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Cognitive flexibility and academic self-efficacy are the strongest predictors of students' programming computational thinking.
Researchers surveyed 426 university students to identify which factors influence their programming-oriented computational thinking skills. The study revealed that cognitive flexibility and academic self-efficacy strongly predict these skills, while demographic variables like gender and major have no significant direct effect. Additionally, extracurricular programming time and previous experience played vital roles in student performance.
Design programming instruction that builds cognitive flexibility by exposing learners to diverse, non-rigid problem-solving approaches. Foster academic self-efficacy through scaffolded tasks and early, low-stakes successes, rather than relying on demographic assumptions. Encourage and structure extracurricular coding practice to help students build practical experience.
AI image generators speed up design ideation but introduce gender stereotypes, highlighting the need for critical visual literacy.
Generative AI is rapidly reshaping higher education, forcing institutions to rethink teaching, learning, and institutional strategy.
Building digital textbooks using a crowdsourced model of professionals, students, and AI produces highly rated, deep learning resources.
Researchers have validated a reliable 30-item scale to measure how university instructors adopt and use AI in their teaching.
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