This paper tracks a debate that occurred, first, within the field of Ubiquitous Computing but quickly spread to CHI and beyond, in which design scholars argued that seamlessness had long been an implicit and privileged design virtue, often at the expense of seamfulness. Seamless design emphasizes clarity, simplicity, ease of use, and consistency to facilitate technological interaction. Seamful design emphasizes configurability, user appropriation, and revelation of complexity, ambiguity or inconsistency. Here we review these literatures together and argue that, rather than rival approaches, seamful and seamless design are complements, each emphasizing different aspects of downstream user agency. Ultimately, we situate this debate within the larger, perennial discussion about the strategic revelation and concealment of human and technological operations and therein the role of design. CCS CONCEPTS • Human-centered computing → HCI theory, concepts and models.
Since the turn of the 21st century, we have seen a surge of studies on the state of U.S. education addressing issues such as cost, graduation rates, retention, achievement, engagement, and curricular outcomes. There is an expectation that graduates should be able to enter the workplace equipped to take on complex and "messy" or ill-structured problems as part of their professional and everyday life. In the context of online learning, we have identified two key issues that are elusive (hard to capture and make visible): learning with ill-structured problems and the interaction of social and individual learning. We believe that the intersection between learning and analytics has the potential, in the long-term, to minimize the elusiveness of deep learning. A proposed analytics model is described in this article that is meant to capture and also support further development of a learner's reflective sensemaking.
Data availability challenges the management of small-scale fisheries in large river basins. One way to circumvent the challenges of data collection is to rely on local stakeholders who are well-positioned to collect data that can inform management through community-based monitoring (CBM). Although science and management has increasingly considered opportunities for community involvement in scientific research, the efficacy of these programs are rarely assessed. We describe a current CBM initiative in the Kuskokwim River Basin of western Alaska. We then explore how existing approaches for incorporating local involvement in fisheries research and management measure against claims made by CBM programs to understand pathways for data utility for decision makers and approaches to capacity building and meaningful engagement of local citizens. We identify major gaps in the CBM literature and explore one of these gaps through an interview-based study of public participation in the Kuskokwim. We find that the CBM program intent to collect high quality data was complemented by increasing trust in data stewards. Ultimately, through our interview findings we illustrate how definitions of local engagement differ, how CBM data is used by decision makers, and how trust in data is dependent on trust in data stewards and the infrastructure that supports that stewardship.
Current-generation assessment tools used in K-12 and post-secondary education are limited in the type of questions they support; this limitation makes it difficult for instructors to navigate their assessment engines. Furthermore, the question types tend to score low on Bloom's Taxonomy. Dedicated learning management systems (LMS) such as Blackboard, Moodle and Canvas are somewhat better than informal tools as they offer more question types and some randomization. Still, question types in all the major LMS assessment engines are limited. Additionally, LMSs place a heavy burden on teachers to generate online assessments. In this study we analyzed the top three LMS providers to identify inefficiencies. These inefficiencies in LMS design, point us to ways to ask better questions. Our findings show that teachers have not adopted current tools because they do not offer definitive improvements in productivity. Therefore, we developed LiquiZ, a design for a next-generation assessment engine that reduces user effort and provides more advanced question types that allow teachers to ask questions that can currently only be asked in one-on-one demonstration. The initial LiquiZ project is targeted toward STEM subjects, so the question types are particularly advantageous in math or science subjects.
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