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Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education

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Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education

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dc.contributor.author Carrano, Dominic es_ES
dc.contributor.author Chugunov, Ilya es_ES
dc.contributor.author Lee, Jonathan es_ES
dc.contributor.author Ayazifar, Babak es_ES
dc.date.accessioned 2020-06-11T08:12:02Z
dc.date.available 2020-06-11T08:12:02Z
dc.date.issued 2020-04-21
dc.identifier.isbn 9788490488119
dc.identifier.issn 2603-5871
dc.identifier.uri http://hdl.handle.net/10251/146081
dc.description.abstract [EN] 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. es_ES
dc.language Inglés es_ES
dc.publisher Editorial Universitat Politècnica de València es_ES
dc.relation.ispartof 6th International Conference on Higher Education Advances (HEAd'20)
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Higher Education es_ES
dc.subject Learning es_ES
dc.subject Educational systems es_ES
dc.subject Teaching es_ES
dc.subject Python es_ES
dc.subject Jupyter Notebooks es_ES
dc.subject Virtual labs es_ES
dc.subject Educational technology es_ES
dc.subject Signals and systems es_ES
dc.subject Electrical engineering. es_ES
dc.title Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education es_ES
dc.type Capítulo de libro es_ES
dc.type Comunicación en congreso es_ES
dc.identifier.doi 10.4995/HEAd20.2020.11308
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Carrano, D.; Chugunov, I.; Lee, J.; Ayazifar, B. (2020). Self-Contained Jupyter Notebook Labs Promote Scalable Signal Processing Education. En 6th International Conference on Higher Education Advances (HEAd'20). Editorial Universitat Politècnica de València. (30-05-2020):1409-1416. https://doi.org/10.4995/HEAd20.2020.11308 es_ES
dc.description.accrualMethod OCS es_ES
dc.relation.conferencename Sixth International Conference on Higher Education Advances es_ES
dc.relation.conferencedate Junio 02-05,2020 es_ES
dc.relation.conferenceplace València, Spain es_ES
dc.relation.publisherversion http://ocs.editorial.upv.es/index.php/HEAD/HEAd20/paper/view/11308 es_ES
dc.description.upvformatpinicio 1409 es_ES
dc.description.upvformatpfin 1416 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.issue 30-05-2020
dc.relation.pasarela OCS\11308 es_ES


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