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Title: Automatically Assess Elementary Students’ Hand-Drawn Scientific Models Using Deep Learning of Artificial Intelligence
Authors: Li, Tingting
Liu, Feng
Krajcik, Joseph
Keywords: Learning Sciences
Issue Date: 2023
Publisher: International Society of the Learning Sciences
Citation: Li, T., Liu, F., & Krajcik, J. (2023). Automatically assess elementary students’ hand-drawn scientific models using deep learning of artificial intelligence. In Blikstein, P., Van Aalst, J., Kizito, R., & Brennan, K. (Eds.), Proceedings of the 17th International Conference of the Learning Sciences - ICLS 2023 (pp. 1813-1814). International Society of the Learning Sciences.
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.
Description: Poster
Appears in Collections:ISLS Annual Meeting 2023

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