Conference poster
Automatically Assess Elementary Students' Hand-Drawn Scientific Models Using Deep Learning of Artificial Intelligence
ISLS Annual Meeting 2023: Building Knowledge and Sustaining our Community - 17th International Conference of the Learning Sciences, ICLS 2023, Proceedings, pp 1813-1814
2023
Abstract
Because of the complexity of scoring open-end tasks, machine learning (ML) has been utilized for automatically assessing students' constructed responses. However, most existing research focuses on grading text-based responses. No studies have investigated the automatic scoring of hand-drawn models created by elementary students. In this study, we applied ML to automatically score hand-drawn scientific models developed by elementary students for evaluating knowledge-in-use. We first developed algorithms using human-scored responses and then validated these algorithms with new data. We also implemented a data augmentation technique to enhance accuracy. Our findings demonstrate the potential of the developed algorithm to achieve high performance in automatically scoring hand-drawn models.
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Details
- Title
- Automatically Assess Elementary Students' Hand-Drawn Scientific Models Using Deep Learning of Artificial Intelligence
- Creators
- Tingting Li - Michigan State UniversityFeng Liu - Michigan State UniversityJoseph Krajcik - Michigan State University
- Publication Details
- ISLS Annual Meeting 2023: Building Knowledge and Sustaining our Community - 17th International Conference of the Learning Sciences, ICLS 2023, Proceedings, pp 1813-1814
- Conference
- ISLS Annual Meeting 2023 Building Knowledge and Sustaining our Community (Montreal, Quebec, Canada, 10 Jun 2023–15 Jun 2023)
- Number of pages
- 2
- Resource Type
- Conference poster
- Language
- English
- Academic Unit
- Computer Science
- Scopus ID
- 2-s2.0-105005948953
- Other Identifier
- 991022092222604721