Artificial intelligence techniques for supporting face-to-face and online collaborative learning

Abstract

Collaborative learning provides opportunities for data-intensive, educational innovation because learners need to externalize some of their mental processes in the form of dialogue, drawings, and other representations. Digital traces of these externalizations can be captured and analyzed using various Artificial Intelligence and analytic techniques. This can further accelerate researchers’ analysis cycles and help in the development of more effective tools that support collaboration and learning. This chapter describes techniques currently available for supporting face-to-face and online collaborative learning situations. We particularly focus on techniques that provide intelligent support to: (i) form effective groups, (ii) provide direct feedback to students, (iii) collaborate on scripts, (iv) enhance group and teacher awareness, and (v) perform summative assessments in the pre-active, inter-active and post-active phases of collaborative learning. We discuss potential future trends for research and development in this area, emphasizing the need for evaluating the validity, utility and interpretability of emerging techniques for modelling and assessing meaningful aspects of collaborative learning.