Computer-Supported Collaborative Learning and Learning Analytics – A perfect marriage!...?

For computer-supported collaborative learning (CSCL) to unfold its potential for learning, fruitful interaction between the learners is indispensable. The field of CSCL has for many years been concerned with the analyses of collaborative process data, in order to shed light on the processes at play during collaborative learning, to understand how these processes support student learning, and to investigate how collaboration can be promoted by instructional means. However, analyzing collaborative learning processes is laborious and extremely time consuming. There seems to be an inextricable tension between analytical depth and amount of data that can be analyzed: that is, analyzing little data in great detail vs. a larger dataset more coarsely. Given that in CSCL settings usually digital data of learners’ activities accrue, great hope has been put on learning analytics to help overcome this obstacle and enable in depth collaborative process analyses at scale. So, like in a marriage, there clearly is affection between the two fields and one may wonder: what is it that attracts them so much to each other? But it is also true for marriage that after the honeymoon, when the partners get to know each other better and spend their daily life together, they may notice that not everything between them fits so well. They may even wonder: Are there still enough good reasons for staying together or should we get a divorce? In my keynote, I will explore these questions with a view on the “marriage” between the fields of CSCL and Learning Analytics.