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https://repository.isls.org//handle/1/8793
Title: | Introducing Authentic Datasets Into the Biology Classroom: Teachers’ Considerations When Designing Digital Instruction Units |
Authors: | Bar, Carmel Dorfman, Bat-Shahar Yarden, Anat |
Keywords: | Learning Sciences |
Issue Date: | 2022 |
Publisher: | International Society of the Learning Sciences |
Citation: | Bar, C., Dorfman, B., & Yarden, A. (2022). Introducing authentic datasets into the biology classroom: Teachers’ considerations when designing digital instruction units. In Chinn, C., Tan, E., Chan, C., & Kali, Y. (Eds.), Proceedings of the 16th International Conference of the Learning Sciences - ICLS 2022 (pp. 227-234). International Society of the Learning Sciences. |
Abstract: | Introducing datasets into the science classroom may facilitate developing scientific practices and epistemic thinking. However, authentic datasets are rarely used in high school, and little is known about biology teachers’ knowledge considerations regarding dataset-driven instruction. Here we investigated these knowledge considerations in high-school biology teachers. Forty teachers designed dataset-driven instruction units and completed open-ended questionnaires during a professional development workshop. Our analysis, based on the PISA framework of scientific literacy, indicated that although epistemic considerations were the most prominent of teachers' perceived benefits of dataset-driven instruction, they were less pronounced than content and procedural considerations in the instruction units that they designed. This suggests that although teachers recognize the value of epistemic thinking and see authentic datasets as a means to facilitate scientific literacy, further effort is needed to assist teachers in designing instruction units which better reflect these goals. |
Description: | Long Paper |
URI: | https://dx.doi.org/10.22318/icls2022.227 https://repository.isls.org//handle/1/8793 |
Appears in Collections: | ISLS Annual Meeting 2022 |
Files in This Item:
File | Size | Format | |
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ICLS2022_227-234.pdf | 395.07 kB | Adobe PDF | View/Open |
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