We describe and justify the use of a schema for contextualized attention metadata (CAM) and a framework for capturing and exploiting such data. CAM are data about computer-related activities and the foci of attention for computer users. As such, they are a prerequisite for the personalization of both information and task environments. We outline the possibilities of utilizing CAM, with a focus on technology-enhanced learning (TEL) scenarios, presenting the MACE system for architecture education as a CAM test bed.

Introduction

Contextualized attention metadata

The contextualized attention metadata (CAM) format, defined by an XML schema, is a format for data about the foci of attention and activities of computer users. Contextualized attention metadata describe which data objects attract the users' attention, which actions users perform with these objects and what the use contexts are. As such, they are a prerequisite for generating context-specific user profiles that help to personalize and optimize task and information environments. They can be employed for annotating data objects with information about their users and usages, thereby rendering possible object classifications according to use frequency, use contexts and user groups. Moreover, they can be crucial for supporting cooperative work: they may be utilized for monitoring distributed task processing, for identifying and sharing knowledge of critical information, and for bringing together working groups (Schuff et al. 2007; Hauser et al. 2009; Adomavicius and Tuzhilin 2005, among others).