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Imagine a long table covered with scattered materials – balls of wool, small wooden tiles, and pieces of clay. Around it, fifteen designers and researchers gather, quietly moving notes and connecting ideas with yarn. Each thread ties one tension to another: care to control, innovation to sustainability, agency to relation. The table becomes a web of contradictions – messy and unresolved.
This was not a design sprint or a problem-solving session. It was a collective reflection: a one-day workshop at the DIS 2025 conference named Paradoxes, Tensions and Challenges in Decentering the Human organized by the authors of this article together with Marc Hassenzahl and Anton Poikolainen Rosén. Here, we explored the paradoxes and tensions that are inherent to More-than-Human Design (MtHD). Eleven participants and the organizers shared their work. When tensions surfaced in conversations, they were noted, discussed, clustered, and physically connected.
Rather than trying to overcome these contradictions, we asked to name them – to hold them in our hands, literally, and to see what new connections might emerge. What began as a physical act of clustering became the seed for what we now call the Archive of Tensions: a growing collection of challenges in designing beyond the human.
Finanzielle Repression
(2013)
Background:
The COVID-19 pandemic has significantly accelerated the shift toward online and blended learning in higher education, placing renewed emphasis on the individualization of learning content to meet diverse student needs. Even high-quality learning materials may fail to engage learners if they do not align with students’ personal preferences and learning styles. Identifying these learner preferences, therefore, emerges as a critical challenge.
Objectives:
This paper presents ongoing work within a larger research project aimed at employing artificial intelligence to recommend optimal learning path for students in specific courses. Beyond mere optimization, the goal is to ensure the best possible fit between learning materials and individual learners.
Sample & Methods:
A total of 27 students from technical degree programs took part in this survey. All participation was voluntary, and data were handled in full compliance with GDPR regulations. Although our broader project integrates fine-grained learning analytics from Moodle, the present abstract focuses exclusively on the self-report questionnaire results. Participants completed five instruments:
1. Index of Learning Styles (ILS)
2. LIST-K (Learning and Study Strategies Inventory – Short version)
3. BFI-10 (Big Five Inventory – 10 items)
4. Custom Preferences Instrument, capturing preferences for specific learning elements (e.g. instructional videos, lecture notes, summaries) and basic demographic data
5. Motivational Value Systems Questionnaire (MVSQ), piloted last semester to assess value orientations and motivational drivers
Results:
Preliminary analyses of the questionnaire data reveal:
- Learning Styles (ILS): The majority lean toward the visual learning type (M = 5.740, SD = 3.430).
- Learning Strategies (LIST-K): High scores on metacognitive strategies (M = 3.000; SD = 0.520) and collaboration with peers (M = 3.190; SD = 0.540).
- Preferred Learning Elements: Summaries, overviews, and self-checks are most favored.
- Value Orientations (MVSQ): Students are primarily driven by the pursuit of personal achievement (M = 4.400; SD = 11.140).
Conclusion & Significance:
By integrating these five standardized questionnaires, we gain valuable insights into student learning preferences—insights that complement our Moodle analytics in the broader project. Observed trends suggest that learning materials should be concise and designed to facilitate peer interaction and knowledge deepening. These findings will guide the refinement of our AI-driven recommendation engine, enhancing its ability to deliver personalized learning paths that boost both engagement and effectiveness.
Der Begriff Business Analytics (BA) beschreibt die datengetriebene Analyse von Geschäftsprozessen. Ziel ist es, mittels IT-Einsatz und unter Anwendung mathematischer und statistischer Verfahren vergangenheits- und zukunftsorientierte Einblicke in ein Unternehmen zu erhalten um, darauf aufbauend, möglichst optimale Handlungsempfehlungen abzuleiten. Die folgenden Ausführungen sind dazu gedacht, dem Leser anhand logistischer Fragestellungen ein grundsätzliches Verständnis für den Begriff „Business Analytics“ zu vermitteln.
GHG network analysis FMCG
(2013)