Learners are assessed by several systems during their lifelong learning. Those systems can maintain fragments of information about a learner derived from his learning performance and/or assessment in that particular system. Customization services would perform better if they would be able to exchange as many relevant fragments of information about the learner as possible. This paper presents th...

|We propose a new method of classiication built as a cascade of a distributed learner and a local learner. The distributed learner generalizes to learn the \rule" and the local learner learns the \exceptions" not covered by the \rule." We show how such a system can be trained using cross-validation. We use a multi-layer perceptron with sigmoidal hidden units as the rule-learner and a k-nearest ...

Emerging MOOC platforms capture huge amounts of learner data. This paper presents our MOOClm platform, for transforming data from MOOCs into independent learner models that can drive personalisation and support reuse of the learner model, for example in an Open Learner Model (OLM). We describe the MOOClm architecture and demonstrate how we have used it to build OLMs.

We consider open learner models (OLM) with reference to adaptive navigation support and learner control. Our purpose is to assess the potential of a greater range of OLMs in adaptive educational hypermedia. We introduce five OLMs, discuss how these might be applied, and present learner reactions.

This talk is concerned with using syntactic annotation of learner language and the corresponding target hypothesis to find structural acquisition difficulties in German as a foreign language. Using learner data for the study of acquisition patterns is based on the idea that learners do not produce random output but rather possess a consistent internal grammar (interlanguage; cf. [1] and many ot...