Zahn, Carmen
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Trend Monitoring & Erarbeitung fundierter Entscheidungsgrundlagen für die Entwicklung von FHNW Learning Spaces
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.
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.
Future Skills – Zukunftsorientierte Hochschullehre
ZusammenfassungIn diesem Theoriebeitrag der Zeitschrift für Psychodrama und Soziometrie wird ein wissenschaftliches Erklärungsmodell für die Wirksamkeit psychodramatischer Methoden in der Hochschullehre entwickelt. Eine zukunftsfähige Hochschullehre, die bei Studierenden neben dem Erlernen komplexer Wissens- und Handlungszusammenhänge auch mit fundierten Methoden die Kreativität, Innovationsfreude und „Future skills“ fördert, ist wichtiger denn je.
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.