Barreiros Carla, Silva Nelson, Veas Eduardo Enrique, Pammer-Schindler Viktoria
2020
Dennerlein Sebastian, Wolf-Brenner Christof, Gutounig Robert, Schweiger Stefan, Pammer-Schindler Viktoria
2020
In society and politics, there is a rising interest in considering ethical principles in technological innovation, especially in the intersection of education and technology. We propose a first iteration of a theory-derived framework to analyze ethical issues in technology-enhanced learning (TEL) software development. The framework understands ethical issues as an expression of the overall socio-technical system that are rooted in the interactions of human actors with technology, so-called socio-technical interactions (STIs). For guiding ethical reflection, the framework helps to explicate this human involvement, and to elicit discussions of ethical principles on these STIs. Prompts in the form of reflection questions can be inferred to reflect on the technology functionality from relevant human perspectives, and in relation to a list of fundamental ethical principles. We illustrate the framework and discuss its implications for TEL
Gayane Sedrakya, Dennerlein Sebastian, Pammer-Schindler Viktoria, Lindstaedt Stefanie
2020
Our earlier research attempts to close the gap between learning behavior analytics based dashboard feedback and learning theories by grounding the idea of dashboard feedback onto learning science concepts such as feedback, learning goals, (socio-/meta-) cognitive mechanisms underlying learning processes. This work extends the earlier research by proposing mechanisms for making those concepts and relationships measurable. The outcome is a complementary framework that allows identifying feedback needs and timing for their provision in a generic context that can be applied to a certain subject in a given LMS. The research serves as general guidelines for educators in designing educational dashboards, as well as a starting research platform in the direction of systematically matching learning sciences concepts with data and analytics concepts
Fruhwirth Michael, Breitfuß Gert, Pammer-Schindler Viktoria
2020
The availability of data sources and advances in analytics and artificial intelligence offers the opportunity for organizationsto develop new data-driven products, services and business models. Though, this process is challenging for traditionalorganizations, as it requires knowledge and collaboration from several disciplines such as data science, domain experts, orbusiness perspective. Furthermore, it is challenging to craft a meaningful value proposition based on data; whereas existingresearch can provide little guidance. To overcome those challenges, we conducted a Design Science Research project toderive requirements from literature and a case study, develop a collaborative visual tool and evaluate it through severalworkshops with traditional organizations. This paper presents the Data Product Canvas, a tool connecting data sources withthe user challenges and wishes through several intermediate steps. Thus, this paper contributes to the scientific body ofknowledge on developing data-driven business models, products and services.
Dennerlein Sebastian, Pammer-Schindler Viktoria, Ebner Markus, Getzinger Günter, Ebner Martin
2020
Sustainably digitalizing higher education requires a human-centred approach. To address actual problems in teaching as well as learning and increase acceptance, the Technology Enhanced Learning (TEL) solution(s) must be co-designed with affected researchers, teachers, students and administrative staff. We present research-in-progress about a sandpit-informed innovation process with a f2f-marketplace of TEL research and problemmapping as well team formation alongside a competitive call phase, which is followed by a cooperative phase of funded interdisciplinary pilot teams codesigning and implementing TEL innovations. Pilot teams are supported by a University Innovation Canvas to document and reflect on their TEL innovation from multiple viewpoints.
Fadljevic Leon, Maitz Katharina, Kowald Dominik, Pammer-Schindler Viktoria, Gasteiger-Klicpera Barbara
2020
This paper describes the analysis of temporal behavior of 11--15 year old students in a heavily instructionally designed adaptive e-learning environment. The e-learning system is designed to support student's acquisition of health literacy. The system adapts text difficulty depending on students' reading competence, grouping students into four competence levels. Content for the four levels of reading competence was created by clinical psychologists, pedagogues and medicine students. The e-learning system consists of an initial reading competence assessment, texts about health issues, and learning tasks related to these texts. The research question we investigate in this work is whether temporal behavior is a differentiator between students despite the system's adaptation to students' reading competence, and despite students having comparatively little freedom of action within the system. Further, we also investigated the correlation of temporal behaviour with performance. Unsupervised clustering clearly separates students into slow and fast students with respect to the time they take to complete tasks. Furthermore, topic completion time is linearly correlated with performance in the tasks. This means that we interpret working slowly in this case as diligence, which leads to more correct answers, even though the level of text difficulty matches student's reading competence. This result also points to the design opportunity to integrate advice on overarching learning strategies, such as working diligently instead of rushing through, into the student's overall learning activity. This can be done either by teachers, or via additional adaptive learning guidance within the system.
Thalmann Stefan, Fessl Angela, Pammer-Schindler Viktoria
2020
Digitization is currently one of the major factors changing society and the business world. Most research focused on the technical issues of this change, but also employees and especially the way how they learn changes dramatically. In this paper, we are interested in exploring the perspectives of decision makers in huge manufacturing companies on current challenges in organizing learning and knowledge distribution in digitized manufacturing environments. Moreover, weinvestigated the change process and challenges of implementing new knowledge and learning processes.To this purpose, we have conducted 24 interviews with senior representatives of large manufacturing companies from Austria, Germany, Italy, Liechtenstein and Switzerland. Our exploratory study shows that decision makers perceive significant changes in work practice of manufacturing due to digitization and they currently plan changes in organizational training and knowledge distribution processes in response. Due to the lack of best practices, companies focus verymuch on technological advancements. The delivery of knowledge just-in-time directly into work practice is afavorite approach. Overall, digital learning services are growing and new requirements regarding compliance,quality management and organisational culture arise.
Kaiser Rene_DB, Thalmann Stefan, Pammer-Schindler Viktoria, Fessl Angela
2020
Organisations participate in collaborative projects that include competitors for a number of strategic reasons, even whilst knowing that this requires them to consider both knowledge sharing and knowledge protection throughout collaboration. In this paper, we investigated which knowledge protection practices representatives of organizations employ in a collaborative research and innovation project that can be characterized as a co-opetitive setting. We conducted a series of 30 interviews and report the following seven practices in structured form: restrictive partner selection in operative project tasks, communication through a gatekeeper, to limit access to a central platform, to hide details of machine data dumps, to have data not leave a factory for analysis, a generic model enabling to hide usage parameters, and to apply legal measures. When connecting each practice to a priori literature, we find three practices focussing on collaborative data analytics tasks had not yet been covered so far.