More Like this

1. Adversarial Thinking: Innovating Contemporary Problem-Solving Skills in Computing

   Servin, Christian; Childress, JJ; Karichev, Nadia (April 2025, Journal of computing sciences in colleges)

   Attarwala, Abbas (Ed.)

   Adversarial Thinking (AT) is essential in cybersecurity and computing, promoting strategic thinking and problem-solving by anticipating worst-case scenarios. However, embedding AT in early computing education, especially during the first two years of college, remains underexplored. Introductory programming courses, such as CS 0, lay the groundwork for computational thinking and advanced studies while preparing students for courses like CS 1 and CS 2. Implementing such curricula in resource-limited community colleges, which serve diverse students aiming for workforce entry or transfer to four-year programs, poses unique challenges. Enhancing AT skills among these students provides a competitive edge in the job market and a strong foundation for further studies. This paper explores the integration of AT into CS 0 courses at community colleges, identifying key characteristics and fostering a sense of belonging critical for AT development. The findings offer actionable insights for educators to better prepare students for computing careers and address broader cybersecurity demands. 

   more » « less
2. Promoting Computational Thinking in Integrated Engineering Design and Physics Labs

   Lopez-Parra, R. D.; Subramaniam, R. C.; Morphew, J. W. (January 2023, ASEE Annual Conference & Exposition Proceedings)

   Computational thinking has widely been recognized as a crucial skill for engineers engaged in problem-solving. Multidisciplinary learning environments such as integrated STEM courses are powerful spaces where computational thinking skills can be cultivated. However, it is not clear the best ways to integrate computational thinking instruction or how students develop computational thinking in those spaces. Thus, we wonder: To what extent does engaging students in integrated engineering design and physics labs impact their development of computational thinking? We have incorporated engineering design within a traditional introductory calculus-based physics lab to promote students’ conceptual understanding of physics while fostering scientific inquiry, mathematical modeling, engineering design, and computational thinking. Using a generic qualitative research approach, we explored the development of computational thinking for six teams when completing an engineering design challenge to propose an algorithm to remotely control an autonomous guided vehicle throughout a warehouse. Across five consecutive lab sessions, teams represented their algorithms using a flowchart, completing four iterations of their initial flowchart. 24 flowcharts were open coded for evidence of four computational thinking facets: decomposition, abstraction, algorithms, and debugging. Our results suggest that students’ initial flowcharts focused on decomposing the problem and abstracting aspects that teams initially found to be more relevant. After each iteration, teams refined their flowcharts using pattern recognition, algorithm design, efficiency, and debugging. The teams would benefit from having more feedback about their understanding of the problem, the relevant physics concepts, and the logic and efficiency of the flowcharts 

   more » « less
3. Toward a Block-Based Programming Approach to Interactive Storytelling for Upper Elementary Students

   https://doi.org/10.1007/978-3-030-62516-0_10  

   Smith, Andy; Mott, Bradford; Taylor, Sandra; Hubbard-Cheuoua, Aleata; Minogue, James; Oliver, Kevin; Ringstaff, Cathy (October 2020, International Conference on Interactive Digital Storytelling)

   null (Ed.)

   Developing narrative and computational thinking skills is crucial for K-12 student learning. A growing number of K-12 teachers are utilizing digital storytelling, where students create short narratives around a topic, as a means of creating motivating problem-solving activities for a variety of domains, including history and science. At the same time, there is increasing awareness of the need to engage K-12 students in computational thinking, including elementary school students. Given the challenges that the syntax of text-based programming languages poses for even novice university-level learners, block-based programming languages have emerged as an effective tool for introducing computational thinking to elementary-level students. Leveraging the unique affordances of narrative and computational thinking offers significant potential for student learning; however, integrating them presents significant challenges. In this paper, we describe initial work toward solving this problem by introducing an approach to block-based programming for interactive storytelling to engage upper elementary students (ages 9 to 11) in computational thinking and narrative skill development. Leveraging design principles and best practices from prior research on elementary-grade block-based programming and digital storytelling, we propose a set of custom blocks enabling learners to create interactive narratives. We describe both the process used to derive the custom blocks, including their alignment with elements of interactive narrative and with specific computational thinking curricular goals, as well as lessons learned from students interacting with a prototype learning environment utilizing the block-based programming approach. 

   more » « less
4. An Engineering Computational Thinking Diagnostic: A Psychometric Analysis

   https://doi.org/10.1109/FIE49875.2021.9637142  

   Diaz, Noemi V.; Trytten, Deborah A.; Meier, Russ; Yoon, So Yoon (October 2021, 2021 IEEE Frontiers in Education Conference (FIE))

   This research-track work-in-progress paper contributes to engineering education by documenting progress in developing a new standard Engineering Computational Thinking Diagnostic to measure engineering student success in five factors of computational thinking. Over the past year, results from an initial validation attempt were used to refine diagnostic questions. A second statistical validation attempt was then completed in Spring 2021 with 191 student participants at three universities. Statistics show that all diagnostic questions had statistically significant factor loadings onto one general computational thinking factor that incorporates the five original factors of (a) Abstraction, (b) Algorithmic Thinking, (c) Decomposition, (d) Data Representation and Organization, and (e) Impact of Computing. This result was unexpected as our goal was a diagnostic that could discriminate among the five factors. A small population size caused by the virtual delivery of courses during the COVID-19 pandemic may be the explanation and a third round of validation in Fall 2021 is expected to result in a larger population given the return to face-to-face instruction. When statistical validation is completed, the diagnostic will help institutions identify students with strong entry level skills in computational thinking as well as students that require academic support. The diagnostic will inform curriculum design by demonstrating which factors are more accessible to engineering students and which factors need more time and focus in the classroom. The long-term impact of a successfully validated computational thinking diagnostic will be introductory engineering courses that better serve engineering students coming from many backgrounds. This can increase student self- efficacy, improve student retention, and improve student enculturation into the engineering profession. Currently, the diagnostic identifies general computational thinking skill 

   more » « less
5. Infusing Computational Thinking into a Computer Science Gateway Course

   Benkarroum, Younes; Azhar, Mohammad Q (April 2024, Journal of computing sciences in colleges)

   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 

   more » « less