that technology should be a transparent tool to enable students to achieve their learning goals.

Science literacy is an increasingly important skill in the 21st century. With engagement and motivation as vital precursors to learning science, we believe introducing children to playful interactions with scientific phenomena would improve their motivations and attitudes towards science. To investigate this, we developed a tablet application where children journey in a story-driven game to capture virtual creatures by manipulating the sound measured using the built-in microphone. This game was designed with feedback from 16 children and 10 parents. In this paper, we describe the iterative design process and findings in a multi-day study with 11 more children aged between 8 and 12. Children were motivated by the game, demonstrated a strong association between sound and its behaviour in the physical world, and expressed enthusiasm to learn more in the classroom.

Knowledge of programming and computing is becoming increasingly valuable in today's world, and thus it is crucial that students from all backgrounds have the opportunity to learn. As the teaching of computing at high-school becomes more common, there is a growing need for approaches and tools that are effective and engaging for all students. Especially for students from groups that are traditionally underrepresented at university level, positive experiences at high-school can be an important factor for their future academic choices. In this paper we report on a hands-on programming workshop that we ran over multiple sessions for Maori and Pasifika high-school students who are underrepresented in computer science at the tertiary level in New Zealand. In the workshop, participants developed Scratch programs starting from a simple template we provided. In order to control the action in their programs, half of the participants used standard mouse and keyboard inputs, and the other half had access to plug-and-play sensors that provided real-time environmental data. We explore how students' perceptions of self-efficacy and outcome expectancy -- both key constructs driving academic career choices -- changed during the workshop and how these were impacted by the availability of the sensor toolkit. We found that participants enjoyed the workshop and reported improved self-efficacy with or without use of the toolkit, but outcome expectancy improved only for students who used the sensor toolkit.

Block-based programming environments are a popular way to introduce programming as they provide helpful visual cues and remove the complexities of syntax, allowing learners to focus on being creative. However, input to programs in block-based environments is often limited to the keyboard and mouse, meaning programs respond only to the direct actions of the user. Allowing programs to respond to changes in the physical environment may influence the types of programs students create and their motivation and interest towards learning programming. We explore this idea by integrating real-time sensor data into Scratch. With our platform, students simply connect a sensor to their computer via USB and use custom blocks, alongside conventional Scratch code, to read and react to the sensor data in real-time. We evaluated this approach using a field study involving 25 students with limited prior experience in Scratch. We found students were highly motivated by the sensors and expressed a strong desire to use them in future projects. Analysis of the created programs revealed that, compared to using standard input sources, students explored a wider variety of Scratch blocks when controlling sprites using the sensors.

The sense of touch offers interesting possibilities as a robust and ubiquitous communication channel. In this paper, we present OM, a tool that enables users to design subjective vibrotactile expressions associated with contextualised information relevant to them. OM consists of a pair of wrist-worn devices that can reproduce complex vibrotactile symbols and a companion editor smartphone app that allows users to create, customise and store personalised expressions. We studied OM in real-world contexts by allowing 13 participants to explore the functionalities of OM throughout their daily interactions with complete autonomy. We highlight relevant scenarios, design considerations, and future directions towards a tool that can help people unveil an alternative, ubiquitous and private communication system accessible to all.

Self-Tracking stress at work is an important aspect of stress management and is often the first step to improving mental health and personal well-being. We introduce StressShoe, a DIY toolkit for self-tracking stress and just-in-time stress interventions for office workers performing sedentary tasks. Informed by a focus group study and a pilot study, we designed and tested a toolkit that consists of a single, off-the-shelf, shoe-mounted inertial measurement unit (IMU), a machine learning model that estimates acute stress, and a companion mobile app. The mobile app allows for user-defined just-in-time interventions on the estimated stress level. To demonstrate the benefits of our system we evaluated StressShoe with 10 users over 4 weeks. We identified several effective just-in-time personalised intervention users created and found that StressShoe is easy to use and helped them reflect on their daily stressful experiences.