Christoph Greven

What Should I Read Next? A Personalized Visual Publication Recommender System

Discovering relevant publications for researchers is a non-trivial task. Recommender systems can reduce the effort required to find relevant publications. We suggest using a visualization- and user-centered interaction model to achieve both a more trusted recommender system and a system to understand a whole research field. In a graph-based visualization papers are aligned with their keywords according to the relevance of the keywords. Relevance is determined using text-mining approaches. By letting the user control relevance thresholds for individual keywords we have designed a recommender system that scores high in accuracy (\(\bar{x}=5.03/6\)), trust (\(\bar{x}=4.31/6\)) and usability (SUS \(\bar{x}=4.89/6\)) in a user study, while at the same time providing additional information about the field as a whole. As a result, the inherent trust issues conventional recommendation systems have seem to be less significant when using our solution.

Context-Aware Mobile Professional Learning in PRiME

Technology Enhanced Learning (TEL) in professional and organizational settings is increasingly gaining importance. The high availability of mobile end devices and their ability to support learning across contexts open up new perspectives for effective professional learning and knowledge management. The BMBF project Professional Reflective Mobile Personal Learning Environments (PRiME) addresses the challenge of mobile learning in context and realizes a seamless learning framework which connects learning and work processes. PRiME enables the mobile professional learner to harness implicit knowledge and supports continuous knowledge creation and reflection at three different layers: the personal learning environment (PLE), the personal knowledge network (PKN), and the network of practice (NoP).

What Do My Colleagues Know? Dealing with Cognitive Complexity in Organizations Through Visualizations

In order to cope with the growth of information complexity, organizations have started to implement various forms of knowledge management applications. Approaches range from file-, data-, information-centric software to information retrieval, search engines, and decision support systems. Thereby, the data presentation plays often a crucial part in making knowledge available in organizational settings. We examine two visualizations and investigate their capabilities to support organizational knowledge and their usability. One is a document-keyword centric graph-based visualization, while the other is person-institute centric. Both were evaluated positively in supporting improvement of organizational knowledge.

An Open-Source Object-Graph-Mapping Framework for Neo4j and Scala: Renesca

The usage and application of graph databases is increasing. Many research problems are based on understanding relationships between data entities. This is where graph databases are powerful. Nevertheless, software developers model and think in object-oriented software. Combining both approaches leads to a paradigm mismatch. This mismatch can be addressed by using object graph mappers (OGM). OGM adapt graph databases for object-oriented code, to relieve the developer. Most graph database access frameworks only support table-based result outputs. This defeats one of the strongest purposes of using graph databases. In order to harness both the power of graph databases and object-oriented modeling (e.g. type-safety, inheritance, etc.) we propose an open-source framework with two libraries: (1) renesca, which is a graph database driver providing graph-query-results and change-tracking. (2) renesca-magic, a macro-based ER-modeling domain specific language (DSL). Both were tested in a graph-based application and lead to dramatic improvements in code size (factor 10) and extensibility of the code, with no significant effect on performance.

Layered Knowledge Networking in Professional Learning Environments

Knowledge Management (KM) and Technology Enhanced Learning (TEL) became a very important issue in modern organizational professional learning and work process integration. Former learning and KM theories which characterize knowledge as a thing or process no longer fit todays digital world where the amount of required information is no more manageable and the half-time of knowledge in general is rapidly decreasing. Younger approaches such as the Learning as a Network (LaaN) theory describe knowledge as complex and emergent and put a heavier focus on knowledge networking. The LaaN theory further stresses the convergence of the learning and work processes in professional learning settings and views KM and TEL as two sides of the same coin. Driven by the LaaN theory, the Professional Reflective Mobile Personal Learning Environments (PRiME) project describes an integrated KM and TEL framework which connects learning and work processes. It enables the professional learner to harness implicit knowledge and offers knowledge networking at three different layers: the Personal Learning Environment (PLE), the Personal Knowledge Network (PKN) and the Network of Practice (NoP). Continuous knowledge networking results in constant evolution of knowledge leading to personal as well as organizational learning.

Lernen und Arbeiten in mobilen persönlichen Lernumgebungen

Die Anforderungen an moderne Lernende haben sich in den letzten Jahren enorm geandert, sowohl im akademischen und privaten Bereich als auch in berufsbezogenen Szenarien. Zunehmend wird auf die Lernenden selbst fokussiert, anstatt auf traditionelle Lehrformen wie Frontalunterricht zu setzen. Durch mehr Kontrolle und Selbstverantwortung obliegt die Auswahl der richtigen Lernzeit, des Ortes, der Materialien etc. dem Lernenden selbst. Auf der einen Seite kann dies durch die freie Gestaltung der personlichen Lernumgebung nach den eigenen Bedurfnissen unterstutzt werden. Auf der anderen Seite bieten die mittlerweile allgegenwartigen mobilen Endgerate wie Smartphones oder Tablets enorme Potenziale, um den flexiblen Anspruchen von allgegenwartigem Lernen gerecht zu werden. Das Projekt Professional Reflective Mobile Personal Learning Environments (PRiME; gefordert durch das Bundesministerium fur Bildung und Forschung) vereint diese Ansatze und fokussiert dabei auf mobile Mitarbeitende im beruflichen Kontext. Es verankert individuelles Lernen in Arbeitsprozessen und macht dieses Wissen fur einen globalen und organisationalen Lernprozess nutzbar. Mobile Anwendungen fur das Wissensmanagement lassen dabei Reflexion auf drei Ebenen zu: der personlichen Lernumgebung, im sozialen Netzwerk und im organisationalen Kontext. Eine flexible Anwendungsarchitektur ermoglicht dabei die individuelle Zusammenstellung der eigenen Arbeitswerkzeuge. Diese kann nach dem plug’n’play-Prinzip jederzeit erweitert oder verandert werden. Hierbei bieten Anwendungen ihre Funktionalitaten in dem Okosystem fur die Nutzung in anderen Anwendungen an, so dass einzelne Funktionen anwendungs- und damit kontextubergreifend genutzt und durch die Lernenden selbst eingebunden werden konnen. Gleichzeitig bleibt ein nahtloser, visueller Ubergang zwischen Anwendungen gewahrleistet, so dass den Nutzerinnen und Nutzern ein homogenes System gegenubersteht. Mit den mobilen Werkzeugen lasst sich vor allem Wissen im Arbeitsprozess festhalten, mit ausgewahlten Lernenden teilen, diskutieren und uberarbeiten. Die zu Grunde liegende komplexe Wissensstruktur erlaubt es, zielgenau Inhalte anzusprechen und zu erweitern. Mit diesen Ruckmeldungen ist es schlieslich mit Hilfe von Redakteurinnen und Redakteuren moglich, Lerninhalte aufzubereiten und iterativ zu verbessern sowie fur die erneute Verwendung zur Verfugung zu stellen. Die automatisierte Verteilung dieser Neuerungen schliest den kontinuierlichen Qualitatssicherungs- und Wissensevolutionskreis, so dass die Organisation schnell auf Veranderungen reagieren kann. Der gesamte Prozess wird durch diverse weitere Werkzeuge unterstutzt. Beispielsweise nutzen intelligente Suchen die vorhandenen Kontextinformationen der Lernenden, um Suchergebnisse einzuschranken und auf ihre aktuellen, situativen Bedurfnisse anzupassen. Bisherige Evaluationen zeigen eine stark ausgepragte Akzeptanz und einen hohen Mehrwert in Lernintensitat, Reflexion, Arbeitseffizienz und Kommunikation.

User Groups and Different Levels of Control in Recommender Systems

The aspect of control in recommender systems has already been extensively researched in the past. Quite a number of studies performed by various researchers reported that an increase in control had a positive effect for example on user satisfaction with a system, or recommendation accuracy. Recent studies investigated whether this positive effect of control applies to all users, or finer distinctions have to be made between different user groups, which in turn require different levels of control. Those studies identified several characteristics, along which users could be divided into groups: expertise in recommender systems, domain knowledge, trusting propensity, persistence. They reported different needs of control for different user groups. However, the effect of those characteristics has not been systematically examined with regard to all three recommendation phases introduced earlier by Pu and Zhang, namely initial preference elicitation, preference refinement, result display. This paper suggests, that for different levels of expertise and trust, different levels of control are necessary during preference elicitation, whereas persistence does not play a prevalent role in this phase. Further assumptions are made for preference refinement and result display. In addition to the three phases, context, type of information required and visualization of control methods are identified as factors influencing the request of users for control.

That’s so Meta! Usability of a Hypergraph-Based Discussion Model

Massive online communication systems such as social networks, message boards and comment sections are widely used, yet fail in conveying a diverse public opinion. Limitations of models and protocols do not allow users to precisely express their intention and to maintain a complete overview in large-scale discussions. Data-driven approaches fail as well, as they remove the nuances of human communication and use coarse representations like trends, summaries and abstract visualizations. We argue that a new discussion model and a large-scale communication protocol is needed. We evaluate the comprehensibility of a hyperedge connection in modeling arguments for online discussions. An initial mechanical turk study (\(n=200\)) revealed that 30% of the subjects intuitively considered using hyperedges. This was followed by a user study of a prototype (\(n=51\)), where 80% actively used hyperedges. Both findings were independent of user diversity factors (age, gender, graph theory knowledge). The prototypical implementation was evaluated positively.

Graph Complexity in visual recommender systems for scientific literature

Digital libraries are becoming larger, while suffering from inefficient interfaces and search patterns. Recommender Systems are a sensible and important service for users of digital libraries. The aim of recommender systems is to reduce cognitive effort, simplify search and to embed results in a larger context. In this article we compare to recommender systems – the Action Science Explorer and Papercube. Both systems are used to recommend scientific literature and use graph-based approaches. From user studies we derive the need for research to understand complexity of graphs. 1 Big Science, Big Data and the Flood of Information Visual recommender systems help to evaluate, filter and structure large amounts of digital information. They are helpful for finding relevant objects from a larger set of objects. One area of use of recommender systems is in digital libraries. According to the National Science Foundation, the rate of publishing in scientific literature increased about 2.3% from 1995 to 2005, collaborations across institutional border from 40% to 61% (Bergrström & Atkinson 2009). Therefore it is becoming increasingly important to process information in a sensible fashion and present it in interfaces that are easy to understand and that have good usability. The aim is to suggest literature from related field and to structure literature in a meaningful context. Veröffentlicht durch die Gesellschaft für Informatik e.V. 2016 in B. Weyers, A. Dittmar (Hrsg.): Mensch und Computer 2016 – Workshopbeiträge, 4. 7. September 2016, Aachen. Copyright

Seamless Integration of Knowledge Management and Professional Learning in PRiME

In an organizational context, Knowledge Management (KM) and Technology Enhanced Learning (TEL) have attracted attention over the past years and are meanwhile important tasks to increase competitive advantages of an organization. In practice, however, KM and TEL fields have evolved down separate paths. In contrast to former KM and TEL theories which characterize knowledge as a thing or process, the Learning as a Network (LaaN) theory views knowledge as a personal network. LaaN provides the theoretical foundation for the seamless integration of KM and TEL into one solution for the purpose of increased individual and organizational learning. Driven by the LaaN theory, the Professional Reflective Mobile Personal Learning Environments (PRiME) project aims at the convergence of KM and TEL by following a knowledge-as-a-network approach. PRiME enables the professional learner to harness tacit knowledge and offers continuous knowledge networking at three different layers: the Personal Learning Environment (PLE), the Personal Knowledge Network (PKN) and the Network of Practice (NoP). Continuous knowledge networking results in constant evolution of knowledge leading to personal as well as organizational learning.