Michael Zastre
ABSTRACT
Jupyter Notebook is currently used as the programming environment for labs and assignments in a CS1 course at the University of Victoria. Some motivation for this choice is given, and the paper then acts as an experience report with a specific focus on the way the environment helps and hinders teaching and learning of Python 3, specifically for new programmers.
- IPython Interactive Computing. 2019. Retrieved March 16, 2019 from https://ipython.orgGoogle Scholar
- Anaconda Distribution. 2019. Retrieved March 16, 2019 from https://www.anaconda.com/distributionGoogle Scholar
- Tony Gaddis. 2017. Starting out with Python (4th ed.). Pearson Higher Education. Google ScholarDigital Library
- Project Jupyter. 2019. Retrieved March 15, 2019 from https://jupyter.org/Google Scholar
- ACM Recognizes Innovators Who Have Shaped the Digital Revolution. 2017. Retrieved March 15, 2019 from https://awards.acm.org/about/2017-technical-awardsGoogle Scholar
Index Terms
- Jupyter Notebook in CS1: An Experience Report
Recommendations
Reviews
Franz J Kurfess
Having observed the ease with which computer science (CS) students with little to no experience in machine learning can start working with existing code presented through Jupyter Notebooks (JNs), together with a few colleagues I explored their use for people outside of CS. Our first experiments went well, and so I started looking for similar approaches in the literature. In this experience report, the author discusses the use of JNs for an introductory programming class in Python, taken by a significant number of students from outside of CS. One appealing aspect of JNs is their ease of access and installation: they rely on a web browser as a primary interface, come pre-installed with the popular Anaconda Python package, and can also be hosted in a cloud-based environment (for example, Google's Colab). The mixture of text cells and code cells lends itself to an interleaved style of learning: participants read an explanation of the concepts (possibly augmented by images or diagrams) and then try out the respective code. Budding programmers can write their own code, while less adventurous readers can simply run the provided code, observe the outcome, and possibly make modifications to the code. While they may not be suitable for the development of complex programs, the author's experience reflects our own: JNs are excellent tools to guide learners with limited or no coding experience through activities presented as a sequence of text and code cells in order to accomplish a computational task. As their wide acceptance in the data science and machine learning community shows, these tasks range from simple beginner exercises to highly convoluted scripts for experiments involving sophisticated machine learning libraries.
Access critical reviews of Computing literature here
Become a reviewer for Computing Reviews.
Comments