Skip to Main Content
Ad-hoc sensor networks provide a cheap and scalable technology for constructing pervasive learning assessment systems that are embedded in physical environments. This paper proposes an extension to the QTI assessment standard that supports localized sensor data from sensor networks by incorporating the Sensor-ML notation. This extension can lead to a new class of pervasive learning environments where learning is enhanced by interacting with a large number of ad-hoc wireless nodes in a physical environment. Each wireless node can present various questions related to the current physical context. An architecture and a prototype implementation using the Jennic Zigbee toolkit is also presented.