Few-shot learning is challenging for learning algorithms that learn each task in isolation and from scratch. In contrast, meta-learning learns from many related tasks a meta-learner that can learn a new task more accurately and faster with fewer examples, where the choice of meta-learners is crucial. In this paper, we develop Meta-SGD, an SGD-like, easily trainable meta-learner that can initial...

E-learning systems are changing education and organizational training considerably. With the advancement of online-based learning systems, learner control of the instructional process has emerged as a decisive factor inherent to technology-based learning. However, the conceptual work on the role of learner control in e-learning has not advanced sufficiently to predict how learner control impact...

Electronic learning systems and virtual learning environments for education or training are increasingly gaining attention in educational organizations as well as in industry. An important dimension in the evaluation of a virtual learning environment is the assessment of e-learner satisfaction. Critical elements in the assessment procedure are the learner interface, the course content, the acce...

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.