Rack, Oliver
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Exploring linguistic indicators of social collaborative group engagement
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.
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.
Coding and Counting- Frequency Analysis for Group Interaction
The basic idea of this chapter is to provide an introduction to the design and conduct of frequency analysis for group research. Frequency analysis has been commonly used for decades in several disciplines and fields of research as stand alone procedures (e.g., configural frequency analysis in clinical psychology, Lienert, 1971). But, despite of specialised articles in experimental psychology journals (e.g., Wickens, 1993), the description of frequency analysis as a specific method within group studies (e.g., coding group interaction data like chat protocols, then calculating frequencies across categories) is rare. This is remarkable, because the interests in frequency analysis nowadays have moved towards to the procedures of implementing its results as indices for further analysis, e.g. for the investigation of relationships between group processes like collaboration and outputs like performance by using the results of frequency analysis as inputs in inferential statistics. In this vein, this chapter attempts to highlight the most important options to use frequency analysis in group research as a relevant brick to gap the bridge between qualitative and quantitative methods (mixed method research). Furthermore, we fold into our descriptions and discussions empirical examples to illustrate the prerequisites, requirements and consequences of using frequency analysis in the field of group research. Finally, we clarify ways to present the results of frequency analysis for analyzing group data.
Predicting engagement in computer-supported collaborative learning groups using natural language processing
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.
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.
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.