PALE 2017 - 7th International Workshop on Personalization Approaches in Learning Environments (PALE)

PALE 2017 is a follow-up of the six previous editions of PALE (see details at history). The focus of this workshop series is put on the different and complementary perspectives in which personalization can be addressed in learning environments (e.g., informal, workplace, lifelong, mobile, contextualized, and self-regulated learning). Previous editions have shown several important issues in this field, such as behavior and embodiment of pedagogic agents, suitable support of self-regulated learning, appropriate balance between learner control and expert guidance, design of personal learning environments, contextual recommendations at various levels of the learning process, tracking affective states of learners, harmonization of educational and technological standards, processing big data for learning purposes, predicting student outcomes, adaptive learning assessment, and evaluation of personalized learning solutions. PALE workshop offers an opportunity to present and discuss a wide spectrum of issues and solutions.
From the past experience we have identified new areas of interest in this research scope to complement the previous ones. Thus, in this workshop edition we would like to share and discuss the new trends in current research on how user modeling and associated artificial intelligent techniques are able to contextualize and manage the increasing amount of information coming from the task at hand and its surrounding environment in order to provide the personalization support in a wide range of learning environments, which are increasingly more sensitive to the learners and their context. This covers many interrelated fields such as: intelligent tutoring systems, learning management systems, personal learning environments, serious games, agent-based learning environments, and others.
We are especially interested in the enhanced sensitivity towards the management of vast data coming from learners' interactions (e.g., sensor detection of affect in context) and technological deployment (including web, mobiles, tablets, tabletops), and how can this wide range of situations and features impact on modeling the learner interaction and context. Furthermore, we aim to cover the every time more demanding need of personalized learning in wider contexts ranging from daily life activities to massive open online courses (MOOCs).
The higher-level research question to be addressed in the workshop is: "Which approaches can be followed to cater for the increasing amount of information available from immediate (e.g., in terms of wearable devices) to broader contexts in order to provide effective and personalize assistance in learning situations?".