![]() My homework assignments and model answers are also in Jupyter notebooks, so making an exam as a notebook is natural. How much (extra) work did it cost you? Does it outweigh the benefits? I will spend more time on the unit tests which allow intelligent autograding. In my (limited) experience, all students remained until the last moment of the exam: because of the feedback of the notebook, they can keep on trying and improving answers.Īre you going to use it again? If yes, what would you change in the next iteration? Students seem to love working with Jupyter Notebook, not only because they seem to get higher grades. What was the students’ experience? Did they like it and/or did they perform better? Good news for teachers is that this is a way of examining which scales well with student numbers without sacrificing quality of the questions asked. As you can see, this tool provides many advantages!ĭefinitely! Students can now make their exams on the Jupyter Notebooks, allowing them to both save time and formulate better answers. data driven), and exciting problems, and it uses auto grading (although manual grading can be done too, and is remarkably fast). This is the natural way of solving problems.įurthermore, notebooks provide a better measurement of learning objectives, teachers can pose more realistic (e.g. Students can experiment with an answer and receive immediate feedback when they try it out on data. The notebook has lots of low level support helping the student to focus on deeper skills than rote-learning (e.g., autocompletion on variables and methods of objects, complete manual inside the notebook). Jupyter Notebooks provide an interactive problem solving environment, with support for 100s of programming languages such as Python, R, and much more (although at this moment only the Python kernel is installed). Jupyter Notebook (winner of the 2018 ACM Software Award) is installed on all computers in the digital exam rooms at the UvA/HvA since February 2018. Manually grading the solutions scales well. With Jupyter notebooks students can solve complex problems in a natural and familiar environment. I test this in group homework assignments, but wanted to test it also using real life puzzles and data during exams. I give courses in which algorithmic thinking and problem solving play a crucial role. Alternatively if you’re looking for some inspiration then check out this incredible gallery of Jupyter notebooks.Can you tell something about your course before the innovation? What was the issue you were facing in your course? ipynb file to a new or existing repository to view the rendered notebook. With more than 200,000 Jupyter notebooks already on GitHub we’re excited to level-up the GitHub-Jupyter experience. With Git Large File Storage and Jupyter notebook support, GitHub has never been a better place to version and collaborate on data-intensive workflows. From today Jupyter notebooks render in all their glory right here on GitHub. Jupyter notebooks solve this problem by making it easy to capture data-driven workflows that combine code, equations, text and visualizations and share them with others. Whether you’re a researcher studying Wikipedia, an astronomer investigating the movements of galaxies in our cosmic neighborhood or a data-scientist at fashion retailer Stitch Fix, producing insights from data and sharing is a common challenge. Communicating ideas that combine code, data and visualizations can be hard, especially if you’re trying to collaborate in realtime with your colleagues.
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