Abstract:

Computational thinking (CT) has emerged as a fundamental competency in higher education, particularly within computer science, engineering, and technology-related programs, due to its relevance for problem solving, innovation, and participation in digital environments. However, many educational approaches still emphasize technical aspects—such as programming and algorithmic reasoning—while overlooking the attitudinal and socio-emotional factors that influence how students engage with computational processes. Beyond technical proficiency, CT development is shaped by dispositions that determine how learners approach challenges, sustain effort, and construct knowledge.

This study examines the relationship between attitudinal traits and computational thinking competencies during the early stages of university education. It is based on the premise that dispositions such as curiosity, persistence, attention to detail, critical thinking, creativity, collaboration, adaptability, and autonomy act as key enablers of CT development. These attitudes support the acquisition of competencies structured around the five core pillars of computational thinking: decomposition, pattern recognition, abstraction, algorithm design, and evaluation.

A mixed-methods, descriptive–correlational design was adopted. The study was conducted at the Technological University of Panama with a non-probabilistic sample of 234 first-year students enrolled in technology-oriented programs. Data collection combined a Likert-scale instrument to assess attitudinal dimensions with diagnostic tasks inspired by recognized computational thinking frameworks. Pearson correlation coefficients were calculated (p < 0.05) to identify relationships between variables.

The findings indicate that students are at an early stage of CT development, showing higher performance in decomposition and algorithmic thinking, and lower levels in abstraction and pattern recognition. Attitudinal traits such as adaptability, autonomy, and creativity showed relatively high prevalence. Correlation analysis revealed statistically significant relationships ranging from moderate to strong, particularly between persistence and algorithmic thinking and between creativity and algorithm design. Additional moderate associations highlight the role of attention to detail, autonomy, and critical thinking in evaluation and abstraction processes.

These results suggest that computational thinking should be understood as an integrated construct in which cognitive and attitudinal dimensions interact dynamically. Consequently, the study advocates for a holistic and human-centered approach to CT education, emphasizing the integration of socio-emotional and attitudinal competencies within curricular design to enhance learning outcomes and support the sustainable development of computational skills in higher education.

Keywords:

Computational Thinking, Attitudinal Competencies, Higher Education, Computer Science Education, Mixed Methods, Educational Innovation.