Cazalas, JonathanBarlow, MaxCazalas, IbraheemRobinson, Chase2022-07-012022-07-012022-03Cazalas, J., Barlow, M., Cazalas, I., & Robinson, C. (2022, March 3-5). MOCSIDE: An open-source and scalable online IDE and auto-grader for computer science education [Conference poster]. 53rd Annual ACM Technical Symposium on Computer Science Education (SIGCSE 2022), Virtual Conference. https://www.doi.org/10.1145/3478432.3499125http://hdl.handle.net/11416/664https://www.doi.org/10.1145/3478432.3499125Programming is learned through practice, with said practice in introductory programming courses often translating to a prohibitively large number of assignments, increasing the grading workload for faculty and/or teaching assistants. In short, this is unsustainable. Several publishers and a few notable companies have provided meritable auto-grading solutions, although most are plagued with problems including minimal problem sets, limited customization options, high cost, and at times even a disconnect with the pedagogical needs of academia. This poster presents our newly-developed web application, MOCSIDE, an open-source and scalable online IDE and auto-grader for computer science education. Results indicate a positive user experience from students and instructors alike, with cost savings, ease of use, and code collaboration highlighted as key features.enComputer science--Study and teachingWeb applicationsAuto-gradingOtherhttps://www.doi.org/10.1145/3478432.3499125