PALE 2015:
Personalization Approaches
in Learning Environments

Topics

The higher-level research question to be addressed in the workshop is: "Which approaches can be followed to personalize learning environments?" It will be considered in various contexts of interactive, personal, and inclusive learning environments.

This workshop includes (but is not limited to) the following topics related to personalization of learning environments:

  • Affective computing
  • Ambient intelligence
  • Personalization of MOOCs
  • Learning recommendation
  • Learner and context awareness
  • Cognitive and meta-cognitive scaffolding
  • Social issues in personalized learning environments
  • Open-corpus educational systems
  • Adaptive mobile learning
  • Successful methods and techniques
  • Reusability, interoperability, scalability
  • Evaluation of adaptive learning environments

Motivation

Personalization is crucial to foster effective, active, efficient, and satisfactory learning, especially in informal learning scenarios that are being demanded in lifelong learning settings, with more control on the learner side and more sensitivity towards context. Personalization of learning environments is a long-term research area, which evolves as new technological innovations appear.

Nowadays there are new opportunities for building interoperable personalized learning solutions that consider a wider range of learner situations and interaction features (in terms of physiological and context sensors). However, in the current state of the art it is not clear how this enhanced interaction can be supported in a way that positively impacts the learning process. In this context, suitable user modeling is needed to understand the current needs of the learners. There are still open issues in this area, which refer to providing open learner models in terms of standards that cover the extended range of available features and allow for interoperability with external learning services as well as taking advantage of the integration of ambient intelligence devices to gather information about the learner interaction in a wider sense than the classical desktop computer approach.

Moreover, other related topics are to be considered in the learner modeling, including affective states of the learner, changing situations in terms of context, learners' needs and their behavior. Another broad research area addresses personalization strategies and techniques, considering not only the learner model, but the whole context of the learning experience, including the various technological devices that are available in the particular situation.