Please use this identifier to cite or link to this item:
https://repository.isls.org//handle/1/6393
Title: | HERA: Exploring the Power of Adaptive Scaffolding on Scientific Argumentation and Modelling Competencies in Online Learning Systems |
Authors: | Rosen, Yigal Arieli-Attali, Meirav Ward, Susan Seery, Jennifer Simmering, Vanessa Ozersky, Laurel Stoeffler, Kristin Webster, Kacy von Davier, Alina |
Keywords: | Design |
Issue Date: | Jun-2020 |
Publisher: | International Society of the Learning Sciences (ISLS) |
Citation: | Rosen, Y., Arieli-Attali, M., Ward, S., Seery, J., Simmering, V., Ozersky, L., Stoeffler, K., Webster, K., & von Davier, A. (2020). HERA: Exploring the Power of Adaptive Scaffolding on Scientific Argumentation and Modelling Competencies in Online Learning Systems. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 3 (pp. 1665-1668). Nashville, Tennessee: International Society of the Learning Sciences. |
Abstract: | Scientific argumentation addresses how arguments are structured from evidence and is a central process in scientific enquiry and modeling. Although scientific argumentation is considered one of the critical competencies in the U.S. Next Generation Science Standards and internationally there is lack of effective and scalable adaptive online learning systems for students to practice the competencies, including mechanistic reasoning, across domains of scientific knowledge and contexts. In this paper we introduce an experimental framework aimed to facilitate the exploration of the effects of adaptive scaffolding in online learning systems on students’ ability to develop mechanistic reasoning in scientific argumentation. We describe findings from a pilot study and discuss next steps in research and development of adaptive scaffolding in online learning systems. |
URI: | https://doi.dx.org/10.22318/icls2020.1665 https://repository.isls.org//handle/1/6393 |
Appears in Collections: | ICLS 2020 |
Files in This Item:
File | Size | Format | |
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1665-1668.pdf | 256.06 kB | Adobe PDF | View/Open |
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