In beginning computer science courses, students are often overwhelmed by the complex and novel way of problem-solving. Having a good expectation of how long the task is going to take is not easy even for students with much experience in programming. Without knowing their progress, beginners may become discouraged, have poor efficiency, miss deadlines, fail the course, and even drop out of the program. In this paper, I introduce a way to predict student time spent on Jupyter assignments by training models with collected student logs. The key idea of this project is that the student’s future relative progress can be deduced by the student’s past relative progress and problems together with information on other students. I develop a toolkit used for creating and troubleshooting prediction schemes. I present two ways of prediction: Assignment-based binary classification, and Cell-based ternary completion time. These two approaches have satisfactory results but still have room for improvement. I also evaluate an adaptation of an existing prediction scheme and compare the results. The video of the oral presentation of this thesis is available at (https://youtu.be/2qiuRLjfF-Y)Bachelor of Scienc
