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Biology question generation from a semantic network

Abstract Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply instructors with biology questions, a semantic network approach was developed for generating open response biology questions. The generated questions were compared to professional authorized questions.

To boost students’ learning experience, adaptive selection was built on the generated questions. Bayesian Knowledge Tracing was used as embedded assessment of the student’s current competence so that a suitable question could be selected based on the student’s pr... (more)
Created Date 2015
Contributor Zhang, Lishan (Author) / VanLehn, Kurt (Advisor) / Baral, Chitta (Committee member) / Hsiao, Ihan (Committee member) / Wright, Christian (Committee member) / Arizona State University (Publisher)
Subject Computer science / Education / adaptive learning / evaluation / intelligent tutoring system / question generation
Type Doctoral Dissertation
Extent 178 pages
Language English
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2015
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis