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For the last 10 years, our university has offered a two-semester bridge into a master's in computer science for people with undergraduate degrees in non-computing disciplines. Since its inception, the program has expanded to eight campuses across North America and has opened admission to students from all disciplines, including non-STEM disciplines. The bridge program has over 2000 currently enrolled students, with more than 50% women every year since 2020, and domestic enrollment has increased relative to direct entry master's students. Our data show that bridge students, including those with non-STEM backgrounds, perform comparably to direct entry students in terms of GPA. We attribute much of the program's success to institutional investment in resources specifically designed to meet the unique needs of bridge students. These resources include dedicated academic and career advising, co-curricular programming, and the hiring of full-time teaching faculty specifically recruited to teach these bridge students. This paper examines data pertaining to the bridge program and MSCS from 2013 to 2023; it includes analyses of the expansion of the bridge program to eight campuses in North America, the admission of students with non-STEM degrees to the bridge, the achievement of enrolling over 50% women and non-binary identifying students, the success of bridge students in the MSCS program and in obtaining job placements, and domestic student enrollment growth as compared to traditional direct entry master's students. 

Students in entry level CS courses come from diverse backgrounds and are learning study and time management skills. Our belief for their success is that they must master a growth mindset and that the final grade should represent their final mastery of topics in the course. Traditional grading systems tend to be too restrictive and hinder a growth mindset. They require strict deadlines that fail to easily account for student accommodations and learning differences. Furthermore, they run into averaging and scaling issues with 59% of a score counting as failing, making it difficult for students to redeem grades even if they later demonstrate mastery of topics. We designed a formative/summative grading system in our CS0 and CS1 classes for both on-campus and online students to support a structured growth mindset. Students can redo formative assignments and are provided flexible deadlines. They demonstrate their mastery in summative assignments. While being inspired by other grading systems, our system works seamlessly with auto-grading tools used in large, structured courses. Despite the flexibility, the courses provided a level of rigor before allowing students to continue onto the next course. Overall, 65% of students resubmitted assignments increasing their scores, participated in ungraded assignments, and used formative assignments for additional practice without a distinction between race or gender. These students went to the traditional follow-on CS2 course and 94% passed compared with 71% who took CS1 with a traditional grading system. 

Intervention in the form of changing one's teaching style is beneficial for boosting student grades and retention. However, in spite of the availability of multiple intervention approaches, a key hindrance is reliance on the belief that students know how to study. We dedicated time and resources to not only teach the discipline of Computer Science, but also to teach students how to study using techniques grounded in psychology. We offered a one-credit "booster" course to students taking CS 2: Data Structures. Through direct advisor intervention based on the first exam grade, students were encouraged to take the booster course along with traditional interventions. We then tracked student growth across exams for the course as students were learning and being held accountable to study techniques not often emphasized in Computer Science. The students continued to increase their grades throughout the semester relative to the students who chose to not take the booster class. The students who were targeted for intervention but did not take the booster course continued to have lower grades throughout the semester, and only 41% of them passed the course. Students who participated in the booster course showed a 31% rate of growth across the semester, taking a failing grade to a passing grade, with 100% passing the course with a C or above. These results show a significant influence to help students succeed, which led to higher retention and increased grades. If we want students to truly succeed, we must teach them to study. 

Computer Science (CS 1) offerings in most universities tend to be notoriously difficult. Over the past 60 years about a third of the students either fail or drop out of the course. Past research has focused on improving teaching methods through small changes without changing the overall course structure. The premise of our research is that restructuring the CS 1 course using a Spiral pedagogy based on principles for improving memory and recall can help students learn the information and retain it for future courses. Using the principles of Spacing, Interleaving, Elaboration, Practiced Retrieval, and Reflection, we fundamentally redesigned CS 1 with a complete reordering of topics. We evaluated the newly designed CS 1 by comparing the students with those coming from a traditional offering in terms of (1) CS 1 performance, (2) retention of students between CS 1 and 2, and (3) CS 2 performance. We demonstrate that the Spiral method helped students outperform those who learn via the traditional method by 9% on final exam scores in CS 1. Retention is increased between CS 1 and CS 2 with a 19.2% increase for women, and 9.2% overall. Furthermore, students continue to do better in CS 2 with increased grades across all assessments and show a 15% increase in passing grades. We provide a framework for the Spiral methodology so that others may replicate the design. Our results lead us to consider evaluating and improving the underlying methodology with which we teach Computer Science.