TY - JOUR
T1 - Adaptively selecting biology questions generated from a semantic network
AU - Zhang, Lishan
AU - VanLehn, Kurt
N1 - Funding Information:
This research was supported by The Diane and Gary Tooker Chair for Effective Education in Science, Technology, Engineering and Math, and by the National Science Foundation under grant DUE-1525197.
Publisher Copyright:
© 2016 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2017/10/3
Y1 - 2017/10/3
N2 - The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student’s competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology knowledge components. Tasks were represented and selected based on which knowledge components they addressed. Unlike earlier work, where the knowledge components and their relationships to the questions were defined by domain experts, this project demonstrated that the knowledge components, questions and their relationships could all be generated from a semantic network. An experiment found that students using our adaptive question selection had reliably larger learning gains than students who received questions in a mal-adaptive order.
AB - The paper describes a biology tutoring system with adaptive question selection. Questions were selected for presentation to the student based on their utilities, which were estimated from the chance that the student’s competence would increase if the questions were asked. Competence was represented by the probability of mastery of a set of biology knowledge components. Tasks were represented and selected based on which knowledge components they addressed. Unlike earlier work, where the knowledge components and their relationships to the questions were defined by domain experts, this project demonstrated that the knowledge components, questions and their relationships could all be generated from a semantic network. An experiment found that students using our adaptive question selection had reliably larger learning gains than students who received questions in a mal-adaptive order.
KW - Adaptive learning
KW - Bayesian Knowledge Tracing
KW - adaptive test items selection
KW - question generation
KW - student modeling
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U2 - 10.1080/10494820.2016.1190939
DO - 10.1080/10494820.2016.1190939
M3 - Article
AN - SCOPUS:84975167695
SN - 1049-4820
VL - 25
SP - 828
EP - 846
JO - Interactive Learning Environments
JF - Interactive Learning Environments
IS - 7
ER -