language buttonlanguage button

Clusters Arrow Cluster02


Peer productivity in Web 2.0 Environments

The development of digital media revolutionized educational and learning processes. One of the most striking transformations in this regard has emerged with the advent of Web 2.0 technologies: Learners can participate in the creation of the very resources from which they learn. But when are learners willing to actively contribute to Web 2.0 environments – and when not? This cluster investigates boundary conditions of peer productivity in Web 2.0 environments as well as the dynamics of this productivity. The cluster takes an interdisciplinary approach: Social science aspects are investigated both from a psychological and an economic perspective. Moreover, tools for the analysis of Web 2.0 data are developed by computer scientists in the cluster. Research foci are (1) the role of cognitive conflicts as drivers of peer productivity; (2) the analysis of temporal dynamics in Web 2.0 environments; (3) the spillover effects of changes in an article to other articles in a network; (4) the influence of user ratings on productivity; and (5) the development of tools for analysis that enable social scientists to explore large datasets. These aspects are investigated in three prototypical Web 2.0 contexts: online discussion forums (opinion context), question & answer forums (problem-solving context), and Wikipedia (knowledge-building context).

Spokesperson of the cluster: Dr. Jürgen Buder

Partners of the cluster:


Determinants of Peer Productivity in Online Discussion Forums

Project manager: Dr. Jürgen Buder

Although productivity is important for self-sustaining communities, very little is known about the conditions that drive productivity. Thus, this project investigates contextual and personal factors that influence productivity in controversial online discussions. Based on prior work and mostly in experimental studies, three main assumptions will be tested: Productivity will be increased by 1) the conflict between opinions expressed in a discussion and the opinion of a potential contributor, 2) social validation, reached by corresponding ratings of the community in relation to the opinion of the person, and 3) reciprocity, reached by corresponding feedback received by the community.


Collaborative Knowledge Creation in Wikipedia

Project manager: Prof. Dr. Ulrike Cress

The project investigates the relationship between conflicting views and knowledge creation during mass collaboration. The effect of balance between two systems of thinking is analyzed by testing predictions derived from the co-evolution model (Cress & Kimmerle, 2008). A medium size cognitive incongruity between an individual and a Web 2.0 collaborative artifact is expected to be optimal for the creation of new knowledge. Dynamic large-scale data from Wikipedia is evaluated employing automatic techniques of computational linguistics to categorize the changes in the point of view in a Wikipedia article and in the contributions of an author. Network analyses complement the exploration methodology.


Knowledge Spillover and Content Valuation in Online Collaboration

Project manager: Dr. Marianne Saam

This project aims at measuring spillovers that arise in the process of peer production of user-generated content. We focus on new or improved content that is generated in reaction to impulses resulting from the structure of a network. These impulses may concern contributors, who are motivated by their peers, or content items that receive greater attention because they are linked to other popular items. Such spillovers are highly relevant in the context of user-generated content but also for knowledge generation, innovation and the decentralized provision of public goods in general. Strategies for adequate measurement of spillovers and peer effects are thus also of great methodological interest.


Interactivity, Scale, and Parallelism in Domain-Expert Data Analysis

Project manager: Prof. Dr. Torsten Grust

This project's main goal is to lay expressive and efficient data analysis tools into the hands of domain experts whose background, in the context of this cluster, will be in psychology and economics (but not in computer science). We propose a declarative mode of data access in which the domain experts' focus solely remains on their objects of study. Details of data layout, order of access, indexing, or distribution do not invade that focus. The project concentrates on interactive data manipulation (instant feedback), aspects of scalability (database support) and the exploitation of parallel execution strategies (which will enable interactivity and scalability in the first place).


Buder, J. (2011). Group awareness tools for learning: Current and future directions. Computers in Human Behavior, 27, pp. 1114-1117.

Buder, J., Schwind, C., Rudat, A., & Bodemer, D. (2013). Navigating through controversial online discussions: The influence of visualized ratings. In N. Rummel, M. Kapur, M. Nathan, & S. Puntambekar (Eds.), To See the World and a Grain of Sand: Learning across Levels of Space, Time, and Scale: CSCL 2013 Conference Proceedings (Vol. I, pp. 65-72). Madison, USA: International Society of the Learning Sciences.

Cress, U., Barron, B., Halatchliyski, I., Oeberst, A., Forte, A., Resnick, M., & Collins, A. (2013). Mass collaboration - an emerging field for CSCL research. In N. Rummel, M. Kapur, N. Nathan, & S. Puntambekar (Eds.), To see the world and a grain of sand: Learning across levels of space, time and scale: CSCL 2013 Proceedings (Vol. I, pp. 557-563). Madison, USA: International Society of the Learning Sciences.

Giorgidze, G., Grust, T., Schweinsberg, N., & Weijers, J. (2011). Bringing Back Monad Comprehensions. Proceedings of the ACM SIGPLAN Haskell Symposium (Haskell 2011). Tokyo, Japan: ACM.

Giorgidze, G., Grust, T., Halatchliyski I., & Kummer M. (2013). Analysing the entire Wikipedia history with database supported haskell. In K. Sagonas (Ed.), Practical Aspects of Declarative Languages – 15th International Symposium, PADL 2013 (Vol. 7752, pp. 19-25). Berlin, Heidelberg: Springer.

Halatchliyski, I., Moskaliuk, J., Kimmerle, J., & Cress, U. (2014). Explaining authors’ contribution to pivotal artifacts during mass collaboration in the Wikipedia’s knowledge base. International Journal of Computer-Supported Collaborative Learning, 9, pp. 97-115.

Kummer, M., M. Saam, I. Halatchliyski und G. Giorgidze (2012). Centrality and Content Creation in Networks – The Case of German Wikipedia. ZEW Discussion Paper No. 12-053.

Kummer, M. (2013). Spillovers in Networks of User Generated Content – Evidence from 23 Natural Experiments on Wikipedia. ZEW Discussion Paper No. 13-098.