Paneth, Lisa
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Beyond words: investigating non-verbal indicators of collaborative engagement in a virtual synchronous CSCL environment
In the future of higher education, student learning will become more virtual and group-oriented, and this new reality of academic learning comes with challenges. Positive social interactions in virtual synchronous student learning groups are not self-evident but need extra support. To successfully support positive social interactions, the underlying group processes, such as collaborative group engagement, need to be understood in detail, and the important question arises: How can collaborative group engagement be assessed in virtual group learning settings? A promising methodological approach is the observation of students’ non-verbal behavior, for example, in videoconferences. In an exploratory field study, we observed the non-verbal behavior of psychology students in small virtual synchronous learning groups solving a complex problem via videoconferencing. The groups were videorecorded to analyze possible relations between their non-verbal behaviors and to rate the quality of collaborative group engagement
Exploring nonverbal behavior and collaborative group engagement in online learning groups
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.
Grundbausteine engagierter Zusammenarbeit in Lerngruppen
Verlässliche Kooperation in Zeiten der Digitalisierung basiert auf Gruppenprozessen. In diesem Kapitel wird herausgearbeitet, wie eine hohe Qualität engagierter Zusammenarbeit in Gruppen auf verschiedenen Ebenen - sowohl kognitiv-aufgabenbezogen als auch interpersonell-emotional - beschrieben werden kann. Dabei geht es um die praxisrelevante Frage, wie digital unterstütztes Lernen etwa im Hochschulstudium bezüglich der Qualität des gemeinsamen Engagements in Studierendengruppen besser gefördert werden kann. Denn: eine verlässliche Kooperation in Zeiten der Digitalisierung muss gelernt werden. Hochschulen leisten einen bedeutenden Beitrag zur Realisierung verlässlicher Kooperation, wenn sie Studierende gezielt in kollaborativen Lehr- und Lernsituationen fördern und fordern, in denen Gruppenprozesse effektiv und effizient zu gestalten sind. Von besonderer Bedeutung ist es dabei, digital unterstützte Lehr-/Lernszenarien zu entwickeln, in denen Studierende Gelegenheit haben, mit digitalen Werkzeugen zu arbeiten, diese in der Gruppenarbeit anzuwenden und auszuprobieren, um damit komplexe Probleme zu lösen und Teamkompetenzen zu erwerben.
A Multi-Method Approach to Capture Quality of Collaborative Group Engagement
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.
Exploring nonverbal behavior and collaborative group engagement in online learning groups
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.
A multi-method approach to capture quality of collaborative group engagement
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.