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1. Real-World Problems, Data, and Visualizations Using BRIDGES (PreConference Workshop)

   Kalpathi Subramanian, Erik Saule (January 2022, Journal of Computer Science in Colleges (Central Plains))

   The rise in CS enrollments in the past few years has also resulted in a more diverse population of learners that have different expectations, motivations and interests, making it important to provide relevant learning materials in early foundational courses. Grounding Computer Science concepts in reality by solving important real-world or fun problems are keys to increasing students’ motivation and engagement in computing, which may help improve student retention and success. This workshop provides instructors with a hands-on introduction to BRIDGES, a software infrastructure for programming assignments in early computer science courses, such as CS1, CS2, data structures, and algorithm analysis. BRIDGES provides the tools for creating engaging programming assignments, including: (1) a simplified API for accessing real-world data, such as those from social networks, entertainment (songs, movies), science, engineering (USGIS Earthquakes, elevation maps), geography (OpenStreet maps), and literature (Project Gutenberg), (2) creating visualizations of the data, (3) an easy to use API for game-based assignments, and, (4) algorithm benchmarking. Workshop attendees will engage in hands-on experience using BRIDGES with multiple datasets, have the opportunity to discuss the challenges they face in their own courses, and how BRIDGES can be used in their own courses. Using BRIDGES in data structures, algorithms, and other courses have shown improved retention of CS knowledge and better student performance in follow-on courses, when compared to students from other sections of the same course. BRIDGES has impacted nearly 2000 students across 20 institutions since its inception 5 years ago. A repository of BRIDGES assignments is now maintained for instructors using BRIDGES in their classes. 

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2. A Framework that Explores the Cognitive Load of CS1 Assignments Using Pausing Behavior

   https://doi.org/10.1145/3626252.3630760  

   Urry, Joshua; Edwards, John (March 2024, ACM)

   Pausing behavior in introductory Computer Science (CS1) courses has been related to course outcomes and could be linked to a student’s cognitive load. Using Cognitive Load Theory and Vygotsky’s Zone of Proximal Development as a theoretical framework, this study empirically analyzes keystroke latencies, or pause times between keystrokes, with the goal of better understanding what types of assignments need more scaffolding than others. We report the characteristics of eleven assignments, introduce a method to analyze pausing behavior, and investigate how pausing behavior changes with assignment characteristics (e.g., introducing new programming constructs, engaging creativity through Turtle graphics, etc). We find evidence that pausing behavior does change based on the assignment characteristics and that assignments with particular characteristics, such as object-oriented principles, may be more likely to have excessive demands on student working memory. We also find evidence that assignment completion time may not be an accurate measure of assignment difficulty. 

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3. Location Based Assignments in Early CS Courses Using BRIDGES Engages Students

   Mcquaigue Matthew; Strahler, Jay; Subramanian, Kalpathi; Saule, Erik; Payton, Jamie (January 2023, Journal of computing sciences in colleges)

   Freshmen and sophomore level courses in computer science are critical to long-term student development and success. At the same time, these courses, such as data structures and algorithms are usually challenging and require significant motivation to keep students engaged. In this work, we present through our BRIDGES system a set of location based assignments that can serve to reinforce core concepts and algorithms by placing them in more meaningful settings and applications, and demonstrate the relevance of computing in the early stages of a student's career. We performed a small pilot study using a subset of these assignments in a special topics course on algorithms, and conducted student surveys after each assignment. The surveys were unanimously positive, and the students enjoyed coding the algorithms as well as the datasets and visualizations associated with the assignments. 

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4. Self-Efficacy Study in Computing Among College Freshmen

   Ghimire, Amrita; Lee, S.; Lineberry, L. (July 2020, ASEE Annual Conference proceedings)

   Computer Science (CS) is not introduced equitably across K-12 schools, yet it is increasingly a necessary skill regardless of vocational pathway. Co-curricular activities such as summer camps have become a popular way to introduce CS to K-12 students. Researchers at our institution, through partnerships with other educational institutions and practitioners, developed a transdisciplinary approach of teaching CS in K-12 informal learning environments. Building on positive results in the K-12 informal learning environment, researchers are exploring the applicability of the transdisciplinary modules in formal instruction for early college learners in CS0 and CS1 courses. This paper explores self-efficacy data collected from multiple CS0 and CS1 courses. Learners include freshmen in computing majors and in non-computing majors. We compare their self efficacy growth in computing across race and gender, considering their formal or informal CS education experiences prior to entering college. This work is a part of a larger effort to redesign CS0 and CS1 courses to introduce more complex concepts and important design concepts such as parallel and distributed computing earlier in the curriculum. The authors’ longer-term goal is to investigate active learning strategies that will introduce higher level computer science topics early in the curriculum to enable students to recognize content applicability earlier in their college pathway. 

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5. CS1 Student Assessments of Themselves Relative to Others: The Role of Self-Critical Bias and Gender

   https://doi.org/10.22318/icls2021.597  

   Gorson, Jamie; O'Rourke, Eleanor (June 2021, ISLS Annual Meeting 2021)

   University introductory computer science courses (CS1) present many challenges. Students enter CS1 with varying backgrounds and many are evaluating their potential for success in the major. Students often negatively self-assess in response to natural programming moments, such as getting a syntax error, but we have a limited understanding of the mechanisms that drive these self-assessments. In this paper, we study the differences in student assessments of themselves and their assessments of others in response to particular programming moments. We analyze survey data from 214 CS1 students, finding that many have a self-critical bias, evaluating themselves more harshly than others. We also found that women have a stronger self-critical bias, and that students tend to be more self-critical when the other is female. These insights can help us reduce the impact of negative self-assessments on student experiences. 

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