Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning

Ștefan Rușeți, R. Lala, Gabriel Gutu-Robu, Mihai Dascălu*, J.T. Jeuring, Marcell Van Geest

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

Abstract

Gamification is frequently employed in learning environments to enhance learner interactions and engagement. However, most games use pre-scripted dialogues and interactions with players, which limit their immersion and cognition. Our aim is to develop a semantic matching tool that enables users to introduce open text answers which are automatically associated with the most similar pre-scripted answer. A structured scenario written in Dutch was developed by experts for this communication experiment as a sequence of possible interactions within the environment. Semantic similarity scores computed with the SpaCy library were combined with string kernels, WordNet-based distances, and used as features in a neural network. Our experiments show that string kernels are the most predictive feature for determining the most probable pre-scripted answer, whereas neural networks obtain similar performance by combining multiple semantic similarity measures.
Original languageEnglish
Title of host publication Artificial Intelligence in Education
Subtitle of host publication20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II
EditorsSeiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin
Place of PublicationCham
PublisherSpringer
Chapter45
Pages242-246
Number of pages5
Volume2
ISBN (Electronic)9783030232078
ISBN (Print)9783030232061
DOIs
Publication statusPublished - 21 Jun 2019
Event20th International Conference on Artificial Intelligence in Education - Palmer House Hilton Hotel, Chicago, United States
Duration: 25 Jun 201929 Jun 2019
https://caed-lab.com/aied2019/

Publication series

SeriesLecture Notes in Computer Science
Volume11626
ISSN0302-9743

Conference

Conference20th International Conference on Artificial Intelligence in Education
Abbreviated titleAIED 2019
CountryUnited States
CityChicago
Period25/06/1929/06/19
Internet address

Fingerprint

Semantics
Neural networks
Experiments
Communication

Keywords

  • answer matching
  • semantic similarity
  • natural language processing
  • Neural network

Cite this

Rușeți, Ș., Lala, R., Gutu-Robu, G., Dascălu, M., Jeuring, J. T., & Van Geest, M. (2019). Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II (Vol. 2, pp. 242-246). Cham: Springer. Lecture Notes in Computer Science, Vol.. 11626 https://doi.org/10.1007/978-3-030-23207-8_45
Rușeți, Ștefan ; Lala, R. ; Gutu-Robu, Gabriel ; Dascălu, Mihai ; Jeuring, J.T. ; Van Geest, Marcell. / Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning. Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. editor / Seiji Isotani ; Eva Millán ; Amy Ogan ; Peter Hastings ; Bruce McLaren ; Rose Luckin. Vol. 2 Cham : Springer, 2019. pp. 242-246 (Lecture Notes in Computer Science, Vol. 11626).
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abstract = "Gamification is frequently employed in learning environments to enhance learner interactions and engagement. However, most games use pre-scripted dialogues and interactions with players, which limit their immersion and cognition. Our aim is to develop a semantic matching tool that enables users to introduce open text answers which are automatically associated with the most similar pre-scripted answer. A structured scenario written in Dutch was developed by experts for this communication experiment as a sequence of possible interactions within the environment. Semantic similarity scores computed with the SpaCy library were combined with string kernels, WordNet-based distances, and used as features in a neural network. Our experiments show that string kernels are the most predictive feature for determining the most probable pre-scripted answer, whereas neural networks obtain similar performance by combining multiple semantic similarity measures.",
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Rușeți, Ș, Lala, R, Gutu-Robu, G, Dascălu, M, Jeuring, JT & Van Geest, M 2019, Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning. in S Isotani, E Millán, A Ogan, P Hastings, B McLaren & R Luckin (eds), Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. vol. 2, Springer, Cham, Lecture Notes in Computer Science, vol. 11626, pp. 242-246, 20th International Conference on Artificial Intelligence in Education, Chicago, United States, 25/06/19. https://doi.org/10.1007/978-3-030-23207-8_45

Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning. / Rușeți, Ștefan; Lala, R.; Gutu-Robu, Gabriel; Dascălu, Mihai; Jeuring, J.T.; Van Geest, Marcell.

Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. ed. / Seiji Isotani; Eva Millán; Amy Ogan; Peter Hastings; Bruce McLaren; Rose Luckin. Vol. 2 Cham : Springer, 2019. p. 242-246 (Lecture Notes in Computer Science, Vol. 11626).

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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AB - Gamification is frequently employed in learning environments to enhance learner interactions and engagement. However, most games use pre-scripted dialogues and interactions with players, which limit their immersion and cognition. Our aim is to develop a semantic matching tool that enables users to introduce open text answers which are automatically associated with the most similar pre-scripted answer. A structured scenario written in Dutch was developed by experts for this communication experiment as a sequence of possible interactions within the environment. Semantic similarity scores computed with the SpaCy library were combined with string kernels, WordNet-based distances, and used as features in a neural network. Our experiments show that string kernels are the most predictive feature for determining the most probable pre-scripted answer, whereas neural networks obtain similar performance by combining multiple semantic similarity measures.

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Rușeți Ș, Lala R, Gutu-Robu G, Dascălu M, Jeuring JT, Van Geest M. Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning. In Isotani S, Millán E, Ogan A, Hastings P, McLaren B, Luckin R, editors, Artificial Intelligence in Education: 20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II. Vol. 2. Cham: Springer. 2019. p. 242-246. (Lecture Notes in Computer Science, Vol. 11626). https://doi.org/10.1007/978-3-030-23207-8_45