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.

2023-01-20, Paneth, Lisa, Rack, Oliver, Zahn, Carmen

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.

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

2022-11-17, Schwaninger, Adrian, Sterchi, Yanik, Wäfler, Toni, Renggli, Philipp, Rack, Oliver, Bleisch, Susanne, Paneth, Lisa, Jeitziner, Loris Tizian, Gasparik, Matus, Zahn, Carmen

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

2023-06-15, Paneth, Lisa

Multi-method approaches are an emerging trend in CSCL research as they allow to paint a more comprehensive picture of complex group learning processes than using a single method. In this contribution, we combined measures from different data sources to capture the quality of collaborative group engagement (QCGE) in CSCL-groups: QCGE-self-assessments, QCGE-ratings of verbal group communication, and video recorded nonverbal group behaviors. Using different methods of analysis, we visualized, described, and analyzed the data and related the measures to each other. Here, we present results suggesting that measures from different data sources are interrelated: For instance, nonverbal behavior (like nodding the head) is related to high QCGE-ratings of verbal communications. Results are preliminary and show disparities, too. Yet, we conclude that the multi-method approach results in a more comprehensive understanding of QCGE. Feasibility and suitability of the multi-method approach are discussed and conclusions for future research are drawn.

2022-09-05, Paneth, Lisa

In learning groups, nonverbal behavior provides important information about socio-emotional processes, emotional states, and social relationships (Burgoon & Dunbar, 2018), and is therefore of utmost interest for the study of learning processes and group engagement - especially in online scenarios, where certain social cues are missing (cf. Trepte & Reinecke, 2018). The explorative study presented here addressed nonverbal behaviors of students as an indicator for the quality of collaborative group engagement (Sinha et al., 2015) in online learning groups. N=7 student groups were tasked with solving a hidden profile assignment via Zoom video conference. A code system was developed to capture students’ gestures and facial expressions and to analyze and explore them in terms of collaborative group engagement. Data analysis is currently underway. Preliminary results indicate that the frequency of nonverbal behaviors differs between groups and also between different types of nonverbal behavior (e.g., smiling, or braced chin). As a next step, this study will explore what these findings tell us about the differences and the synchronicity of nonverbal behaviors in relation to collaborative group engagement and how they relate to students’ verbal communication. The results of this study will contribute to the development of a system for assessing quality of collaborative group engagement and serve as a basis for real-time feedback to foster collaborative group engagement.