Details
| Original language | English |
|---|---|
| Title of host publication | Artificial Intelligence in Education |
| Subtitle of host publication | 24th International Conference, AIED 2023, Proceedings |
| Editors | Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova |
| Place of Publication | Cham |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 824-829 |
| Number of pages | 6 |
| ISBN (electronic) | 978-3-031-36272-9 |
| ISBN (print) | 9783031362712 |
| Publication status | Published - 26 Jun 2023 |
| Event | 24th International Conference on Artificial Intelligence in Education, AIED 2023 - Tokyo, Japan Duration: 3 Jul 2023 → 7 Jul 2023 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 13916 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Automatic analyses of student drawings in chemistry education have the potential to support classroom teaching. To date, related work on handwritten chemical structures or formulas is limited to well-defined presentation formats, e.g., Lewis structures. However, the large variety of possible illustrations in student drawings in chemical education has not been addressed yet. In this paper, we present a novel approach to identify visual primitives in student drawings from chemistry classes. Since the field lacks suitable datasets for the given task, we introduce a method to synthetically create a dataset for visual primitives. We demonstrate how detected visual primitives can be used to automatically classify drawings according to a taxonomy of drawing characteristics in chemistry and physics. Our experiments show that (1) the detection of visual primitives in student drawings, and (2) the subsequent classification of chemistry- and physics-specific drawing characteristics is possible.
Keywords
- Chemistry Education, Computer Vision, Synthetic Data, Visual Analysis, Visual Primitives
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Artificial Intelligence in Education: 24th International Conference, AIED 2023, Proceedings. ed. / Ning Wang; Genaro Rebolledo-Mendez; Noboru Matsuda; Olga C. Santos; Vania Dimitrova. Cham: Springer Science and Business Media Deutschland GmbH, 2023. p. 824-829 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13916 LNAI).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Automatic Analysis of Student Drawings in Chemistry Classes
AU - Stamatakis, Markos
AU - Gritz, Wolfgang
AU - Oldag, Jos
AU - Hoppe, Anett
AU - Schanze, Sascha
AU - Ewerth, Ralph
N1 - Funding Information: This work has been mainly supported by the Ministry of Science and Culture of Lower Saxony, Germany, through the PhD Program “LernMINT: Data-assisted classroom teaching in the MINT subjects”.
PY - 2023/6/26
Y1 - 2023/6/26
N2 - Automatic analyses of student drawings in chemistry education have the potential to support classroom teaching. To date, related work on handwritten chemical structures or formulas is limited to well-defined presentation formats, e.g., Lewis structures. However, the large variety of possible illustrations in student drawings in chemical education has not been addressed yet. In this paper, we present a novel approach to identify visual primitives in student drawings from chemistry classes. Since the field lacks suitable datasets for the given task, we introduce a method to synthetically create a dataset for visual primitives. We demonstrate how detected visual primitives can be used to automatically classify drawings according to a taxonomy of drawing characteristics in chemistry and physics. Our experiments show that (1) the detection of visual primitives in student drawings, and (2) the subsequent classification of chemistry- and physics-specific drawing characteristics is possible.
AB - Automatic analyses of student drawings in chemistry education have the potential to support classroom teaching. To date, related work on handwritten chemical structures or formulas is limited to well-defined presentation formats, e.g., Lewis structures. However, the large variety of possible illustrations in student drawings in chemical education has not been addressed yet. In this paper, we present a novel approach to identify visual primitives in student drawings from chemistry classes. Since the field lacks suitable datasets for the given task, we introduce a method to synthetically create a dataset for visual primitives. We demonstrate how detected visual primitives can be used to automatically classify drawings according to a taxonomy of drawing characteristics in chemistry and physics. Our experiments show that (1) the detection of visual primitives in student drawings, and (2) the subsequent classification of chemistry- and physics-specific drawing characteristics is possible.
KW - Chemistry Education
KW - Computer Vision
KW - Synthetic Data
KW - Visual Analysis
KW - Visual Primitives
UR - http://www.scopus.com/inward/record.url?scp=85164970202&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-36272-9_78
DO - 10.1007/978-3-031-36272-9_78
M3 - Conference contribution
AN - SCOPUS:85164970202
SN - 9783031362712
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 824
EP - 829
BT - Artificial Intelligence in Education
A2 - Wang, Ning
A2 - Rebolledo-Mendez, Genaro
A2 - Matsuda, Noboru
A2 - Santos, Olga C.
A2 - Dimitrova, Vania
PB - Springer Science and Business Media Deutschland GmbH
CY - Cham
T2 - 24th International Conference on Artificial Intelligence in Education, AIED 2023
Y2 - 3 July 2023 through 7 July 2023
ER -