Zahn, Carmen
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Zahn, Carmen
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- PublikationZooming in: The role of nonverbal behavior in sensing the quality of collaborative group engagement(Springer, 16.05.2024) Paneth, Lisa; Jeitziner, Loris Tizian; Rack, Oliver; Opwis, Klaus; Zahn, Carmen [in: International Journal of Computer-Supported Collaborative Learning]01A - Beitrag in wissenschaftlicher Zeitschrift
- PublikationPredicting engagement in computer-supported collaborative learning groups using natural language processing(20.03.2024) Jeitziner, Loris Tizian; Paneth, Lisa; Rack, Oliver; Zahn, Carmen; Wulff, DirkCollaborative 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.06 - Präsentation
- PublikationTrend Monitoring & Erarbeitung fundierter Entscheidungsgrundlagen für die Entwicklung von FHNW Learning Spaces(Hochschule für Angewandte Psychologie FHNW, 31.12.2023) Jeitziner, Loris Tizian; Frick, Andrea; Paneth, Lisa; Zahn, Carmen05 - Forschungs- oder Arbeitsbericht
- PublikationStressors in online exams – Same same but different?(06/2023) Roos, Anna-Lena; Jeitziner, Loris Tizian; Zahn, Carmen04B - Beitrag Konferenzschrift
- PublikationExploring linguistic indicators of social collaborative group engagement(International Society of the Learning Sciences, 2023) Jeitziner, Loris Tizian; Paneth, Lisa; Rack, Oliver; Zahn, Carmen; Wulff, Dirk U.; Damşa, Crina; Borge, Marcela; Koh, Elizabeth; Worsley, Marcelo [in: Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023]This study takes a NLP approach to measuring social engagement in CSCL-learning groups. Specifically, we develop linguistic markers to capture aspects of social engagement, namely sentiment, responsiveness and uniformity of participation and compare them to human ratings of social engagement. We observed small to moderate links between NLP-markers and human ratings that varied in size and direction across the different groups. We discuss measurement and prediction of social collaborative group engagement using natural language processing.04B - Beitrag Konferenzschrift
- PublikationA multi-method approach to capture quality of collaborative group engagement(International Society of the Learning Sciences, 2023) Paneth, Lisa; Jeitziner, Loris Tizian; Rack, Oliver; Zahn, Carmen; Damsa, Crina; Borge, Marcela; Koh, Elizabeth; Worsley, Marcelo [in: Proceedings of the 16th International Conference on Computer-Supported Collaborative Learning - CSCL 2023]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.04B - Beitrag Konferenzschrift
- PublikationPoster Presentation of Project Examples in the Field of Artificial Intelligence(17.11.2022) Schwaninger, Adrian; Sterchi, Yanik; Wäfler, Toni; Renggli, Philipp; Rack, Oliver; Bleisch, Susanne; Paneth, Lisa; Jeitziner, Loris Tizian; Gasparik, Matus; Zahn, Carmen06 - Präsentation
- PublikationExploring nonverbal behavior and collaborative group engagement in online learning groups(22.07.2022) Rack, Oliver; Paneth, Lisa; Jeitziner, Loris Tizian; Zahn, CarmenIn 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.06 - Präsentation
- PublikationWhat if the computer crashes? Findings from an exploratory factor analysis on stressors in online exams(06/2022) Jeitziner, Loris Tizian; Roos, Anna-Lena; Ruf, Alessia; Zahn, CarmenThe pandemic has forced higher education to shift from onsite to online environments. This novel situation may increase students’ exam stress and induce new stressors. In the present study, we identified stressors in online exams by conducting an exploratory factor analysis of a novel questionnaire. The analysis revealed five factors that categorize students’ experience of stress. Preliminary descriptive results suggest that possible system failures and social pressures cause the highest stress for students.04B - Beitrag Konferenzschrift