In the rapidly evolving twenty-first century, collaboration stands as a vital skill. Recognizing its significance, the detection of collaboration quality can play a pivotal role in fostering effective teamwork. This newly published chapter dives into the realm of collaboration quality detection and measurement, aiming to achieve the following objectives:

  1. Defining collaboration quality by leveraging audio data and unobtrusive learning analytics measures.
  2. Detailing the design of a sensor-based setup specifically tailored for automatic collaboration analytics.
  3. Advancing the quantification of collaboration quality through the utilization of this setup and presenting the analysis using insightful visualizations.

Moreover, the chapter delves into the challenges and issues at hand while exploring potential solutions that build upon existing research. To elucidate the various objectives, the terminology of “indicators” (i.e., the events) and “indexes” (i.e., the process) is employed to define the components necessary for detecting collaboration quality.

A compelling study conducted during collaborative brainstorming revealed a positive correlation between the equality (i.e., the index) of total speaking time (i.e., the indicator) among group members, decreased dominance of individual members (in terms of total speaking time), and improved collaboration quality. Nevertheless, it is crucial to recognize that the quality of collaboration depends on the specific context and content of the discussion. Thus far, content analysis during collaboration has primarily remained superficial, relying on representative keywords to categorize different topic clusters.

To address this limitation, a novel sensor-based setup for automatic collaboration analytics has been developed, enabling a holistic understanding of collaboration quality within a learning context. The primary objective of this setup is to uncover insights into “how” group members communicate (i.e., speaking time indicator) and “what” they discuss (i.e., the content of conversations), ultimately leading us closer to effective collaboration quality measurement.

By delving into collaboration quality detection and measurement, this chapter provides valuable insights and practical approaches to enhance collaborative endeavors. Embracing the power of sensor-based technology, we can unlock new dimensions of effective collaboration, fostering an environment where teams thrive and excel.


Praharaj, S., Scheffel, M., Specht, M., Drachsler, H. (2023). Measuring Collaboration Quality Through Audio Data and Learning Analytics. In: Kovanovic, V., Azevedo, R., Gibson, D.C., lfenthaler, D. (eds) Unobtrusive Observations of Learning in Digital Environments. Advances in Analytics for Learning and Teaching. Springer, Cham.