Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Artificial Intelligence in Education |
| Untertitel | 24th International Conference, AIED 2023, Proceedings |
| Herausgeber/-innen | Ning Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 824-829 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 978-3-031-36272-9 |
| ISBN (Print) | 9783031362712 |
| Publikationsstatus | Veröffentlicht - 26 Juni 2023 |
| Veranstaltung | 24th International Conference on Artificial Intelligence in Education, AIED 2023 - Tokyo, Japan Dauer: 3 Juli 2023 → 7 Juli 2023 |
Publikationsreihe
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Band | 13916 LNAI |
| ISSN (Print) | 0302-9743 |
| ISSN (elektronisch) | 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.
ASJC Scopus Sachgebiete
- Mathematik (insg.)
- Theoretische Informatik
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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Artificial Intelligence in Education: 24th International Conference, AIED 2023, Proceedings. Hrsg. / Ning Wang; Genaro Rebolledo-Mendez; Noboru Matsuda; Olga C. Santos; Vania Dimitrova. Cham: Springer Science and Business Media Deutschland GmbH, 2023. S. 824-829 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 13916 LNAI).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › 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 -