2024-03-20, Jeitziner, Loris Tizian, Paneth, Lisa, Rack, Oliver, Zahn, Carmen, Wulff, Dirk

Collaborative group engagement is a key factor of success in learning groups. This work explores the development of an innovative natural language processing method for predicting collaborative group engagement. To this end, we identified linguistic markers based on an established observation-based scheme for rating collaborative group engagement, such as, semantic similarity to task instructions, verbal mimicry, sentiment, and use of jargon. We evaluated the predictive power of the linguistic markers on the data of an observational study in which 38 learning groups were instructed to perform a collaborative learning task. Overall, the data consisted of 2588 expert ratings on collaborative group engagement. We relied on machine learning to the predict collaborative group engagement ratings using informative subsets of linguistic markers. The results showed above-baseline predictive accuracy for all four dimensions of collaborative group engagement. Moreover, the analysis of feature importance points to quantity of utterances, responsiveness and uniformity of participation as the most important markers for collaborative group engagement. By harnessing natural language processing, this work extends traditional qualitative analysis and delivers nuanced quantitative metrics suitable for capturing the complexity and dynamics of contemporary Computer Supported Collaborative Learning (CSCL) environments. Thereby, it contributes to the evolving landscape of CSCL research and demonstrates the potential of novel analytic techniques to support and enrich qualitative analysis in multiple domains.

2022-07-22, Rack, Oliver, Paneth, Lisa, Jeitziner, Loris Tizian, Zahn, Carmen

In an explorative field study, we investigated nonverbal behavior and collaborative group engagement (QCGE) in online learning groups. Participants in small groups performed a hidden profile task. Results suggests differences within and between groups in their nonverbal behavior. We expect that nonverbal behaviors relate to QCGE in online learning groups.

2017, Kropp, Martin, Anslow, Craig, Mateescu, Magdalena, Burkhard, Roger, Vischi, Dario, Zahn, Carmen

2016, Zahn, Carmen, Krämer, Nicole C., Schwan, Stephan, Unz, Dagmar, Suckfüll, Monika

2023-12-31, Jeitziner, Loris Tizian, Frick, Andrea, Paneth, Lisa, Zahn, Carmen

2021-01-28, Zahn, Carmen, Paneth, Lisa

2017, Zahn, Carmen, Schwan, Stephan, Cress, Ulrike

2023-06, Roos, Anna-Lena, Jeitziner, Loris Tizian, Zahn, Carmen

2019-02-18, Rack, Oliver, Zahn, Carmen, Bleisch, Susanne

Group coordination is a relevant prerequisite for understanding the effectiveness of groups. But, contrary to the large number of empirical studies, only a few studies concentrate on the development of analysis methods of coordination in groups. Hence, the purpose of this paper is to give a summary of the opportunities and limitations of common methods for capturing group coordination in applied field settings and to outline how visual analytics approaches might add to the common methods. Based on two illustrated visual analytics implementation examples—1) coordination and movements of soccer players, and 2) spatio-temporal event data—the potential of visual analytics approaches is identified for studying a greater variety of types of group coordination, and to consider the multifaceted nature of group processes in order to go beyond traditional coding processes.

2016, Mateescu, Magdalena, Zahn, Carmen, Klinkhammer, Daniel, Rack, Oliver, Reiterer, Harald, Weiße, Nora