Using the IPython notebook as the computing platform for signals and systems courses

Lovejoy, McKenna R.; Wickert, M.A.

doi:10.1109/dsp-spe.2015.7369568

Cited by 7 publications

(3 citation statements)

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“…Around the time our Department restructured its lower-division curriculum, the Jupyter Notebook (Kluyver et al, 2016) was published as a spin-off of the IPython suite (Pérez & Granger, 2007), providing an interactive platform for scientific computing ("Project Jupyter," 2020). Since its initial release, the Jupyter Notebook has exploded in popularity as an educational tool in fields such as signal processing, machine learning, and artificial intelligence (Lovejoy & Wickert, 2015;O'Hara et al, 2015;Granado et al, 2018;Herta et al, 2019). Even within our Department, many other courses, such as EE 123: Digital Signal Processing and EECS 126: Probability and Random Processes, use Jupyter Notebook assignments.…”

Section: Design Considerationsmentioning

confidence: 99%

Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education

Carrano

Chugunov

Ayazifar

2020

6th International Conference on Higher Education Advances (HEAd'20)

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Our upper-division course in Signals and Systems at UC Berkeley comprises primarily sophomore and junior undergraduates, and assumes only a basic background in Electrical Engineering and Computer Science. We’ve introduced Jupyter Notebook Python labs to complement the theoretical material covered in more traditional lectures and homeworks. Courses at other institutions have created labs with a similar goal in mind. However, many have a hardware component or involve in-person lab sections that require teaching staff to monitor progress. This presents a significant barrier for deployment in larger courses. Virtual labs—in particular, pure software assignments using the Jupyter Notebook framework—recently emerged as a solution to this problem. Some courses use programming-only labs that lack the modularity and rich user interface of Jupyter Notebook’s cell-based design. Other labs based on the Jupyter Notebook have not yet tapped the full potential of its versatile features. Our labs (1) demonstrate real-life applications; (2) cultivate computational literacy; and (3) are structured to be self-contained. These design principles reduce overhead for teaching staff and give students relevant experience for research and industry.

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Section: Design Considerationsmentioning

confidence: 99%

Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education

Carrano

Chugunov

Ayazifar

2020

6th International Conference on Higher Education Advances (HEAd'20)

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“…A balance has to be reached where the student can follow the mathematical process. On the basis of recently reported research, Jupyter Notebook self‐regulated learning has been gaining ground due to its interactive environment [11,24,32], which makes it easy to manipulate code and adjust its parameters. Finally, its very recent use in education means that the tool is studied here for the first time in the area of structural analysis where materials related to other programming languages such as MATLAB and Mathematica [3] can also be found.…”

Section: Introductionmentioning

confidence: 99%

Teaching structural analysis theory with Jupyter Notebooks

Suárez‐García

Arce

Hernández

et al. 2021

Comp Applic In Engineering

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The STEAM model combines the academic disciplines of Science, Technology, Engineering, Arts, and Mathematics (STEAM), forming a very relevant and increasingly fundamental educational approach within technologically advanced societies. Real‐world concepts and applications are associated in ways that encourage students to apply them whenever possible to their learning processes. In this context, Jupyter Notebooks and their highly innovative tools offer transparency for scientific research and the reproducibility of new findings. These virtual notebooks can be used to display text, video, and embedded interactive graphics. In this study, two laboratory sessions are described where students use the Jupyter Notebook to learn about the direct stiffness method. This study unit within the Theory of Structures and Industrial Constructions course module involves matrix structural analysis, requiring intensive matrix calculus and computational methods, which cannot be solved with pen and paper alone. Student use of the Jupyter Notebook when applying the direct stiffness method to the resolution of reticulated structures was studied in two laboratory sessions. An example of the software was first presented and explained. The students were then required to solve a series of problems of increasing difficultly where only part of the solution was available to check their results, thereby facilitating self‐directed learning. At the end of the session, the students were administered a survey to evaluate their satisfaction. The survey responses reflected their high levels of acceptance of this teaching methodology.

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“…Moreover, the inclusion of the theory of Integer Programming (or Linear Integer Programming) may pose a challenge for students and lecturers. Thus, based on literature research, Jupyter Notebook self‐regulated learning is growing in importance in recent years through its interactive environment, making possible to touch the code and make changes to parameters without requiring the compilation of the entire program [12,31]. But it should be emphasized that the use of this type of tools in the educational field is recent , so it was not found any application in the area of manufacturing teaching.…”

Section: Introductionmentioning

confidence: 99%

Teaching optimization of manufacturing problems via code components of a Jupyter Notebook

Suarez¹,

Álvarez-Feijoo²,

Fernández‐González

et al. 2018

Comp Applic In Engineering

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The Degree in Mechanical Engineering combines knowledge and skills of the academic STEM disciplines (Science, Technology, Engineering, and Mathematics). The teaching of the STEM subjects must give prominence to the association of the physical, chemical, and mathematical concepts with the real world. The Jupyter Notebook can be defined as a scientific interactive framework. They can contain text, mathematical equations, interactive graphs, and videos. It allows creating a workspace for watching and playing with the concepts to be learned. Because of their didactic usefulness and easy divulgation, they are highly recommended as a learning environment. In the present work, there are reflected the experiences of using interactive Jupyter Notebook in virtual laboratory sessions within a 4th year subject of Mechanical Engineering related with manufacturing processes. Students' judgments on the efficacy of this resource were evaluated. The results showed that students are in agreement on the effectiveness of using Jupyter notebooks as a teaching‐learning tool.

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Using the IPython notebook as the computing platform for signals and systems courses

Cited by 7 publications

References 0 publications

Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education

Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education

Teaching structural analysis theory with Jupyter Notebooks

Teaching optimization of manufacturing problems via code components of a Jupyter Notebook

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