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Community + Culture features practitioner perspectives on designing technologies for and with communities. We highlight compelling projects and provocative points of view that speak to both community technology practice and the interaction design field as a whole.--- Sheena Erete, Editor 

The importance of considering local context and partnering with target users is well established in co-design. Less common is an examination of the adaptations needed when deploying the same co-design program across heterogenous settings to maximize program efficacy and equity. We report on our experience co-designing educational games with six culturally and socioeconomically diverse afterschool sites over two years, and insights from interviewing ten program administrators across all sites. We found that even within the same afterschool program network, site differences in organizational culture and resources impacted the effectiveness of co-design programs, the co-design output, and expectations for student engagement. We characterize our afterschool partners into different archetypes – Safe Havens, Recreation Centers, Homework Helpers, and STEM Enrichment Centers. We provide recommendations for conducting co-design at each archetype and reflect on strategies for increasing equitable partnerships between researchers and afterschool centers. 

Community-based afterschool programs are valuable spaces for researchers to codesign technologies with direct relevance to local communities. However, afterschool programs differ in resources available, culture, and student demographics in ways that may impact the efficacy of the codesign process and outcome. We ran a series of multi-week educational game codesign workshops across five programs over twenty weeks and found notable differences, despite deploying the same protocol. Our findings characterize three types of programs: Safe Havens, Recreation Centers, and Homework Helpers. We note major differences in students' patterns of participation directly influenced by each program's culture and expectations for equitable partnerships and introduce Comparative Design-Based Research (cDBR) as a beneficial lens for codesign. 

Fostering equal design partnerships in adult-child codesign interactions is a well-documented challenge in HCI. It is assumed that adults come into these interactions with power and have to make adjustments to allow childrens’ input to be equally valued. However, power is not a unilateral construct - it is in part determined by social and cultural norms that often disadvantage minoritized groups. Striving for equal partnership without centering users’ and participants’ intersectional identities may lead to unproductive adult-child codesign interactions. We codesigned a game, primarily facilitated by a black woman researcher, with K-5 afterschool programs comprised of students from three different communities – a middle-class, racially diverse community; a low-income, primarily African American community; and a working-class rural, white, community over a period of 20 weeks. We share preliminary insights on how racial and gender biases affect codesign partnerships and describe future research plans to modify our program structure to foster more effective adult-child interactions. 

Digital games featuring programmable agents are popular tools for teaching coding and computational thinking skills. However, today's games perpetuate an arguably obsolete relationship between programmable agents and human operators. Borrowing from the field of human-robotics interaction, we argue that collaborative robots, or cobots, are a better model for thinking about computational agents, working directly with humans rather than in place of or at arm's length from them. In this paper, we describe an initial design inquiry into the design of “cobot games”, programmable agent scenarios in which players program an in-game ally to assist them in accomplishing gameplay objectives. We detail three questions that emerged out of this exploration, our present thinking on them, and plans for deepening inquiry into cobot game design moving forward. 

Intelligent tutoring systems (ITSs) have consistently been shown to improve the educational outcomes of students when used alone or combined with traditional instruction. However, building an ITS is a time-consuming process which requires specialized knowledge of existing tools. Extant authoring methods, including the Cognitive Tutor Authoring Tools' (CTAT) example-tracing method and SimStudent's Authoring by Tutoring, use programming-by-demonstration to allow authors to build ITSs more quickly than they could by hand programming with model-tracing. Yet these methods still suffer from long authoring times or difficulty creating complete models. In this study, we demonstrate that Simulated Learners built with the Apprentice Learner (AL) Framework can be combined with a novel interaction design that emphasizes model transparency, input flexibility, and problem solving control to enable authors to achieve greater model completeness in less time than existing authoring methods. 

A wide variety of design strategies, tools, and processes are used across the game industry. Prior work has shown that these processes are often collaborative, with experts in different domains contributing to different parts of the whole. However, the ways in which these professionals give and receive peer feedback have not yet been studied in depth. In this paper we present results from interviews with industry professionals at two game studios, describing the ways they give feedback. We propose a new, six step process that describes the full feedback cycle from making plans to receive feedback to reflecting and acting upon that feedback. This process serves as a starting point for researchers studying peer feedback in games, and allows for comparison of processes across different types of studios. It will also help studios formalize their understanding of their own processes and consider alternative processes that might better fit their needs. 

Computational models of learning can be powerful tools to test educational technologies, automate the authoring of instructional software, and advance theories of learning. These mechanistic models of learning, which instantiate computational theories of the learning process, are capable of making predictions about learners’ performance in instructional technologies given only the technology itself without fitting any parameters to existing learners’ data. While these so call “zero-parameter” models have been successful in modeling student learning in intelligent tutoring systems they still show systematic deviation from human learning performance. One deviation stems from the computational models’ lack of prior knowledge—all models start off as a blank slate—leading to substantial differences in performance at the first practice opportunity. In this paper, we explore three different strategies for accounting for prior knowledge within computational models of learning and the effect of these strategies on the predictive accuracy of these models.