Building Efficient Assessment Applications with Personalized Feedback

Abstract

The aim of this chapter is to provide a model for requirement specification, useful in developing efficient e-assessment applications with personalized feedback, which is enhanced by calling a recommender engine. The research was done in the context of using educational technology to facilitate learning processes. The data used to build the requirement model was collected from a set of interviews with the users and creators of an e-assessment application in project management. Requirement analysis assumes human effort and thus introduces uncertainties. To minimize the subjective factor, the data extracted from interviews with the users and the developers of the existing e-assessment application are clustered using a fuzzy logic solution into classes of requirements. These classes are the units of the model. The connections between classes are also mentioned: relations such as “if-then,” “switch,” or” contains” are explained. The requirements analysis conducts a smart set of specifications, obtained in a collaborative manner, useful for the design of e-assessment applications in project management or other similar domains.