Please use this identifier to cite or link to this item:
Title: Personalized Automated Formative Feedback Can Support Students in Generating Causal Explanations in Biology
Authors: Ariely, Moriah
Nazaretsky, Tanya
Alexandron, Giora
Keywords: Learning Sciences
Issue Date: 2022
Publisher: International Society of the Learning Sciences
Citation: Ariely, M., Nazaretsky, T., & Alexandron, G. (2022). Personalized automated formative feedback can support students in generating causal explanations in biology. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 953-956). International Society of the Learning Sciences.
Abstract: The ability to formulate scientific explanations is considered a major goal in science education. However, explaining scientific phenomena is a challenging task, and research has shown that many students are unable to construct proper explanations. And while personalized formative feedback can greatly assist students in closing the gaps between their current and the expected level of reasoning, teachers rarely find the time that is required to provide it. We present a novel framework for designing and providing formative feedback on causal explanations in biology, which can be generated automatically using NLP-based algorithms. Results from a controlled experiment showed that applying this framework led to significant improvement in student explanations. Based on these findings, we believe that our framework provides an effective approach for aligning between automated NLP-based analysis and formative assessment of biological explanations in the science classroom.
Description: Short Paper
Appears in Collections:ISLS Annual Meeting 2022

Files in This Item:
File SizeFormat 
ICLS2022_953-956.pdf334.06 kBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.