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

doi:10.1007/978-3-030-23207-8_45

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 Line Learning (part of LIRS program)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-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 language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	20th International Conference, AIED 2019, Chicago, IL, USA, June 25-29, 2019, Proceedings, Part II
Editors	Seiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin
Place of Publication	Cham
Publisher	Springer
Chapter	45
Pages	242-246
Number of pages	5
Volume	2
ISBN (Electronic)	9783030232078
ISBN (Print)	9783030232061
DOIs	https://doi.org/10.1007/978-3-030-23207-8_45
Publication status	Published - 21 Jun 2019
Event	20th International Conference on Artificial Intelligence in Education - Palmer House Hilton Hotel, Chicago, United States Duration: 25 Jun 2019 → 29 Jun 2019 https://caed-lab.com/aied2019/

Publication series

Series	Lecture Notes in Computer Science
Volume	11626
ISSN	0302-9743

Conference

Conference	20th International Conference on Artificial Intelligence in Education
Abbreviated title	AIED 2019
Country	United States
City	Chicago
Period	25/06/19 → 29/06/19
Internet address	https://caed-lab.com/aied2019/

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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). 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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title = "Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning",

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.",

keywords = "answer matching, semantic similarity, natural language processing, Neural network",

author = "Ștefan Rușeți and R. Lala and Gabriel Gutu-Robu and Mihai Dasc{\u a}lu and J.T. Jeuring and {Van Geest}, Marcell",

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doi = "10.1007/978-3-030-23207-8_45",

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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 proceeding › Conference article in proceeding › Academic › peer-review

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T1 - Semantic Matching of Open Texts to Pre-scripted Answers in Dialogue-Based Learning

AU - Rușeți, Ștefan

AU - Lala, R.

AU - Gutu-Robu, Gabriel

AU - Dascălu, Mihai

AU - Jeuring, J.T.

AU - Van Geest, Marcell

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N2 - 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.

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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BT - Artificial Intelligence in Education

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A2 - Hastings, Peter

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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

Access to Document

10.1007/978-3-030-23207-8_45