Abstract:

Assessment Wiki is a platform used to crowd-source question banks from teachers. The platform uses a modified version of Media Wiki engine to allow teachers to contribute multiple-choice questions based on the IMS Question and Test Interoperability (QTI) standard. Question created using this system are run through the usual editorial workflows to ensure appropriate quality. After publishing, a teacher is able to select student learning outcomes based on the curriculum to automatically generate exams in PDF format. The availability of cheap sensors has enabled the enactment of learning and assessment designs that directly incorporate sensor data. This paper presents the design and implementation of a system based on Assessment Wiki that allows direct utilization of sensors into the crowd-sourcing process. For example, a question can directly include reading of the GPS position from a smart phone in the stem or possible answers. In addition, an ability to read various types of sensors (e.g., temperature, pressure, etc.) from heterogeneous networks is also provided. The IMS QTI format has been extended to incorporate this capability. A middleware that simplifies the application development and separates the hardware complexity has also been developed. Communicating with sensor nodes is implemented through standardized formats, where each of the sensor nodes is defined via a model that helps their identification through a RESTful API. The direct incorporation of sensor data into questions allows the crowd-sourcing of highly contextualized questions around the internet of things that depend on the current situation in which the question is being asked.

Keywords:

Internet of Things, crowd-sourcing, assessments, sensors.