As a creative endeavor, scientific research requires inspiration, innovation, exploration, and divergent thinking. Yet, in K-12 settings, it is often viewed as rigid and formulaic. MindHive is a web-based platform designed to facilitate student-teacher-scientist partnerships in research on human behavior. Features support research phases (e.g., question finding, study design, peer review, iteration), and their creative dimensions, including exploration, expressiveness, collaboration, and enjoyment. Interviews with teachers and students who used MindHive show how learners describe their experiences as creative agents. This work illustrates how educational technologies can broaden STEM participation by being authentic to methodical and creative aspects of STEM research.

Students are often tasked in engaging with activities where they have to learn skills that are tangential to the learning outcomes of a course, such as learning a new software. The issue is that instructors may not have the time or the expertise to help students with such tangential learning. In this paper, we explore how AI-generated feedback can provide assistance. Specifically, we study this technology in the context of a constructionist curriculum where students learn about experimental research through the creation of a gamified experiment. The AI-generated feedback gives a formative assessment on the narrative design of student-designed gamified experiments, which is important to create an engaging experience. We find that students critically engaged with the feedback, but that responses varied among students. We discuss the implications for AI-generated feedback systems for tangential learning.

Open data programs have become increasingly established at national and local levels of government. While the degree of success these programs have had in achieving their objectives remains open to question, one factor that has been identified as important to any success is the role of open data intermediaries, individuals and organizations that help others to make use of open data. In this paper we investigate how people become engaged with open data, what their motivations are, and the barriers and facilitators program participants perceive with regard to using open data effectively. We interview participants from a variety of backgrounds with differing levels of experience and engagement with open data. Participants include students learning how to train others in open data techniques and tools; people who attend open data events and use open data for commercial or social benefit; and representatives from local government, municipal agencies and a civic tech non-profit. We identify pathways to successfully developing and nurturing a community of open data intermediaries, and make five recommendations for organizations planning and managing open data programs.

Memes have become ubiquitous artifacts of contemporary digital culture that integrate visual and textual components in order to communicate about a topic. They can be used as forms of visual argumentation that draw on cultural references while facilitating critical commentary that typically results in humorous and caustic dialogue. In this paper, we investigate the meme creation tool, DataMeme where middle school students explore graphs then construct GIFs using existing Gyphy GIFs and overlay their own text onto them in order to communicate about the meaning behind the data. We explore the ways the students engaged in data reasoning and their argumentation practices as they communicate through their memes. Findings from our analysis of 56 data memes and the corresponding written explanations from the students, show that data memes allow students to evaluate data claims within their broader societal implications, while also expressing personal beliefs and attitudes about data.

Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships. The 7th grade unit involved students analyzing data graphs about adolescent friendships and crafting comic narratives to convey perspectives on that data. Findings from our analysis of 33 student comics, and interviews with two teachers and four students, show that students engaged in various forms of data reasoning and social-emotional reasoning. These findings contribute an understanding of how students make sense of data about personal, everyday experiences; and how an arts-integrated curriculum can be designed to support their mutual engagement in both data and social-emotional reasoning.

Interactivity and player experience are inextricably entwined with the creation of compelling narratives for interactive digital media. Narrative shapes and buttresses many such experiences, and therefore designers must construct compelling narrative arcs while carefully considering the effects of interaction on both the story and the player. As the narrative becomes more structurally complex, due to choice-based branching and other player actions, designers need to employ commensurately capable models and visualizations to keep track of that growing complexity. However, previous models of interactive narrative have failed to fully capture interactive elements with automated, operationalized visualizations. In this paper, we describe an algorithm for automated construction of a framework-driven, graph-based representation of interactive narrative. This representation more fully and transparently models structural and interactive features of the narrative than did prior approaches. We present an initial evaluation of this representation, based on modified cognitive walkthroughs performed by interactive narrative design and research experts from our research team, and we describe the takeaways for future improvement on interactive narrative modeling and analysis.