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Computational thinking (CT) stands as a universal problem-solving approach applicable across diverse disciplines, transcending the domain of computer science. It embodies the mental process of structuring a problem to enable a computational solution feasible for both humans and machines. This methodology involves dissecting problems into smaller parts that are easier to understand and solve. This study delineates a meticulously designed series of CT activities within an introductory computer science course and explores their profound impact on student engagement and problem-solving proficiency. Our findings underscore the pivotal role of hands-on CT practice in augmenting students' ability to decompose problems, recognize patterns, and abstract complexities, and employ algorithms effectively. Notably, this infusion of CT not only cultivates theoretical understanding but also bridges the gap between conceptual knowledge and real-world application through the use of computational tools like Python programming. As CT continues to emerge as a cornerstone skill in diverse domains, this research presents compelling evidence advocating for its integration into introductory courses, laying a robust foundation for students to navigate the evolving technological landscape with enhanced problem-solving capabilities 

This paper shares the analysis of our quantitative findings regarding the impact of a virtual informal collaborative experiential learning activity on diverse students' computational thinking, critical thinking, and self-efficacy in STEM activities. Designed as part of an ongoing National Science Foundation sponsored project to provide underrepresented minority (URM) students from underserved economic backgrounds with real-world career preparation and technical education across disciplines through collaborative project activities using cutting-edge technologies, the Hackathon for Social Good was implemented during the COVID-19 shutdowns in a New York City community college in lower Manhattan. Students worked in teams to innovate practical solutions to global problems with mentor support from both academia and the tech industry. This intervention drew 36 students from Computer Science, Business, and Sociology classes, who worked with volunteers and alumni during a full-day event in the Fall of 2021, using AI and data science to design culturally sensitive data-driven solutions for real-world problems. The tracks covered the following topics: Zero Hunger, Clean Water, and Sanitation, Green Consumption, Racial Justice, Quality Education, Good Health, and Well Being. The two main objectives of this project are as follows: (1) Design a remote interdisciplinary one-day experiential collaborative learning environment to engage URM teams of students from a community college in applying computational thinking to develop solutions for social good. (2) Conduct research on our intervention to study its effect on students' self-efficacy, as well as their knowledge of, and comfort with, computational thinking, critical thinking, problem-solving, and STEM. The evidence gathered from qualitative and quantitative data indicates that using these mechanisms to infuse CT into student learning across disciplines has several positive outcomes. Students reported increased leadership skills, comfort with teamwork, problem-solving, and critical thinking. A quantitative study specifically showed a positive impact on student confidence in their ability to do CT and improved their sense of efficacy in impacting the world outside of the hackathon.