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Early computer science courses (CS1, CS2) are the cornerstone of student understanding of computer science. These courses introduce the foundational knowledge of computer science needed to understand more complex topics and to be successful in follow-on courses. It is thus important to introduce CS concepts in an engaging and easy-to-understand manner to increase student interest and retention. This paper presents a new approach to teaching the Computer Science 1 (CS1) course through our BRIDGES system. This approach aims to increase student engagement and improve learning outcomes by using audio-based assignments that they can manipulate and process audio signal information, as well as visualize and play them. We explain how to design and implement audiobased assignments and connect them to fundamental programming constructs such as variables, control flow, and simple data structures, such as arrays. These assignments encourage and engage students by using audio data they are interested in to write code, promoting problem-solving and improvements in their critical thinking skills. 

BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data collected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addition of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared student cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to students enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today’s computational challenges. 

BRIDGES is a software framework for creating engaging assignments for required courses such as data structures and algorithms. It provides students with a simplified API that populates their own data structure implementations with live and real-world data, and provides the ability for students to easily visualize the data structures they create as part of routine classroom exercises. The objective is to use the infrastructure to promote a better understanding of the data structure and its underlying algorithms. This report describes the BRIDGES infrastructure and provides evaluation data col- lected over the first five years of the project. In the first 2 years, as we were developing the BRIDGES projects, our focus was on gathering data to assess whether the addi- tion of the BRIDGES exercises had an effect on student retention of core concepts in data structures; and throughout the 5-year duration of the project, student interest and faculty feedback were collected online and anonymously. A mixed method design was used to evaluate the project impact. A quasiexperimental design compared stu- dent cohorts who were enrolled in comparable course sections that used BRIDGES with those that did not. Qualitative and quantitative measures were developed and used together with course grades and grade point averages. Interest and relevance in BRIDGES programming assignments was assessed with additional survey data from students and instructors. Results showed that students involved in BRIDGES projects demonstrated larger gains in knowledge of data structures compared to stu- dents enrolled in comparable course sections, as well as long-term benefits in their performance in four follow-on required courses. Survey responses indicated that some investment of time was needed to use BRIDGES, but the extra efforts were associated with several notable outcomes. Students and instructors had positive perceptions of the value of engaging in BRIDGES projects. BRIDGES can become a tool to get students more engaged in critical foundational courses, demonstrating relevance and context to today’s computational challenges. 

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.