This working group is conducting a scoping review exploring how interpretivist research paradigms are articulated, applied, and represented in qualitative research in computer science education. Despite the prevalence of qualitative research in our field slowly growing, interpretivist assumptions remain inconsistently described and understood; the direction of influence between this inconsistency and enduring debates over which paradigms are endorsed in our field remains unclear.

With the ubiquity of computing technologies, adolescents are increasingly affected by algorithmic biases. While previous work provides insight into adolescents’ perceptions of algorithmic bias, few provide guidance on how to engage adolescents in discourse on algorithmic bias that prioritizes both their agency and safety. To address this, we developed and conducted group discussions and design activities based on three scenarios of algorithmic bias with 15 adolescents of color (ages 15-17) in a summer academic program in the United States targeted at students from families with low-income backgrounds or who would be the first in their family to pursue post-secondary education. When sensemaking, all participants considered factors beyond the scenarios, using their situated knowledge to contextualize perceptions of unfairness. They also considered sources of bias and impacts of unfairness at different levels of individuals, communities, and society. However, when designing solutions, they tended to design for hypothetical “average users” instead of considering nuances of user populations. We offer insights for algorithmic fairness learning experiences that support situated reasoning in adolescents.

Open-ended programming engages students by connecting computing with their real-world experience and personal interest. However, such open-ended programming tasks can be challenging, as they require students to implement features that they may be unfamiliar with. Code examples help students to generate ideas and implement program features, but students also encounter many learning barriers when using them. We explore how to design code examples to support novices' effective example use by presenting our experience of building and deploying Example Helper, a system that supports students with a gallery of code examples during open-ended programming. We deployed Example Helper in an undergraduate CS0 classroom to investigate students' example usage experience, finding that students used different strategies to browse, understand, experiment with, and integrate code examples, and that students who make more sophisticated plans also used more examples in their projects.

Participatory simulations usually aim to bring simulations off screen into a shared physical space with people acting as agents in the simulation. In this paper, we describe considerations and design decisions related to creating a participatory simulation for use in learning settings with restrictions imposed due to the COVID-19 pandemic where typical classroom interactions were no longer allowed. We describe how our design decisions might help children both “dive in” and “step out” to understand more about pollinators and the prairie in spite of various restrictions on how exactly they can interact with each other. Our simulation, Buzz About, uses augmented reality in a multi-device setting to allow learners to explore the impacts of a prairie restoration on local beneficial pollinators from the perspective of a bee. We focus on supporting scenarios where the children may be in a shared space with social-distancing requirements or participating remotely, but synchronously.