Using Intelligent Tutor Technology to Implement Adaptive Support for Student Collaboration

Abstract

Research on computer-supported collaborative learning has shown that students need support to benefit from collaborative activities. While classical collaboration scripts have been effective in providing such support, they have also been criticized for being coercive and not allowing students to self-regulate their learning. Adaptive collaboration support, which would provide students with assistance when and where they need it, is a possible solution. However, due to limitations of natural language processing, the development of adaptive support based on an analysis of student dialogue is difficult. To facilitate the implementation of adaptive collaboration support, we propose to leverage existing intelligent tutoring technology to provide support based on student problem-solving actions. The present paper gives two examples that demonstrate this approach and reports first experiences from the implementation of the systems in real classrooms. We conclude the paper with a discussion of possible future developments in adaptive collaboration support.