PALE 2017:
Personalization Approaches
in Learning Environments


The higher-level research question to be addressed in the workshop is: "How to deal with the increasing amount of information available from various resources and contexts, in order to provide effective personalized assistance in learning situations?". It is considered in various contexts: interactive, personal, and inclusive learning environments.

PALE 2017 edition includes (but is not limited to) the following topics related to personalization of learning environments:

  • Affective computing
  • Big data in education
  • Personal and context modeling
  • Data processing within and across learning situations
  • Ambient intelligence
  • Personalization in MOOCs
  • Learning recommendation and explanations
  • 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
  • Wearable devices for sensing and acting in ubiquitous learning scenarios
  • Inclusive and adaptive education


Personalization is crucial to foster effective, active, efficient, and satisfactory behavior in learning situations in an increasing and varied number of contexts, which includes informal learning scenarios that are being demanded in everyday life activities and 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 data coming from varied 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 the new information sources are to be managed and combined in order to enhance interaction in a way that positively impacts the learning process whose nature is essentially adaptive.

In this context, suitable user modeling is needed to understand both realistic learning environments cropping up in a wider range of situations and the needs of the learners within and across them. There are new open issues in this area, which refer to detecting and effective managing personal and context data in an increasing and varied range of learning situations in order to provide personal assistance to the learner, which can also take into account her affective state. This requires enhancing the management of an increasing number of information sources (including wearables) and big data which ultimately are to provide a better understanding of every person's learning needs within different contexts and over short-, medium-, and long-term periods of time.

This will hopefully increase learner's understanding of their own needs in terms of open learner models that are to be built from standards that support interoperability and which are to cover an extended range of available features thus allowing for combining different external learning services as well as taking advantage of the integration of an increasing amount of information sources coming from ambient intelligence devices to gather information not only about the learner interaction, but the whole context of the learning experience. In this way, the learner modeling involves analyzing changing situations in terms of context, learners' needs and their behavior, requiring personal and collective management of the information available.