Group process quality in digital learning

The research team led by Carmen Zahn from the University of Applied Sciences and Arts Northwestern Switzerland (FHNW) studied complex, dynamic collaborative processes occurring in student teams that use digital tools to work together to solve problems, for example for a project report. The aim was to measure the quality of collaborative group engagement. To this end, the team developed a new, multi-method approach that can record the quality of this collaborative group engagement (QGCE) in its four dimensions – behavioural, social, cognitive and conceptual-consequential. They observed groups using video analyses and combined this data with self-assessments by group members. Each method opened up a unique perspective: While the video-based evaluations provided an observer’s view, the self-assessments reflected the groups’ internal dynamics.

Collaborative engagement is a key factor for successful group learning in higher education settings. Until now, little was known about how the quality of this engagement develops, and what role non-verbal communication plays. The project delivers new insights to improve collaboration and provides an instrument that can be used to measure and therefore assess the quality of collaborative group engagement.

The reason that little was known about the quality of this engagement and non-verbal behaviour in collaborative learning was largely the fact that these factors are not easy to measure. The researchers therefore sought to develop automated methods to record and visualise non-verbal social interaction and collaborative processes in learning groups.

The project findings are not only scientifically relevant, but should also serve as a solid foundation for evidence-based higher education development and the advancement of university teaching in the age of the digital transformation. These objectives are particularly important as interdisciplinary cooperation and teamwork are increasingly becoming key competencies, especially in technical degree programmes.

The findings show that certain non-verbal behaviours – such as nodding, laughing and eye contact – are meaningful indicators of collaborative group engagement quality when working in groups. However, there are discrepancies between perspectives, with some participants rating their own cognitive engagement as high, while observers only assess it as moderate to low. On the whole, the data analysis shows the complexity of the phenomenon of collaborative group engagement in learning settings – and the importance of combining different data sources to obtain a more complete picture of group dynamics.

Three main messages

1. Collaborative group engagement can be broken down into four dimensions: behavioural, cognitive, social, and conceptual-consequential.

2. The researchers propose a multi-method approach to measure the quality of collaborative group engagement (QCGE) in all its complexity. Besides non-verbal behaviour, this also comprises verbal communication and includes internal perspectives (self-assessment by the group) and external perspectives (observation by third parties). This combination is central to the development of practicable measurement methods in education.

3. Visual analytics (VA) is particularly well suited to the analysis of non-verbal behaviour and multi-modal communication. This method allows professionals to investigate and process complex data in a flexible and readily understandable way to identify high-quality data sets and meaningful patterns. Challenges remain, however, for example in feature extraction, in the trade-off between data quality and computing power, and in the design of effective visualisations.

Next generation learning: Investigating and enhancing collaborative group engagement quality to support learning groups [by social robots]