Computer-based learning environments are designed to support learning processes to facilitate acquisition, development, use, and transfer of knowledge and strategies required to solve complex tasks. These systems have to interact with different users, and support them with decisional processes that are sensitive to individual differences.

A primary concern is self-regulation, which is important for developing independent learners. Students need to plan their learning activities, to adapt their learning strategies to meet learning goals, monitor and control their cognitive processes, and self-assess their own performance.

In addition, students must also regulate their affect and motivation. Students also need flexible systems that can provide visualization and browsing of multirepresentational materials that match the studentʼs learning profile.

These systems have to exhibit a very tight interaction between learner characteristics, and the mediating regulatory processes.

Teachers and human tutors need easy-to-use tools to visualize the domain for design purposes, and to control the acquisition of self-regulatory skills, to stimulate knowledge elicitation, and to integrate new knowledge. External regulating agents need to monitor and model students as they are involved in their learning tasks. In this way the externally regulated feedback to the students is optimized, and their learning is maximized.

Visualization and analysis of the data to facilitate monitoring and inference of studentsʼ behaviors and strategies may require complicated inference methods. Some examples are social networks analysis, knowledge visualization tools in relation to the domain regions to be explored and linguistic tools to analyze studentsʼ sentences in forums and chats.

Traditional intelligent (i.e., rational) systems have limitations in achieving all these goals. Systems in support of education have to be “cognitive”. A (meta)cognitive system is self-aware – it can adapt to the user, and may propose self-regulation strategies to help the user learn and deploy self-regulatory processes and facilitate dynamic adaptivity during learning.

This sort of cognitive push-pull can be enabled via multi-modal interaction where the linguistic modality as well as other detectors (for affect, motivation, and behavioral monitoring and control) is very crucial. The possibility to define a systemʼs “mental state” can enable it to increase autonomously its knowledge to support the user in his/her decisional processes.

MCES 2009 is aimed to stimulate the creation of a dedicated research community about the definition of what is a (meta)cognitive educational system. What aspects of cognition, metacognition, affect, and motivation have to be explored and integrated to achieve the goal of a new generation of MetaCognitive Tools for enhancing learning with understanding and transfer in MetaCognitive Educational Systems?

Important dates

Abstract submission deadline:
June 9 2009

Full paper submission deadline:
June 30 2009

Notification of Acceptance:
Extended: July 30 2009

Camera-ready due:
September 1 2009

Registration deadline:
October 16 2009

AI Funding Seminar and AAAI FS MCES:
November 4-7 2009