iCreate: mining creative thinking patterns from contextualized educational data

Nasrin Shabani; Amin Beheshti; Helia Farhood; Matt Bower; Michael Garrett; Hamid Alinejad Rokny

doi:10.1007/978-3-031-11647-6_68

iCreate: mining creative thinking patterns from contextualized educational data

Nasrin Shabani^*, Amin Beheshti, Helia Farhood, Matt Bower, Michael Garrett, Hamid Alinejad Rokny

^*Corresponding author for this work

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

4 Citations (Scopus)

Abstract

Creativity can be defined as the process of having original ideas that have value. The use of educational technology to promote creativity has attracted a great deal of attention. However, mining creative thinking patterns from educational data remains challenging. In this paper, we introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. We also evaluate our approach with a real-world dataset and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students’ activities and assessment tasks.

Original language	English
Title of host publication	Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium
Subtitle of host publication	23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, proceedings, part II
Editors	Maria Mercedes Rodrigo, Noburu Matsuda, Alexandra I. Cristea, Vania Dimitrova
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	352-356
Number of pages	5
ISBN (Electronic)	9783031116476
ISBN (Print)	9783031116469
DOIs	https://doi.org/10.1007/978-3-031-11647-6_68
Publication status	Published - 2022
Event	23rd International Conference on Artificial Intelligence in Education, AIED 2022 - Durham, United Kingdom Duration: 27 Jul 2022 → 31 Jul 2022

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	13356
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	23rd International Conference on Artificial Intelligence in Education, AIED 2022
Country/Territory	United Kingdom
City	Durham
Period	27/07/22 → 31/07/22

Keywords

Educational data mining
Creativity
Data curation

Access to Document

10.1007/978-3-031-11647-6_68

Cite this

Shabani, N., Beheshti, A., Farhood, H., Bower, M., Garrett, M., & Alinejad Rokny, H. (2022). iCreate: mining creative thinking patterns from contextualized educational data. In M. M. Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, proceedings, part II (pp. 352-356). (Lecture Notes in Computer Science; Vol. 13356). Springer, Springer Nature. https://doi.org/10.1007/978-3-031-11647-6_68

Shabani, Nasrin ; Beheshti, Amin ; Farhood, Helia et al. / iCreate : mining creative thinking patterns from contextualized educational data. Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, proceedings, part II. editor / Maria Mercedes Rodrigo ; Noburu Matsuda ; Alexandra I. Cristea ; Vania Dimitrova. Cham : Springer, Springer Nature, 2022. pp. 352-356 (Lecture Notes in Computer Science).

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title = "iCreate: mining creative thinking patterns from contextualized educational data",

abstract = "Creativity can be defined as the process of having original ideas that have value. The use of educational technology to promote creativity has attracted a great deal of attention. However, mining creative thinking patterns from educational data remains challenging. In this paper, we introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. We also evaluate our approach with a real-world dataset and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students{\textquoteright} activities and assessment tasks.",

keywords = "Educational data mining, Creativity, Data curation",

author = "Nasrin Shabani and Amin Beheshti and Helia Farhood and Matt Bower and Michael Garrett and \{Alinejad Rokny\}, Hamid",

year = "2022",

doi = "10.1007/978-3-031-11647-6\_68",

language = "English",

isbn = "9783031116469",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Springer Nature",

pages = "352--356",

editor = "Rodrigo, \{Maria Mercedes\} and Noburu Matsuda and Cristea, \{Alexandra I.\} and Vania Dimitrova",

booktitle = "Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium",

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note = "23rd International Conference on Artificial Intelligence in Education, AIED 2022 ; Conference date: 27-07-2022 Through 31-07-2022",

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Shabani, N, Beheshti, A , Farhood, H , Bower, M, Garrett, M & Alinejad Rokny, H 2022, iCreate: mining creative thinking patterns from contextualized educational data. in MM Rodrigo, N Matsuda, AI Cristea & V Dimitrova (eds), Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, proceedings, part II. Lecture Notes in Computer Science, vol. 13356, Springer, Springer Nature, Cham, pp. 352-356, 23rd International Conference on Artificial Intelligence in Education, AIED 2022, Durham, United Kingdom, 27/07/22. https://doi.org/10.1007/978-3-031-11647-6_68

iCreate: mining creative thinking patterns from contextualized educational data. / Shabani, Nasrin; Beheshti, Amin ; Farhood, Helia et al.
Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, proceedings, part II. ed. / Maria Mercedes Rodrigo; Noburu Matsuda; Alexandra I. Cristea; Vania Dimitrova. Cham: Springer, Springer Nature, 2022. p. 352-356 (Lecture Notes in Computer Science; Vol. 13356).

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

TY - GEN

T1 - iCreate

T2 - 23rd International Conference on Artificial Intelligence in Education, AIED 2022

AU - Shabani, Nasrin

AU - Beheshti, Amin

AU - Farhood, Helia

AU - Bower, Matt

AU - Garrett, Michael

AU - Alinejad Rokny, Hamid

PY - 2022

Y1 - 2022

N2 - Creativity can be defined as the process of having original ideas that have value. The use of educational technology to promote creativity has attracted a great deal of attention. However, mining creative thinking patterns from educational data remains challenging. In this paper, we introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. We also evaluate our approach with a real-world dataset and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students’ activities and assessment tasks.

AB - Creativity can be defined as the process of having original ideas that have value. The use of educational technology to promote creativity has attracted a great deal of attention. However, mining creative thinking patterns from educational data remains challenging. In this paper, we introduce a pipeline to contextualize the raw educational data, such as assessments and class activities. We also evaluate our approach with a real-world dataset and highlight how the proposed pipeline can help instructors understand creative thinking patterns from students’ activities and assessment tasks.

KW - Educational data mining

KW - Creativity

KW - Data curation

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DO - 10.1007/978-3-031-11647-6_68

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AN - SCOPUS:85135953129

SN - 9783031116469

T3 - Lecture Notes in Computer Science

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BT - Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium

A2 - Rodrigo, Maria Mercedes

A2 - Matsuda, Noburu

A2 - Cristea, Alexandra I.

A2 - Dimitrova, Vania

PB - Springer, Springer Nature

CY - Cham

Y2 - 27 July 2022 through 31 July 2022

ER -

Shabani N, Beheshti A , Farhood H , Bower M, Garrett M, Alinejad Rokny H. iCreate: mining creative thinking patterns from contextualized educational data. In Rodrigo MM, Matsuda N, Cristea AI, Dimitrova V, editors, Artificial intelligence in education. Posters and late breaking results, workshops and tutorials, industry and innovation tracks, practitioners' and doctoral consortium: 23rd International Conference, AIED 2022, Durham, UK, July 27–31, 2022, proceedings, part II. Cham: Springer, Springer Nature. 2022. p. 352-356. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-11647-6_68

iCreate: mining creative thinking patterns from contextualized educational data

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