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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.