Automatic question generation for learning evaluation in medicine

Authors:
Weiming Wang;Tianyong Hao;Wenyin Liu
Affiliations:
School of Computer, Wuhan University, Wuhan, PR China and Department of Computer Science, City University of Hong Kong, Hong Kong SAR, PR China;Department of Computer Science, City University of Hong Kong, Hong Kong SAR, PR China;Department of Computer Science, City University of Hong Kong, Hong Kong SAR, PR China
Venue:
ICWL'07 Proceedings of the 6th international conference on Advances in web based learning
Year:
2007

Citing 5
Cited 3

Online evaluation in WWW-based courseware

SIGCSE '97 Proceedings of the twenty-eighth SIGCSE technical symposium on Computer science education
Questions and Information: Contrasting Metaphors

Information Systems Frontiers
Automatic Generation of Trivia Questions

ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Online feedback by tests and reporting for elearning and certification programs

Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters
Incorporating non-local information into information extraction systems by Gibbs sampling

ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics

Math word question generation for training the students with learning difficulties

Proceedings of the International Conference & Workshop on Emerging Trends in Technology
Automatic question generation for literature review writing support

ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Learning good decompositions of complex questions

NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

An approach of automatic question generation from given learning material of medical text is presented in this paper. The main idea is to generate the questions automatically based on question templates which are created by training on many medical articles. In order to provide interesting questions, our research focuses on medical related concepts. This method can be used for evaluation of learner's comprehension after he/she finished a reading material. Different from traditional learning system the articles and questions are all prepared beforehand; participants can learn whatever new input medical articles with the help of automatic question generation.