Most of this course treats interaction as a conversation between one person and one system. This topic widens the frame. Computer-Supported Cooperative Work (CSCW) studies how groups of people coordinate, communicate, and share artifacts through technology, and groupware frameworks are the tools built to support that work: shared documents, version control, notebooks, messaging, awareness cues, and handoff mechanisms. Alongside CSCW, this topic covers advanced interface paradigms — immersive and spatial displays, mixed reality, mobile and embedded interaction — plus the question of how much a system should automate and how much the human should oversee.
These ideas matter here because the design principles you have been learning do not disappear when the context gets more complex; they get harder to apply. Shared workspaces multiply the number of mental models in play, immersive displays change what information can be presented and how attention is divided, and automation shifts the user's role from doing to monitoring. Advanced technology does not excuse bad task analysis, confusing feedback, or untested prototypes — it raises the cost of getting them wrong.
The three videos below are short conference presentations from ACM SIGCHI, the professional community that produces much of the research in this field. As you watch, listen for how each team defines the collaborative setting, what methods they used to study it, and what design implications they draw. Pay attention to the vocabulary too — terms like teamwork versus taskwork, awareness, workflow, and research agenda are standard in this literature and will help you read further on your own.
A short CSCW '20 presentation from Dakuo Wang of IBM Research on how data science workers actually collaborate in practice. Based on the title and venue, it likely describes the distinct roles people occupy on data science teams, the workflows that connect them, and the tools they use to hand off work and stay coordinated. Expect an empirical, interview- or survey-based framing rather than a tool demo.
It is a direct, accessible entry point to CSCW and groupware: real teams, real handoffs, and the gaps between the tools people have and the coordination they need. Coming from the flagship CSCW conference, it also models how collaboration research is framed and reported.
A CSCW conference talk on collaborative immersive analytics — groups working together with data inside immersive or shared visual environments. The title's question, "Is It My Turn?", signals a focus on turn-taking and coordination, and the talk distinguishes teamwork (how members coordinate with each other) from taskwork (the analytic work itself). Look for how the researchers measured or assessed these two dimensions.
It connects CSCW directly to advanced interface paradigms and display technologies, showing how immersive presentation changes group coordination rather than simply making data prettier. The teamwork/taskwork distinction is a useful analytical lens you can carry into your own group-system designs.
A SIGCHI presentation proposing a future HCI research agenda for emerging technologies in K-12 education, from Van Mechelen, Smith, and colleagues. It likely surveys technologies entering classrooms — which may include AI, data-driven tools, and new interaction modalities — and identifies open questions and design challenges for researchers and practitioners. The framing is agenda-setting rather than reporting a single study.
It covers the emerging trends and future directions subtopic explicitly, and it grounds abstract speculation in a concrete setting with real stakeholders, oversight concerns, and constraints. It also shows students what a research agenda looks like, which is useful if you are choosing a project direction.
These videos extend the module's core outcomes into multi-user and advanced contexts. Discussions of workflows and role handoffs in the data science talk give you a realistic target for Hierarchical Task Analysis (MO3), since a collaborative goal must be decomposed across people as well as steps. The immersive analytics talk makes mental models and memory constraints concrete (MO1, MO2): when several collaborators hold different models of a shared space, and when immersive displays load attention and working memory, systematic coordination errors follow. All three presentations also model evaluation practice (MO4) by showing how researchers study real users in real settings and report findings to a professional audience, and the design implications and open questions they raise are the kind of input that should shape a low-to-mid fidelity prototype (MO5) before it is built.
These videos were chosen from YouTube on October 3, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.