TY - GEN
T1 - Automatic generation of problems and explanations for an intelligent algebra tutor
AU - O'Rourke, Eleanor Mary
AU - Butler, Eric
AU - Díaz Tolentino, Armando
AU - Popović, Zoran
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Intelligent tutors that emulate one-on-one tutoring with a human have been shown to effectively support student learning, but these systems are often challenging to build. Most methods for implementing tutors focus on generating intelligent explanations, rather than generating practice problems and problem progressions. In this work, we explore the possibility of using a single model of a learning domain to support the generation of both practice problems and intelligent explanations. In the domain of algebra, we show how problem generation can be supported by modeling if-then production rules in the logic programming language answer set programming. We also show how this model can be authored such that explanations can be generated directly from the rules, facilitating both worked examples and real-time feedback during independent problem-solving. We evaluate this approach through a proof-of-concept implementation and two formative user studies, showing that our generated content is of appropriate quality. We believe this approach to modeling learning domains has many exciting advantages.
AB - Intelligent tutors that emulate one-on-one tutoring with a human have been shown to effectively support student learning, but these systems are often challenging to build. Most methods for implementing tutors focus on generating intelligent explanations, rather than generating practice problems and problem progressions. In this work, we explore the possibility of using a single model of a learning domain to support the generation of both practice problems and intelligent explanations. In the domain of algebra, we show how problem generation can be supported by modeling if-then production rules in the logic programming language answer set programming. We also show how this model can be authored such that explanations can be generated directly from the rules, facilitating both worked examples and real-time feedback during independent problem-solving. We evaluate this approach through a proof-of-concept implementation and two formative user studies, showing that our generated content is of appropriate quality. We believe this approach to modeling learning domains has many exciting advantages.
KW - Answer set programming
KW - ITS
KW - Problem generation
UR - http://www.scopus.com/inward/record.url?scp=85068334833&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068334833&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-23204-7_32
DO - 10.1007/978-3-030-23204-7_32
M3 - Conference contribution
AN - SCOPUS:85068334833
SN - 9783030232030
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 383
EP - 395
BT - Artificial Intelligence in Education - 20th International Conference, AIED 2019, Proceedings
A2 - Isotani, Seiji
A2 - Millán, Eva
A2 - Ogan, Amy
A2 - McLaren, Bruce
A2 - Hastings, Peter
A2 - Luckin, Rose
PB - Springer Verlag
T2 - 20th International Conference on Artificial Intelligence in Education, AIED 2019
Y2 - 25 June 2019 through 29 June 2019
ER -