Applying artificial intelligence to science education

Date:
October 7, 2020
Source:
Wiley
Summary:
A new review highlights the potential of machine learning--a subset
of artificial intelligence -- in science education.



FULL STORY ==========================================================================
A new review published in the Journal of Research in Science Teaching highlights the potential of machine learning -- a subset of artificial intelligence -- in science education. Although the authors initiated
their review before the COVID-19 outbreak, the pandemic highlights the
need to examine cutting-edge digital technologies as we re-think the
future of teaching and learning.


========================================================================== Based on a review of 47 studies, investigators developed a framework to conceptualize machine learning applications in science assessment. The
article aims to examine how machine learning has revolutionized
the capacity of science assessment in terms of tapping into complex
constructs, improving assessment functionality, and facilitating scoring automaticity.

Based on their investigation, the researchers identified various ways in
which machine learning has transformed traditional science assessment,
as well as anticipated impacts that it will likely have in the future
(such as providing personalized science learning and changing the process
of educational decision- making).

"Machine learning is increasingly impacting every aspect of our lives, including education," said lead author Xiaoming Zhai, an assistant
professor in the University of Georgia's Mary Frances Early's Department
of Mathematics and Science Education. "It is anticipated that the
cutting-edge technology may be able to redefine science assessment
practices and significantly change education in the future."

========================================================================== Story Source: Materials provided by Wiley. Note: Content may be edited
for style and length.


========================================================================== Journal Reference:
1. Xiaoming Zhai, Kevin Haudek, Lehong Shi, Ross Nehm, Mark
Urban‐Lurain. From substitution to redefinition: A framework
of machine learning‐based science assessment. Journal of
Research in Science Teaching, 2020; DOI: 10.1002/tea.21658 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2020/10/201007085624.htm

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