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Collaborative learning in small groups can enrich and enhance the learning experience in large online courses by facilitating interaction and collaborative knowledge building between peers. on the work reported here addresses scenarios based on asynchronous communication and exchange. As compared to synchronous and face-to-face settings, these scenarios require higher explicit coordination efforts and specific support mechanisms to reach out to inactive group members. This can be achieved by human tutors as well as through automatic interventions by the system in the organization and facilitation of group work. Our approach is based on characterizing the quality or well-functioning of collaboration using sequence alignment techniques that were originally developed in bioinformatics for the comparison of DNA sequences. Sequence alignment provides a similarity measure between pairs of action logs originating from the group work. The ensuing similarity matrix allows for clustering the working groups characterized by the similarity of their complete action logs. Notably, these clusters differ clearly in terms of quality measures related to the activity distribution and group output. This allows for determining quality indicators that can trigger feedback and adaptive scaffolding. Our data stem from inter-university online courses in which regular (weekly or bi-weekly) assignments had to be completed in small groups supported by an online forum for coordination and a separate tool for the actual collaborative writing. The basic actions are interpreted/coded as different contribution types, namely coordination, monitoring, minor/major contributions. Longer periods of inactivity are coded as another type of action ("gap"). These are the building blocks for the sequence alignment. One consequence of our analysis is a revision of the intuitive assumption that inactivity is a main indicator of inefficient group work: We found that inactivity can be compensated by early coordination to the extent that coordination is even the most important factor for successful group work. These insights are now being used to design alert mechanisms to signal inefficient group work and to generate timely interventions.

This commentary elaborates on the role of "grounding" as a focal theoretical concept and aims to contextualize the claims formulated by Schneider and Pea (2015, this issue) in previous CSCL discussions. It provides fingerposts for enriching and refining the concept of grounding from a pedagogical point of view. It also suggests using the mutual awareness of eye-gaze in the context of dialogical learning. [For "Does Seeing One Another's Gaze Affect Group Dialogue? A Computational Approach," see EJ1126980.]

This paper introduces the scientometric method of main path analysis and its application in an exemplary study of the paths of knowledge development and the roles of contributors in Wikiversity. Data from two scientific domains in this online learning community has been used. We see this as a step forward in adapting and adopting network analysis techniques for analyzing collaboration processes in knowledge building communities. The analysis steps are presented in detail including the description of a tool environment ("workbench") designed for flexible use by non-computer experts. By identifying directed acyclic graphs, the meaningful interconnections between developing learning resources are analyzed by considering their temporal sequence. A schema for the visualization of the results is introduced. The potential of the method is elaborated for the evaluation of the overall learning process in different domains as well as for the individual contributions of the participants. Different outstanding roles of contributors in Wikiversity are presented and discussed.

Orchestrating collaborative learning in the classroom involves tasks such as forming learning groups with heterogeneous knowledge and making learners aware of the knowledge differences. However, gathering information on which the formation of appropriate groups and the creation of graphical knowledge representations can be based is very effortful for teachers. Tools supporting cognitive group awareness provide such representations to guide students during their collaboration, but mainly rely on specifically created input. Our work is guided by the questions of how the analysis and visualization of cognitive information can be supported by automatic mechanisms (especially using text mining), and what effects a corresponding tool can achieve in the classroom. We systematically compared different methods to be used in a "Grouping and Representing Tool" (GRT), and evaluated the tool in an experimental field study. Latent Dirichlet Allocation proved successful in transforming the topics of texts into values as a basis for representing cognitive information graphically. The Vector Space Model with Euclidian distance based clustering proved to be particularly well suited for detecting text differences as a basis for group formation. The subsequent evaluation of the GRT with 54 high school students further confirmed the GRT's impact on learning support: students who used the tool added twice as many concepts in an essay after discussing as those in the unsupported group. These results show the potential of the GRT to support both teachers and students.

The 1998 paper by Martin Mühlenbrock, Frank Tewissen, and myself introduced a multi-agent architecture and a component engineering approach for building open distributed learning environments to support group learning in different types of classroom settings. It took up prior work on "multiple student modeling" as a method to configure and inform group learning situations based on individually assessed learner models. Additionally, methods for detecting collaboration patterns in group action logs were introduced. The approach was exemplified with several applications in the areas of mathematics and general problems solving. The commentary traces a line of development from this work to current mobile and web-based learning architectures and approaches to action logging for interaction analysis. "Lessons learned" are discussed and briefly illustrated with examples from recent work on intelligently enhanced learning environments.