Using Wikipedia and conceptual graph structures to generate questions for academic writing support
|Using Wikipedia and conceptual graph structures to generate questions for academic writing support|
|Author(s)||Liu M., Calvo R.A., Aditomo A., Pizzato L.A.|
|Published in||IEEE Transactions on Learning Technologies|
|Keyword(s)||Automatic question generation, natural language processing, writing support (Extra: Abstract concept, Automatic question generation, Conceptual graph, First year students, Key-phrase, Literature reviews, NAtural language processing, Research fields, Semi-automated, Turing tests, Wikipedia, Abstracting, Artificial intelligence, Computational linguistics, Graphic methods, Natural language processing systems, Quality control, Supervisory personnel, Teaching, Websites, Students)|
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Using Wikipedia and conceptual graph structures to generate questions for academic writing support is a 2012 journal article written in English by Liu M., Calvo R.A., Aditomo A., Pizzato L.A. and published in IEEE Transactions on Learning Technologies.
In this paper, we present a novel approach for semiautomatic question generation to support academic writing. Our system first extracts key phrases from students' literature review papers. Each key phrase is matched with a Wikipedia article and classified into one of five abstract concept categories: Research Field, Technology, System, Term, and Other. Using the content of the matched Wikipedia article, the system then constructs a conceptual graph structure representation for each key phrase and the questions are then generated based the structure. To evaluate the quality of the computer generated questions, we conducted a version of the Bystander Turing test, which involved 20 research students who had written literature reviews for an IT methods course. The pedagogical values of generated questions were evaluated using a semiautomated process. The results indicate that the students had difficulty distinguishing between computer-generated and supervisor-generated questions. Computer-generated questions were also rated as being as pedagogically useful as supervisor-generated questions, and more useful than generic questions. The findings also suggest that the computer-generated questions were more useful for the first-year students than for second or third-year students.
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