Computational thinking (CT), derived from computer science, has now been actively discussed in the field of education overall, often involving computer programming. Despite national attention on CT and the countless endeavors to tie CT into school curricula, teachers have received relatively little or limited teacher preparation on what CT is and what it entails for student learning (Israel et al., 2015; Jeong & Kim, 2017; Magen-Nagar & Firstater, 2019; Xie et al., 2017). It was mainly understood as a part of computer programming (i.e., coding) and taught by involving learners in the context related to programming, which learners with little to no prior experience cannot approach easily. Teaching children with no prior experience in CT through computer programming-related activities may even more drastically marginalize who have been already historically underrepresented in STEM education (e.g., female, minority students, English as second language learners, and children with disabilities).

Unplugged CT learning—a CT instructional strategy that does not require computer use—is a powerful way to bridge local classrooms and computer programming for young children! It has been actively studied and applied to classrooms with support from teachers. Unplugged CT learning is designed in a way to use hands-on learning involving multiple representations of information. Children can use multiple representations as the different ways to represent their ideas or understandings. (Some examples of multiple representations can be pictures, colors, symbols, verbal/written texts, gestures, and embodied movements). One great example is using real-world concrete materials, like having children make a peanut butter sandwich while having them think about step-by-step processes. These unplugged approaches can even encourage children to use those other representations like pictures, colors, symbols, written texts, gestures, and embodied movements. Research has shown that using multiple representations for learning has many advantages. For computational thinking learning in young children, multiple representations,

(1) allow children to try out as many tools they can use as possible to solve a task (and later can be connected to switching over from one type of representation to other representation types)

(2) providing adaptations to the environment, materials, and guidance you offer to encourage children’s active participation and decision making in their own learning

(3) make CT easier to approach by offering foundational, real-life experiences

(4) help young children actively develop fine motor skills and cognitive skills

(5) have children freely explore solutions and ideas about tasks that typically do not require a single correct answer

(6) function as a great building block to enter into the world of computer science

Combining unplugged CT learning and the use of multiple representations – particularly for younger children - can engage children in CT skills (e.g., collaboration; understanding and learning abstract concepts/skills using concrete manipulatives; increased engagement).

One of the most common designs of unplugged CT learning activities is using toy robots and having children navigate the toy robot’s moving path. To successfully complete this task, children brainstorm ideas to solve a problem, manipulate the toy robot’s direction, think from the perspective of the toy robot, and think from the toy robot’s perspective. It can be challenging when children first try to figure out how to operate the toy robot, but what’s noticeable is that they become active learners in the scene. Furthermore, their attention span lasts longer; they actively engage in the problem; they don’t get frustrated easily from failing; they communicate and often collaborate with peers; and they find errors and fix them. Studies describe involving hands-on, real-world, concrete manipulatives and their connections to sensory systems and later learning (e.g., Carbonneau et al., 2013; Kwon & Capraro, 2021; National Research Council, 2001). Having children feel, touch, move, write, draw, see, and think with concrete tools certainly encourages them to engage in CT learning which can be abstract and helps them iteratively refine their designed solutions to a given problem. Another great thing is that such tasks typically do not require a single correct answer, thereby encouraging children to explore multiple solutions.

To create meaningful unplugged CT learning, prior studies (in collaboration with teachers) identified key strategies for considering meaningful and effective instructions, particularly for young children, including children with disabilities (e.g., Israel et al., 2015).

• Provide explicit instructions to children (e.g., step-by-step demonstrations for children to follow the guidance and build foundational skills).
• Prompt children to use multiple ways to communicate (e.g., gestures, sounds, colors, symbols, written texts, communication boards, etc.).
• Provide a variety of tangible, manipulable materials to play with, but be mindful of their size. If too small, children may experience difficult times operating/using them.
• Embed tasks into real-world situations.
• Encourage children to collaborate with peers.

So, what is an example CT task we can work on with young children? Here, I share my experience with a 5-year-old boy, Brandon (pseudonym). Brandon is a boy who is quiet but actively observing and learning everything happening around him, including verbal and written language and directions.

• Task: Create a story of the toy robot’s journey from a starting point to a destination.
• Materials: A grid mat, a toy robot, papers, colored pens, colored papers, play dough, foil, and other materials for crafting work
• What we learned:
  • Brandon was able to successfully generate a story of the toy robot’s journey (the robot had a home where it could stay safe, and the robot had to eat a donut which is Brandon’s favorite snack. Then, the robot had to come back home without getting caught by a green/pink scary slimy monster.)
  • Brandon successfully operated/manipulated the toy robot in the way that he wanted to operate it after several attempts using various representation methods.
  • He verbally talked to himself, used two right fingers to follow the toy robot’s paths; moved around and rotated the grid mat to see where the toy robot was facing; modeled the movements of the toy robot and followed it to see how many cells he needed to move the toy robot; and used different colored papers to discriminate directional arrow symbols to manipulate/code the robot.
  • His attention span lasted the whole time until he completed the task.
  • Peers collaborated (and helped each other) when Brandon was stuck figuring out one of the directional arrow codes he needed (symbol).
  • Brandon learned from and collaborated with his peers. By the end of the activity, he was able to generate his own story independently and found errors and fixed them correctly.
• Challenges Brandon experienced:
  • Perspective-taking (understanding and thinking from the perspective of the toy robot)
  • Directions (discriminating directions – left & right) – He experienced the most difficulty using the symbolic (written type – arrow codes) representations.
  • Reading/recognizing symbols that are abstract such as the directional arrows of the toy robot
  • Clicking the toy robot’s arrows as the robot was small for young children who are developing fine motor skills
• What I found helpful in supporting Brandon:
  • Letting him explore various ways such as trying out operating the robot’s directions
  • Moving around to fit his perspective to the robot’s perspective
  • Using both hands to put one hand on one cell where the robot stayed while moving the other hand following the robot’s path/direction
  • Having a peer as a collaborative learning partner to brainstorm ideas and explore solutions together

All these kinds of activities can be designed WITHOUT toy robots if there’s nothing available!

So, how would you design your activity to introduce computational thinking to your children who are eager to explore computational thinking? J  

References:

Carbonneau, K. K., Marley, S. M., & Selig, J. P. (2013). A meta-analysis of the efficacy of teaching mathematics with concrete manipulatives. Journal of Educational Psychology, 105(2), 380-400. https://doi.org/10.1037/a0031084

Israel, M., Wherfel, Q. M., Pearson, J., Shehab, S., & Tapia, T. (2015). Empowering K–12 students with disabilities to learn computational thinking and computer programming. TEACHING Exceptional Children, 48(1), 45-53. https://doi.org/10.1177/0040059915594790

Jeong, H. I., & Kim, Y. (2017). The acceptance of computer technology by teachers in early childhood education. Interactive Learning Environments, 25(4), 496-512. https://doi.org/10.1080/10494820.2016.1143376

Kwon, H., & Capraro, M. M. (2021). Nurturing Problem Posing in Young Children: Using Multiple Representation within Students’ Real-World Interest. International Electronic Journal of Mathematics Education, 16(3), 1-12. https://doi.org/10.29333/iejme/11066  

Magen-Nagar, N., & Firstater, E. (2019). The obstacles to ICT implementation in the kindergarten environment: Kindergarten teachers’ beliefs. Journal of Research in Childhood Education, 33(2), 165-179. https://doi.org/10.1080/02568543.2019.1577769

National Research Council. (2001). Adding it up: Helping children learn mathematics. Washington, DC: The National Academies Press. https://doi.org/10.17226/9822

Xie, K., Kim, M. K., Cheng, S.-L., & Luthy, N. C. (2017). Teacher professional development through digital content evaluation. Education Tech Research Dev 65(4), 1067–1103. https://doi.org/10.1007/s11423-017-9519-0

One of the first tasks we did when we started our work a year ago was to take a look at what kind of research evidence exists in STEM learning and young children with disabilities. We conducted an extensive review of the research – called a scoping review – to see what we could find.  We searched 102 different sources such as databases, direct searches of journals, reports, conference proceedings, master’s theses, presentation transcripts, films, and dissertations) with 20 search terms.  This yielded 1,407 unique references, which two-person teams independently reviewed for exclusion based on age and topic. We ended up with 486 unique references, which we categorized in several ways.

The scoping review found that the vast majority (92.6%) of these 486 references were related to children of preschool age (3-4 years old). Very few discussed infants (1.9%) or toddlers (1%). 

We also wanted to know if these 486 references covered young children with disabilities. We allowed the search to cover STEM learning in all early care arrangements (e.g., home, child care, preschool, Head Start, etc.) for all children with and without disabilities, ages birth to five years. Only 6% (n=29) of the references we found referred to children with disabilities.