Exam Question Recommender System

TL;DR: This paper proposes using recommendation techniques to help a teacher search for and select questions from a shared and centralized IMS QTI-compliant question bank, using a hybrid, feature-augmentation, recommendation approach.

Abstract: Although E-learning has advanced considerably in the last decade, some of its aspects, such as E-testing, are still in the development phase. Authoring tools and test banks for E-tests are becoming an integral and indispensable part of E-learning platforms, and with the implementation of E-learning standards, such as IMS QTI, E-testing material can be easily shared and reased across various platforms. With this extensive E-testing material and knowledge comes a new challenge: searching for and selecting the most adequate information. In this paper we propose using recommendation techniques to help a teacher search for and select questions from a shared and centralized IMS QTI-compliant question bank. Our solution, the Exam Question Recommender System, uses a hybrid, feature-augmentation, recommendation approach. The recommender system uses Content-Based and Knowledge-Based recommendation techniques, resorting to the use of a new heuristic function. The system also engages in collecting both implicit and explicit feedback from the user in order to improve on future recommendations.