2020
DOI: 10.1002/cae.22277
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Digital signal processing course on Jupyter–Python Notebook for electronics undergraduates

Abstract: The Jupyter–Python Notebook open source is currently used world‐wide as a programming environment for courses and labs. This paper describes an application of a set of Python computer programs implemented in the form of Jupyter notebooks to learn the course of digital signal processing and also seeks to contribute to the development of innovative teaching methodologies. A set of user‐friendly Jupyter notebooks workflows allows teachers–students to teach and learn theoretical concepts, description of problems, … Show more

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Cited by 14 publications
(6 citation statements)
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“…Computational notebooks have gained traction in recent years as a popular format for instruction in higher education. Despite the original goals for their use, which were focused on the expansion of computational literacy and research reproducibility [49,52], the potential for their use in educational spaces has been extensively documented [5,44,53], [47,74]. Examples of their use now include topics such as artificial intelligence [44], chemical engineering [5,20], optimization [53], digital signal processing [74], computational modeling [47], and remote lab experimentation [7].…”
Section: Computational Notebooksmentioning
confidence: 99%
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“…Computational notebooks have gained traction in recent years as a popular format for instruction in higher education. Despite the original goals for their use, which were focused on the expansion of computational literacy and research reproducibility [49,52], the potential for their use in educational spaces has been extensively documented [5,44,53], [47,74]. Examples of their use now include topics such as artificial intelligence [44], chemical engineering [5,20], optimization [53], digital signal processing [74], computational modeling [47], and remote lab experimentation [7].…”
Section: Computational Notebooksmentioning
confidence: 99%
“…However, most of the existing literature on the use of computational notebooks for teaching challenging concepts is limited to reporting the technical implementation of the notebooks [e.g., [53,74]]. Few of them include the learning goals of the summarized courses [e.g., [7]] and even less report on any output related to the student experience, such as student engagement [53], or student satisfaction [47,74]. In addition, some criticism exists that computational notebooks can also propagate the dissemination of negative learning outcomes, such as poor coding practices [67].…”
Section: Computational Notebooksmentioning
confidence: 99%
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“…A similar study was conducted by [10] to prepare teaching materials for electronics undergraduates through JNs. The authors created modules to teach the subject Digital Signal Processing to the students using these modules.…”
Section: Related Workmentioning
confidence: 99%
“…Table 1 shows the main educational goals of courses within different educational areas where Jupyter Notebooks are employed. The versatility of Jupyter Notebooks for educational purposes means that they have been used in different fields [39]: Computer Science, Agriculture, Manufacturing, Mechatronics, Chemical Engineering, Digital Humanities, and so on. These notebooks are usually employed for teaching concepts and promoting active learning environments.…”
Section: Introductionmentioning
confidence: 99%