PALE 2013:
Personalization Approaches
in Learning Environments

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Topics

The higher-level research question to be addressed in the workshop is: "What are suitable approaches to personalize learning environments?" It will be considered in various contexts of interactive, personal, and inclusive learning environments. In this sense, the topics of the workshop include (but are not limited to) the following:

• Motivation, benefits, and issues of personalization in learning environments
• Approaches for the personalization of inclusive, personal and interactive learning environments
• Successful methods and techniques for personalization of learning environments
• Results and metrics in personalized learning environments
• Social and educational issues in personalized learning environments
• Use of pedagogic conversational agents
• Affective computing in personalized learning environments
• User and context awareness in personalized learning environments

Motivation

The main motivation of this workshop is to facilitate exchange of experience and discussions on personalization in the educational domain, following a well-established methodology of the Learning Cafe. Learning is a traditional and very popular area in the field of user modeling, personalization, and adaptation.

Individualization of learning is a major challenge in education and rapid technological development brings new opportunities how to address it. A lot of data can be collected in the educational process, but we need to find ways how to use it reasonably and to develop useful services in order to make the learning process more effective and efficient. Novel personalized services and environments are needed especially in lifelong and workplace educational settings, in order to support informal, self-regulated, mobile, and contextualized learning scenarios.

A big challenge is also adaptation considering both long-term objectives and short-term dynamically changing preferences of learners. Here open and inspectable learner models play an important role. In the case of pedagogic conversational agents personalization is fostered by the use of adapted dialogues to the specific needs and level of knowledge of each student.

Participants at previous PALE workshops have raised also other interesting issues, like predicting student outcomes from unstructured data, modeling affective state and learner motivation, and using sensors to understand student behavior.