577
emphasizes the importance of problem-solving, abstraction, and algorithmic thinking in CT
education. The CT4E framework is based on the following five components: problem
decomposition, pattern recognition, abstraction, algorithms, and evaluation. By focusing on
these components, educators can help students develop their CT skills in a structured and
organized manner[1].
Another framework for teaching CT is the Computational Thinking Education
Framework (CTEF), which emphasizes the importance of CT education across different
domains and disciplines. The CTEF is based on the following five components: CT concepts
and skills, CT teaching and learning, CT assessment, CT curriculum, and CT teacher
education. By focusing on these components, educators can ensure that CT education is
integrated into the broader curriculum and that students are exposed to CT concepts and
skills in various domains[3].
Analysis and results.
As mentioned in the related research, there are several effective
pedagogical strategies for teaching computational thinking (CT) to students in higher
education institutions. These include scaffolding, collaborative learning, and project-based
learning.
Scaffolding involves breaking down complex problems into smaller, more manageable
parts and providing students with step-by-step instructions to help them develop their CT
skills. This strategy is effective because it provides students with the necessary support
and guidance to help them learn at their own pace. Collaborative learning, on the other
hand, allows students to work together and learn from each other. This strategy is effective
because it allows students to develop their problem-solving and communication skills, as
well as their ability to work in teams.
Project-based learning is also an effective strategy for teaching CT, as it allows students
to apply their CT skills to real-world problems. This strategy involves providing students
with a project that requires them to use their CT skills to solve a problem or develop a
solution. By engaging in project-based learning, students can develop their CT skills in a
meaningful and engaging way, as well as develop their ability to work independently and
manage complex projects.
In terms of the theoretical foundations of teaching CT, there are several models and
frameworks that can be used. For example, the Computational Thinking Framework by
Wing (2006) outlines the key components of CT, including algorithms, data structures, and
abstraction. This framework can be used to design CT curricula and to ensure that students
are exposed to CT concepts and skills in various domains[4].
The related research also highlights some challenges to teaching CT, such as the need for
faculty development and the lack of standardized assessments for CT skills. To overcome
these challenges, higher education institutions may need to invest in faculty development
programs that provide educators with the necessary knowledge and skills to teach CT
effectively. Institutions may also need to develop standardized assessments for CT skills to
ensure that students are acquiring the necessary skills and knowledge.
The analysis and results suggest that teaching CT to students in higher education
institutions requires a sound theoretical foundation and effective pedagogical strategies. By
incorporating scaffolding, collaborative learning, and project-based learning into CT
instruction, educators can help students develop their CT skills in a structured and
engaging manner, preparing them for success in the digital age. However, there are also
challenges to teaching CT that must be addressed to ensure the effective integration of CT
education into the higher education curriculum.
