Theory-driven Visual Design to Support Reflective Dietary Practice via mHealth: A Design Science Approach

Design for reflection in human-computer interaction (HCI) has evolved from focusing on an abstract and outcome-driven design subject towards exposing procedural or structural reflection characteristics. Although HCI research has recognized that an individual\u27s reflection is a long-lasting, multi-layered process that can be supported by meaningful design, researchers have made few efforts to derive insights from a theoretical perspective about appropriate translation into end-user visual means. Therefore, we synthesize theoretical knowledge from reflective practice and learning and argue for a differentiation between time contexts of reflection that design needs to address differently. In an interdisciplinary design-science-research project in the mHealth nutrition promotion context, we developed theory-driven guidelines for “reflection-in-action” and “reflection-on-action”. Our final design guidelines emerged from prior demonstrations and a final utility evaluation with mockup artifacts in a laboratory experiment with 64 users. Our iterative design and the resulting design guidelines offer assistance for addressing reflection design by answering reflective practice’s respective contextual requirements. Based on our user study, we show that reflection in terms of “reflection- in-action” benefits from offering actionable choice criteria in an instant timeframe, while “reflection-on-action” profits from the structured classification of behavior-related criteria from a longer, still memorable timeframe

Managing for Learning and Impact

Over the past three years, the King Baudouin Foundation has developed a more systematic approach for the evaluation of its projects, which FSG helped codify in the KBF Project Management Guide: 'Managing for Learning and Impact'. There is a growing interest of foundations in Europe to evaluate the intended impact of their projects and programs. Foundations invest in an impact-driven philanthropy and therefore develop specific strategies, activities and tools

ORKG-Leaderboards: a systematic workflow for mining leaderboards as a knowledge graph

The purpose of this work is to describe the orkg-Leaderboard software designed to extract leaderboards defined as task–dataset–metric tuples automatically from large collections of empirical research papers in artificial intelligence (AI). The software can support both the main workflows of scholarly publishing, viz. as LaTeX files or as PDF files. Furthermore, the system is integrated with the open research knowledge graph (ORKG) platform, which fosters the machine-actionable publishing of scholarly findings. Thus, the systemsss output, when integrated within the ORKG’s supported Semantic Web infrastructure of representing machine-actionable ‘resources’ on the Web, enables: (1) broadly, the integration of empirical results of researchers across the world, thus enabling transparency in empirical research with the potential to also being complete contingent on the underlying data source(s) of publications; and (2) specifically, enables researchers to track the progress in AI with an overview of the state-of-the-art across the most common AI tasks and their corresponding datasets via dynamic ORKG frontend views leveraging tables and visualization charts over the machine-actionable data. Our best model achieves performances above 90% F1 on the leaderboard extraction task, thus proving orkg-Leaderboards a practically viable tool for real-world usage. Going forward, in a sense, orkg-Leaderboards transforms the leaderboard extraction task to an automated digitalization task, which has been, for a long time in the community, a crowdsourced endeavor