Computational thinking skills are growing in importance in information processing tasks, especially for college students. By applying a four-step model of problem solving, students learn how to solve problems with computational support.
Computational thinking is now an essential literacy that combines four pillars—problem decomposition, pattern recognition, abstraction, and algorithms. This is a way to find solutions to automating a process. Computational thinking is the foundation for careers in programming, data science and machine learning. Definitions of each stage of the process include:
- Decomposition: breaking the problem into multiple parts
- Pattern recognition: looking for similarities and trends
- Abstraction: putting aside what is unnecessary and focusing on what’s important
- Algorithm design: creating a computer artifact with step-by-step instructions to solve a problem
Benefits and challenges
The benefits of computational thinking include transferable skills that are a springboard for digital literacy and a deepening of content learning. Integrating computational thinking into the curriculum advances the goal of developing computer science skills for every student.
One of the challenges to implementing this logical process is that most professional development for computational thinking siloes the computational thinking skills. Also, without an integration with problem solving skills, there is little skills transfer.
Margulieux believes the value for higher education is that it is an opportunity to incorporate into teacher training programs to build their professional skills as another tool in their tool kit, much like their ability to integrate technology across the curriculum. Also, it’s an opportunity for faculty to automate some of the processes they don’t like to do, such as creating algorithms to grade scientific projects or constructing digital media for instruction purposes. But primarily it is an opportunity to facilitate new learning skills for both faculty and students by creating an interactive framework for learning.
“Learning a new language is daunting,” said Margulieux. “It requires a paradigm shift in thinking and sometimes the program activities don’t really embody computational thinking.” Margulieux also believes that it can be expensive to implement particularly in rural areas where computer infrastructure is less available. To advance the usage of the computational thinking problem solving practice, Margulieux works with the Georgia State teacher preparation program to “design integration activities within disciplines and grade bands to prepare our preservice teachers to use the activities in their future classes.”