An Exploration of the Role of Affective Factors on Computational Thinking and Problem Solving

• In Event: Computational Thinking and Computational Modeling in Higher Education and K–8 Contexts

Abstract

This study explored the structural relationships between learner affective factors, computational thinking and problem-solving in the context of online higher education. In addition, the effects of CT components such as decomposition, pattern recognition, planning, algorithm, reusing, and debugging on problem-solving self-efficacy were also evaluated. An online survey was conducted with a total of 69 undergraduate students. Structural equation modeling was utilized to examine direct relationships among study variables. The results revealed that programming self-efficacy and computer science usefulness significantly predicted CT self-efficacy, and algorithm and debugging were positively significant on problem-solving self-efficacy. The findings contribute to a validation of CT affective factors predicting CT and problem solving and suggest what activities should be emphasized in education curriculum to promote problem-solving skills.

Authors

• Henry (Hyunchang) Moon, Texas Tech University

• Jongpil Cheon, Texas Tech University

• Kyungbin Kwon, Indiana University - Bloomington