Assessment2Vec: learning distributed representations of assessments to reduce marking workload

Shuang Wang; Amin Beheshti; Yufei Wang; Jianchao Lu; Quan Z. Sheng; Stephen Elbourn; Hamid Alinejad-Rokny; Elizabeth Galanis

doi:10.1007/978-3-030-78270-2_68

Assessment2Vec: learning distributed representations of assessments to reduce marking workload

Shuang Wang^*, Amin Beheshti^*, Yufei Wang, Jianchao Lu, Quan Z. Sheng, Stephen Elbourn, Hamid Alinejad-Rokny, Elizabeth Galanis

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

4 Citations (Scopus)

Abstract

Reducing instructors workload in online and large-scale learning environments could be one of the most important factors in educational systems. To address this challenge, techniques such as Artificial Intelligence has been considered in tutoring systems and automatic essay scoring tasks. In this paper, we construct a novel model to enable learning distributed representations of assessments namely Assessment2Vec and mark assessments automatically with Supervised Contrastive Learning loss which will effectively reduce instructors’ workload in marking large number of assessments. The experimental results based on the real-world datasets show the effectiveness of the proposed approach.

Original language	English
Title of host publication	Artificial Intelligence in Education
Subtitle of host publication	22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021: Proceedings, Part II
Editors	Ido Roll, Danielle McNamara, Sergey Sosnovsky, Rose Luckin, Vania Dimitrova
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	384-389
Number of pages	6
ISBN (Electronic)	9783030782702
ISBN (Print)	9783030782696
DOIs	https://doi.org/10.1007/978-3-030-78270-2_68
Publication status	Published - 2021
Event	22nd International Conference on Artificial Intelligence in Education, AIED 2021 - Virtual, Online Duration: 14 Jun 2021 → 18 Jun 2021

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	12749
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Artificial Intelligence in Education, AIED 2021
City	Virtual, Online
Period	14/06/21 → 18/06/21

Keywords

Assessment2Vec
Assessment marking
Natural language processing
Supervised contrastive learning
AI-enabled education

Access to Document

10.1007/978-3-030-78270-2_68

Cite this

Wang, S., Beheshti, A., Wang, Y., Lu, J., Sheng, Q. Z., Elbourn, S., Alinejad-Rokny, H., & Galanis, E. (2021). Assessment2Vec: learning distributed representations of assessments to reduce marking workload. In I. Roll, D. McNamara, S. Sosnovsky, R. Luckin, & V. Dimitrova (Eds.), Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021: Proceedings, Part II (pp. 384-389). (Lecture Notes in Computer Science; Vol. 12749). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-78270-2_68

Wang, Shuang ; Beheshti, Amin ; Wang, Yufei et al. / Assessment2Vec : learning distributed representations of assessments to reduce marking workload. Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021: Proceedings, Part II. editor / Ido Roll ; Danielle McNamara ; Sergey Sosnovsky ; Rose Luckin ; Vania Dimitrova. Cham, Switzerland : Springer, Springer Nature, 2021. pp. 384-389 (Lecture Notes in Computer Science).

@inproceedings{36449510248649329814b99b1c96d6ca,

title = "Assessment2Vec: learning distributed representations of assessments to reduce marking workload",

abstract = "Reducing instructors workload in online and large-scale learning environments could be one of the most important factors in educational systems. To address this challenge, techniques such as Artificial Intelligence has been considered in tutoring systems and automatic essay scoring tasks. In this paper, we construct a novel model to enable learning distributed representations of assessments namely Assessment2Vec and mark assessments automatically with Supervised Contrastive Learning loss which will effectively reduce instructors{\textquoteright} workload in marking large number of assessments. The experimental results based on the real-world datasets show the effectiveness of the proposed approach.",

keywords = "Assessment2Vec, Assessment marking, Natural language processing, Supervised contrastive learning, AI-enabled education",

author = "Shuang Wang and Amin Beheshti and Yufei Wang and Jianchao Lu and Sheng, \{Quan Z.\} and Stephen Elbourn and Hamid Alinejad-Rokny and Elizabeth Galanis",

year = "2021",

doi = "10.1007/978-3-030-78270-2\_68",

language = "English",

isbn = "9783030782696",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Springer Nature",

pages = "384--389",

editor = "Ido Roll and Danielle McNamara and Sergey Sosnovsky and Rose Luckin and Vania Dimitrova",

booktitle = "Artificial Intelligence in Education",

address = "United States",

note = "22nd International Conference on Artificial Intelligence in Education, AIED 2021 ; Conference date: 14-06-2021 Through 18-06-2021",

}

Wang, S, Beheshti, A, Wang, Y, Lu, J, Sheng, QZ, Elbourn, S, Alinejad-Rokny, H & Galanis, E 2021, Assessment2Vec: learning distributed representations of assessments to reduce marking workload. in I Roll, D McNamara, S Sosnovsky, R Luckin & V Dimitrova (eds), Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021: Proceedings, Part II. Lecture Notes in Computer Science, vol. 12749, Springer, Springer Nature, Cham, Switzerland, pp. 384-389, 22nd International Conference on Artificial Intelligence in Education, AIED 2021, Virtual, Online, 14/06/21. https://doi.org/10.1007/978-3-030-78270-2_68

Assessment2Vec: learning distributed representations of assessments to reduce marking workload. / Wang, Shuang; Beheshti, Amin; Wang, Yufei et al.
Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021: Proceedings, Part II. ed. / Ido Roll; Danielle McNamara; Sergey Sosnovsky; Rose Luckin; Vania Dimitrova. Cham, Switzerland: Springer, Springer Nature, 2021. p. 384-389 (Lecture Notes in Computer Science; Vol. 12749).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - Assessment2Vec

T2 - 22nd International Conference on Artificial Intelligence in Education, AIED 2021

AU - Wang, Shuang

AU - Beheshti, Amin

AU - Wang, Yufei

AU - Lu, Jianchao

AU - Sheng, Quan Z.

AU - Elbourn, Stephen

AU - Alinejad-Rokny, Hamid

AU - Galanis, Elizabeth

PY - 2021

Y1 - 2021

N2 - Reducing instructors workload in online and large-scale learning environments could be one of the most important factors in educational systems. To address this challenge, techniques such as Artificial Intelligence has been considered in tutoring systems and automatic essay scoring tasks. In this paper, we construct a novel model to enable learning distributed representations of assessments namely Assessment2Vec and mark assessments automatically with Supervised Contrastive Learning loss which will effectively reduce instructors’ workload in marking large number of assessments. The experimental results based on the real-world datasets show the effectiveness of the proposed approach.

AB - Reducing instructors workload in online and large-scale learning environments could be one of the most important factors in educational systems. To address this challenge, techniques such as Artificial Intelligence has been considered in tutoring systems and automatic essay scoring tasks. In this paper, we construct a novel model to enable learning distributed representations of assessments namely Assessment2Vec and mark assessments automatically with Supervised Contrastive Learning loss which will effectively reduce instructors’ workload in marking large number of assessments. The experimental results based on the real-world datasets show the effectiveness of the proposed approach.

KW - Assessment2Vec

KW - Assessment marking

KW - Natural language processing

KW - Supervised contrastive learning

KW - AI-enabled education

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DO - 10.1007/978-3-030-78270-2_68

M3 - Conference proceeding contribution

AN - SCOPUS:85123429734

SN - 9783030782696

T3 - Lecture Notes in Computer Science

SP - 384

EP - 389

BT - Artificial Intelligence in Education

A2 - Roll, Ido

A2 - McNamara, Danielle

A2 - Sosnovsky, Sergey

A2 - Luckin, Rose

A2 - Dimitrova, Vania

PB - Springer, Springer Nature

CY - Cham, Switzerland

Y2 - 14 June 2021 through 18 June 2021

ER -

Wang S, Beheshti A, Wang Y, Lu J, Sheng QZ, Elbourn S et al. Assessment2Vec: learning distributed representations of assessments to reduce marking workload. In Roll I, McNamara D, Sosnovsky S, Luckin R, Dimitrova V, editors, Artificial Intelligence in Education: 22nd International Conference, AIED 2021, Utrecht, The Netherlands, June 14–18, 2021: Proceedings, Part II. Cham, Switzerland: Springer, Springer Nature. 2021. p. 384-389. (Lecture Notes in Computer Science). doi: 10.1007/978-3-030-78270-2_68

Assessment2Vec: learning distributed representations of assessments to reduce marking workload

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