Grading exams is a time-consuming activity for teachers. Zesje is an open-source tool created to aid teach-ers in exam grading and streamline the grading process. Zesje currently uses computer vision techniques torealign images, and automatically find student numbers. However, teachers can currently only use Zesje tograde questions manually. Moreover the computer vision capabilities of Zesje can be improved. To make iteasier to grade exams, it should be possible for teachers to have multiple choice questions graded automati-cally. This project describes various improvements for Zesje, most notably using computer vision for the auto-matic grading of multiple choice questions, improving the accuracy of aligning scanned submissions, andautomatically detecting blank solutions. The team had to make several choices regarding implementations and choice of technology. Design goalswere also created to serve as a guideline for the project. At the end of the project, with the features imple-mented by the team, Zesje can automatically grade multiple choice questions, identify blank solutions andhas the corresponding front-end changes that allow the user to create multiple choice checkboxes on theexam PDF. These features have been tested extensively. The use of Zesje also poses some ethical challenges. Using automated grading may result in the event thatsome submissions may never be seen by a grader. By using benchmarks to compare the performance of processing scans in Zesje, the team found out thatthe grading time has greatly been reduced.

The majority of the courses in the Computer Science Bachelor at the Delft University of Technology use so called lab sessions to provide an opportunity for students to ask questions about course material and get feedback on their assignment. In order to optimally support the students, teaching assistants, or TAs, are appointed to assist the lecturer during the lab sessions. With the number of students in the Bachelor quickly growing, the process of manually recruiting students to become a TA and assigning the TAs to lab sessions is becoming infeasible. This project aims to ease the process of gathering and scheduling TAs. In order to achieve this goal, the Teaching Assistant Management platform, or TAM, has been developed. All parties involved in the process of appointing TAs can use TAM to provide their input. Lecturers can register their courses on TAM, students are able to indicate their interest to help with different courses and representatives from Education and Student Affairs can validate the application of the interested students. Using this input, TAM is able to automatically create a schedule by assigning TAs to lab sessions. To provide an algorithm for the automatic generation of schedules, a model based on the minimumcost max flow problem is created. Due to complications with the implementation of the minimum-cost max flow model, the schedule generation is handled by a linear solver: Gurobi. By modeling the constrains for a schedule to be considered valid, Gurobi is used to process the input of the users into an optimized schedule. TAM consists of three components: a MySQL database, a backend written using Spring, containing the business logic and the implementation of the scheduler, and a frontend website created with Vue to provide an interface to the users. The frontend and the backend are connected using a REST API. A unique aspect of the project is the live deployment of TAM. At the end of the fourth week, the first version was deployed, allowing interested students to submit their course preferences. Subsequent features have been deployed iteratively. During the development, muliple problems have been encountered. The team underestimated the time required to learn the new technologies, as well as the time needed to maintain a system in production. Furthermore, configuring Single Sign-On required more time than expected.

Most courses in the Computer Science Bachelor at the Delft University of Technology make use of lab sessions. During these lab sessions students can ask questions about course material and get feedback on their assignment. Moreover, their knowledge about assignments can be orally tested. In order to properly help the students, teaching assistants, or TAs, are selected to assist the lecturer during the lab sessions. With the number of students in the Bachelor quickly growing, the process of manually recruiting students to become a TA and assigning the TAs to lab sessions is becoming very time consuming and almost impossible. During a Bachelor End Project in 2018 four students (van Deursen et al., 2018) created the Teach- ing Assistant Management (TAM) platform. This project aimed to ease the process of recruiting and scheduling TAs. All parties involved in the process of appointing TAs can use TAM to provide their input. Lecturers can register their courses on TAM and students are able to indicate their interest and availability to help with different courses. However, the first version of TAM missed a number of important features. For example, student avail- ability data had to be extracted manually and teachers still had to email their TA selection to the coordinator. This project aims to continue and improve TAM with these missing features. In order to achieve this goal TAM 2.0 has been developed. TAM 2.0 consists of three components: a MySQL database, a back end written using Spring and Java containing the business logic, and a front end website created using Vue to provide an interface to its users. TAM 2.0 also integrated LabraCORE. LabraCORE provides user and course information to several platforms and stores it conveniently in one central place.