Intelligent Evaluation of Short Responses for e-Learning Systems

Abstract

Evaluation of learners’ response is an important metric in determining learners’ satisfaction for any learning system. E-Learning systems currently use string matching or regular expression-based approaches in evaluating short answers. While these endorse the correctness of an answer, they are limited to handling predictable errors only. The nature of errors, however, may vary and it is important to intelligently judge the nature of the error to correctly gauge the state of learning of the learner. A better learning experience requires the system to also display benevolence, which is an innately human behavior characteristic, in evaluating the response. The current paper presents a k-variable fuzzy finite state automaton-based approach to implement an evaluation system for short answers. The proposed method attempts to emulate human behavior in the context of errors committed which may be knowledge based or inadvertent in nature. The technique is explained with sample scores from test conducted on a group of learners.