Lea's Box' Persuadable Open Learner Model
Michael Kickmeier-Rust, Blandine Ginon, Matthew D. Johnson, Ali Türker · 2018
Learner models are a key element of many intelligent computer-based assessment and training approaches. Oftentimes, these models, the processes, and evidences are hidden from the learners and even teachers. Open learner models (OLM) are intended to make the underlying logic of a model as well as the evidences that contributed to a certain student appraisal transparent to its users. Furthermore, they are intended to support learning in a formative sense, aiming to help learners to self-monitor, plan, focus, and work independently as well as to communicate and negotiate appraisals with peers and teachers. We present a use case where Lea's OLM uses evidence from a speed-reading training application, to build the model. Learners use a persuasion function to interact with the system and maintain the OLM. The study showed that students are able to use it efficiently in order to make their model more accurate. We argue that persuasion, and other interactive maintenance features, are a strong approach to improve a transparent and accurate learner modelling, specifically when the underlying evidences are manifold and from multiple, partially unclear sources. Moreover, we found that the possibility to interact with the system on an individual basis and to have the possibility to intervene in the process is a strong means of making educational systems more personalized