Automatic Generation and Ranking of Questions for Critical Review

Critical review skill is one important aspect of academic writing. Generic trigger questions have been widely used to support this activity. When students have a concrete topic in mind, trigger questions are less effective if they are too general. This article presents a learning-to-rank based syste...

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Bibliographic Details
Published in:Educational technology & society 2014-04, Vol.17 (2), p.333-346
Main Authors: Liu, Ming, Calvo, Rafael A, Rus, Vasile
Format: Article
Language:English
Subjects:
Academic Language
Basic writing
Citation Analysis
Classification
Critical Reading
Educational aspects
Educational technology
Electronic Learning
Full Length Articles
Higher Education
Information Seeking
Interrogative sentences
Language instruction
Literary criticism
Literature reviews
Machine learning
Management
Natural Language Processing
Needs Assessment
Qualitative analysis
Qualitative Research
Questioning Techniques
Ranking
Sentence generation
Statistical Analysis
Student writing
Supervisors
Syntactics
Taxonomy
University students
Verbs
Writing
Writing (Composition)
Writing assignments
Writing instruction
Writing skills
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Summary:Critical review skill is one important aspect of academic writing. Generic trigger questions have been widely used to support this activity. When students have a concrete topic in mind, trigger questions are less effective if they are too general. This article presents a learning-to-rank based system which automatically generates specific trigger questions from citations for critical review support. The performance of the proposed question ranking models was evaluated and the quality of generated questions is reported. Experimental results showed an accuracy of 75.8% on the top 25% ranked questions. These top ranked questions are as useful for self-reflection as questions generated by human tutors and supervisors. A qualitative analysis was also conducted using an information seeking question taxonomy in order to further analyze the questions generated by humans. The analysis revealed that explanation and association questions are the most frequent question types and that the explanation questions are considered the most valuables by student writers.
ISSN:1176-3647
1436-4522
1436-4522