TOKA: A Computer Assisted Assessment Tool Integrated in a Real Use Context
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Computer-aided generation of multiple-choice tests
HLT-NAACL-EDUC '03 Proceedings of the HLT-NAACL 03 workshop on Building educational applications using natural language processing - Volume 2
SIETTE: A Web-Based Tool for Adaptive Testing
International Journal of Artificial Intelligence in Education
Applications of lexical information for algorithmically composing multiple-choice cloze items
EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
A real-time multiple-choice question generation for language testing: a preliminary study
EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
EdAppsNLP 05 Proceedings of the second workshop on Building Educational Applications Using NLP
A Study on the Automatic Selection of Candidate Sentences Distractors
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Automatic distractor generation for domain specific texts
IceTAL'10 Proceedings of the 7th international conference on Advances in natural language processing
Generating educational assessment items from linked open data: the case of DBpedia
ESWC'11 Proceedings of the 8th international conference on The Semantic Web
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP
Hi-index | 0.00 |
Knowledge construction is expensive for Computer Assisted Assessment. When setting exercise questions, teachers use Test Makers to construct Question Banks. The addition of Automatic Generation to assessment applications decreases the time spent on constructing examination papers. In this article, we present ArikIturri, an Automatic Question Generator for Basque language test questions, which is independent from the test assessment application that uses it. The information source for this question generator consists of linguistically analysed real corpora, represented in XML mark-up language. ArikIturri makes use of NLP tools. The influence of the robustness of those tools and the used corpora is highlighted in the article. We have proved the viability of ArikIturri when constructing fill-in-the-blank, word formation, multiple choice, and error correction question types. In the evaluation of this automatic generator, we have obtained positive results as regards the generation process and its usefulness.