The operation of cache memory is a multi-step processes which is often challenging for students to grasp. This project presents a new educational tool built with modern web tooling (React, TypeScript, and TailwindCSS) with the goal of demonstrating the operation of cache accees in a step-by-step manner.

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Student feedback plays a crucial role in course pedagogy and creating inclusive learning environments. Student evaluations of teaching and mid-term feedback surveys continue to be the de facto standard for collecting numerical and written comments. Courses with large enrollments, particularly introductory courses, are often left in the dark about true student perceptions and feedback due to the sheer volume of responses. Additionally, instructors may gain a false sense of the population based on the “loud” feedback within the written comments or the opinions of the small subset of students.

The goal of this project is to develop tools to assist faculty in deciphering the overall themes and sentiment from the student feedback in order to improve their teaching.

One of the challenges of teaching large systems courses is managing submission and grading of course programming assignments. Since 2015, I have been working with a set of undergraduate and graduate student to develop autograding tools to reduce grading overhead, enable equitable grading practices, and provide continual student feedback during development. The foundation of the project utilizes unit-testing to enable both accurate, as well as incremental/partial grading. The goal is to instill good practices and to encourage students to think about edge cases when testing, as well as provide positive feedback as they progress through the assignment.

Past iterations this project include “git-submit,” which creates and manages a private submit branch within the students’ git repositories. A python-based grading framework facilitated container-based testing of each unit test on a student assignment. The framework provided an interface between different types of unit testing environments and gathered all grading information to build a single uniform gradesheet with feedback. We utilized git continuous integration to ensure student submissions compile and execute minimal testcases alerting students immediately to any concerns with their submission.

The current version is a container-based multi-threaded autograder with a variety of unit test options, including validation of multiple program output streams and system state. In the case of test failure students are presented with both the expected and obtained output in one of many available formats (eg. byte-by-byte, character strings, array values, etc). With each term, improvements and features are added to the systems to increase functionality, flexibility, and accuracy. We incorporate student feedback into the tools to improve usability and user experience.

As part of the continued improvements to Stony Brook’s SmartTransit system, a pair of CSE Honors students are using machine learning techniques to automatically detect in real-time route assignments for Stony Brook’s buses. The goal of the project was to use both historical and real-time data to enable a real-time route detection/assignment tool and integrate this tool into the current production Smart Transit Management system. A real-time notification system would provide transit managers with discrepancy between the route taken via the vehicle and the assigned route. Additionally, this system will be used to further refine our departure/arrival time calculations which are presented directly to the user.

SBU Smart Transit was the Stony Brook University student-developed passenger information system for SBU Transit. SBU Smart Transit was developed through a partnership between the Stony Brook University Department of Transportation and Parking Operations, the Center of Excellence in Wireless and Information Technology (CEWIT) and the College of Engineering and Applied Sciences and was a comprehensive bus and shuttle global positioning system that provides live vehicle location, bus stop information and estimated arrival times to passengers using web and mobile-based applications.

Hardware and software for the project was 100% built and maintained by Stony Brook students through coursework and summer internships. Starting in 2011, the project provided approximately 50 students the opportunity to work on a full-stack development project. Configured embedded devices and third-party hardware/software integration was used to provide real-time tracking of approximately 50 campus vehicles. A developed web management system allows Transportation and Facilities Services on campus to managed the visibility of the fleet in real-time.