A systematic review of computational thinking in science classrooms

Figures & data

Table 1. List of the 23 selected articles reviewed in this study.

No.	Authors and year	Title	Context	Subject learning area
1.	Tucker-Raymond et al. (2019)-	“I Broke Your Game!”: Critique among Middle Schoolers Designing Computer Games about Climate Change	Middle school	Climate science
2.	Peel and Friedrichsen (2018)	Algorithms, Abstractions, and Iterations: Teaching Computational Thinking Using Protein Synthesis Translation.	High school	Biology
3.	Kaya et al. (2019)	Animatronic Lions, and Tigers, and Bears, Oh My! How computational thinking and 3D printing can help students create an animatronic zoo.	Elementary school	Elementary science
4.	Hutchins et al. (2020).	C2STEM: A System for Synergistic Learning of Physics and Computational Thinking	High school	Physics
5.	Aksit and Wiebe (2020)	Exploring Force and Motion Concepts in Middle Grades Using Computational Modelling: a Classroom Intervention Study	Middle school	Physics
6.	Luo et al. (2020).	Exploring the evolution of two girls’ conceptions and practices in computational thinking in science	Elementary school	Life science
7.	Basu et al. (2016).	Identifying middle school students’ challenges in computational thinking-based science learning	Middle school	Physics and Biology
8.	Waterman et al. (2020)	Integrating Computational Thinking into Elementary Science Curriculum: An Examination of Activities That Support Students’ Computational Thinking in the Service of Disciplinary Learning	Elementary school	Biology
9.	Sengupta et al. (2013).	Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework	Middle school	Physics and Biology
10.	Irgens et al. (2020).	Modelling and Measuring High School Students’ Computational Thinking Practices in Science	High school	Biology
11.	Dickes et al. (2020)	Sociomathematical Norms for Integrating Coding and Modelling with Elementary Science: A Dialogical Approach	Elementary school	Elementary science (kinematics and Ecology)
12.	Garneli and Chorianopoulos (2019)	The effects of video game making within science content on student computational thinking skills and performance	Middle school	Physics
13.	Basawapatna (2016)	Alexander Meets Michotte: A Simulation Tool Based on Pattern Programming and Phenomenology.	Middle school	Life science
14.	Yin et al. (2020)	Improving and Assessing Computational Thinking in Maker Activities: the Integration with Physics and Engineering Learning.	High school	Physics and Engineering
15.	Lui et al. (2020)	Communicating computational concepts and practices within high school students’ portfolios of making electronic textiles.	High school	Life science
16.	Boulden et al. (2018).	Computational Thinking Integration into Middle Grades Science Classrooms: Strategies for Meeting the Challenges.	Middle school	Life sciences
17.	Yadav et al. (2018)	Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12 Classrooms.	K- 12 classrooms	STEAM
18.	Clark and Sengupta (2020).	Reconceptualising games for integrating computational thinking and science as practice: collaborative agent-based disciplinarily integrated games	Middle school	STEM disciplines
19.	Sullivan and Heffernan (2016)	Robotic construction kits as computational manipulatives for learning in the STEM disciplines	P − 12	Physics and Biology
20.	Lee et al. (2020).	Computational Thinking from a Disciplinary Perspective: Integrating Computational Thinking in K-12 Science, Technology, Engineering, and Mathematics Education	K − 12 Classroom	STEM
21.	Weintrop et al. (2016)	Defining Computational Thinking for Mathematics and Science Classrooms	High school	Science and Mathematics
22.	Martin and Jacobsen (2018)	Coding and Computational Thinking in Maths and Science.	Middle and High school	Science and Maths
23.	Lee et al. (2014)	Integrating computational thinking across the K-8 curriculum	Middle school	Life science

Figure 1. Study selection chart (Adapted from Moher et al.’s (2009) PRISMA Framework.

Figure 2. Article distribution based on the methodology.

Table 2. Distribution of Data Collection Methods.

Method	Frequency
Tests	7
Literature review	5
Interviews	11
Observation	9
Student Portfolios	10
Survey	3

Figure 3. Article distribution based on subject areas.

Figure 4. Article distribution based on the school level.

Table 3. Summary of constructs used in incorporating CT in science teaching.

Computational Thinking Constructs	Frequency
Computational Concepts Branching Conditionals Data Events Loop Operators Parallelism Random values Repetition Sequences Variable	1 7 3 3 7 3 6 1 1 3 5
Computational Practices Abstractions Algorithm thinking Automation, Computational problem-solving practices Coordination & synchronisation Data practices Debugging Decomposition Defining approximations Experimentation Inventing measures Iteration/iterative design Refinement Revising Logical thinking Modelling and simulation practices Synchronisation Pattern generalization/recognition Place-measure-point command System thinking practices Troubleshooting User interactivity	10 7 1 2 1 6 8 6 1 1 1 2 4 1 1 1 1 1 5 1 2 1 1
Computational Perspectives Collaborating Connecting Dealing with practical situations Expressing	1 1 3 1

Table 4. Categories of strategies used in integrating CT in science classrooms.

Approaches	Definition	Frequency
Design-based learning	It is a form of inquiry/project learning which involves the integration of design thinking/activities, process, and projects/games into the classroom to foster students’ creativity, problem-solving skills, and engagement in real-world, cross-curricular tasks (Scheltenaar et al., 2015).	5
Game-based learning	Game-based learning is defined as the implementation of game elements such as core mechanics, challenges, and goals into real-life settings to enhance learning (M.C. Li & Tsai, 2013).	4
Inquiry-based learning	Inquiry-based learning is a form of active learning that involves a process of investigation, problem formulation and solving, as well as the construction of knowledge for better understanding (Pedaste et al., 2015).	4
Modelling based learning	Modelling based learning refers to learning from models, learning with models, and learning by modelling (Spector, 2009). It involves the use of instructions to support the development and evolution of learners’ mental models as representations of physical phenomena and integrated knowledge that includes components of a dynamic system and their interactions (Louca & Zacharia, 2012).	7
Project-based learning	Project-based learning (PBL) is a model that organises learning around the creation of presentations, projects and/or products involving complex tasks, based on challenging questions or problems, that involve students in design, problem-solving, decision making, or investigative activities. PBL allows students to work relatively autonomously over extended periods and culminates in realistic products or presentations (Kokotsaki et al., 2016).	4
Problem-based learning	Problem-based learning is an approach in which students work in groups to learn about a subject, concept, and/or principles using open-ended or complex real-world problems (McGuinness, 2011).	2

Table 5. Summary of tools used in integrating CT.

Teaching tools	Frequency
Block-based Blockly Scratch Snap	1 6 1
Agent-based C2STEM CTSim Simulation creation tool kit Starlogo ViMAP	1 2 1 1 1
Data Processing iSENSE Spreadsheet	1 2
Makerspace	2

Table 6. Distribution of assessment techniques.

Assessment	Frequency
Achievement test	11
Surveys/questionnaires	4
Interviews	12
Observations	7
Portfolio assessment	13

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