- F. D. Jong, J. Ende, Hennie van Heijst, Yoshiaki Matsuzawa, P. Kirschner, Jianwei Zhang, Mei-Hwa Chen, Feng Chen, C. Rosé, Erick Velazquez Godinez, S. Ratté, Bodong Chen, Carol K. K. Chan, J. Aalst, Christine Yang, J. Oshima, C. Hmelo‐Silver, A. Wise. 2017. Making a Difference: Analytics for Quality Knowledge-Building Conversations. Abstract: The symposium focuses on the analysis of the knowledge building process e.g. idea improvement conversations by which students get to a high quality of knowledge and understanding. Learning Analytics (LA) focuses on the collection, measure and analysis of data about learners and their contexts (Long & Siemens, 2011). LA tools are normally rooted in probabilistic/frequency-based approaches. These are themselves incapable of capturing the meaning of texts at any level, because probabilities do not constitute natural language semantics. Therefore, semantic related analytics seems to be a promising approach. Not only to get insight in the process of knowledge building as a support for students and teachers in this collective process but also as a possibility for assessment. Not to control but to mirror and feed forward the semiotic collaborative process of building an understanding that makes a difference for how students look at and act in our world.
