Computational Thinking in (non)formal Education

BRIDGE is designed to support teachers in guiding learners through our digital world. We believe that schools play a vital role in helping students not only use technology but also understand its broader impact. By combining societal dialogue, student activities, and teacher professionalization, we aim to equip schools with the tools they need to prepare young people for a thoughtful and responsible digital future. BRIDGE is built on three pillars to strengthen digital literacy, critical thinking, and teacher confidence in the classroom:

Understanding the Impact of Technology on Society using dialogue sessions

BRIDGE invites teachers and students to explore essential questions about the role of technology in our daily lives. These conversations help students reflect on the social, cultural, and ethical dimensions of technology. Skills that are just as important as technical knowledge.

Professional Development for educators

BRIDGE supports teachers professional-learning activities that strengthen foundational knowledge of computational thinking, offer concrete strategies for classroom implementation. Our training sessions are designed to be accessible, collaborative, and immediately applicable in everyday teaching.

Classroom Activities for Students (Ages 8–14)

BRIDGE offers different hands-on activities that introduce computational thinking in an engaging and age-appropriate way. All activities require little preparation and can be integrated into various subjects, from language arts to science. Whether your students are new to computational thinking or ready for more advanced challenges, our materials support meaningful learning at every level.

The foundations of Computational Thinking

Computational Thinking is a set of skills that helps to solve problems by proposing step-by-step solutions that can be carried out by a person or a computer. Computational Thinking is not thinking like a computer but the opposite, being able to tell a computer what to do to solve a problem.

The foundations of computational thinking are:

Decomposition: Dividing a complex problem or system into smaller parts that are easier to understand. We can divide a complex and/or big task or data (e.g. modelling a student) into simpler and smaller tasks or data components (e.g. personal data and data related to his/her courses). This allows us to work in parallel, define the tasks in more detail, check the partial results of these small tasks, etc.

Pattern recognition: Finding similarities between problems or systems that help to use previous solutions. Identifying patterns in information allows us to process it more efficiently (i.e. data of students" courses have in common the name of the course, the number of hours, the name of the teacher and the grade).

Abstraction: Separating the fundamental from the accessory aspects to ignore irrelevant details to the solution of the problem or the understanding of the system. Following the example of the student, we focus on the common characteristics of all students instead, the ones that define the student category, instead of focusing on individual characteristics.

Algorithms: Developing a step-by-step solution to the problem that usually involves sequences, loops and alternatives. Algorithms are the recipe to be followed to obtain the desired final result. A typical example of an algorithm is a cooking recipe, a set of steps to be followed systematically manipulating the ingredients.