College of Education and Human Development - George Mason University

School of Education Faculty Author Article Chosen for NSTA/NARST “Research Worth Reading” Recognition

February 22, 2024

Erin Peters-Burton
Erin Peters-Burton

An article authored by faculty from the School of Education at George Mason University recently received recognition as “Research Worth Reading” from the National Science Teaching Association (NSTA) and the National Association for Research in Science Teaching (NARST). Appearing in the August 2023 issue of the Journal of Research in Science Teaching, the article is titled “High school biology teachers’ integration of computational thinking into data practices to support student investigations.” The article was written by Erin Peters-Burton, who is the Donna R. and David E. Sterling Endowed Professor in Science Education at Mason, and Anastasia Kitsantas, professor in Mason’s Educational Psychology program and a recently named American Educational Research Association Fellow. Co-authoring the article were Peter Jacob Rich, faculty with the Department of Instructional Psychology and Technology at Brigham Young University, and doctoral graduates Stephanie Stehle and Laura Laclede, both of whom received a PhD in Education at Mason.

Anastasia Kitsantas
Anastasia Kitsantas

The “Research Worth Reading” recognition is given to three papers that have been published in the Journal of Research in Science Teaching in the past year and which are considered to have the most significant implications for science educators and practitioners. The article written by Mason faculty and their co-authors describes the learning outcomes from teacher partners during an NSF-funded Researcher Practitioner Partnership.

The research upon which the article is based focuses on the concept of computational thinking and how science teachers can integrate this way of thinking into their teaching practices and curriculum. The Next Generation Science Standards, based on the Framework for K-12 Science Education developed by the National Research Council, directs teachers to incorporate computational thinking into their instruction.

As the authors explain in the article, computational thinking is an approach to problem-solving consisting of five key components: decomposition (breaking down a large, complex problem into smaller, more manageable pieces), pattern recognition (identifying recurring similarities in data), algorithms (establishing a logical series of sequential steps or instructions to solve a problem), abstraction (identifying details relevant to the problem and eliminating unnecessary information), and automation (using technology or software to automate data practices). Studies show that computational thinking can improve the problem-solving capabilities and complex thinking skills of students, enhance their aptitude in working with data, and encourage more active engagement in student-centered, experiential learning.

The authors of the article note that despite its benefits, some teachers may have misconceptions of what constitutes computational thinking or may find certain data practices more difficult to integrate into the classroom than others. The article describes a study of nine high school biology teachers who reported that they felt comfortable with incorporating decomposition and pattern recognition practices into their teaching but were less confident developing lesson plans that integrated other data practices such as algorithms, abstraction, and automation.

The authors of the article recommend that professional development should be designed “to engage teachers with integrating decomposition and pattern finding first, then building up to integration of algorithmic thinking, abstraction, and automation with support and examples.” In addition, the authors emphasize that professional development should be provided to help educators across all science disciplines become more proficient with computational thinking data practices and that the teacher’s content area should be considered when designing this instruction.

To read this article on the integration of computational thinking data practices in teaching, read this article.