Using the Jupyter Notebook as a Tool to Support the Teaching and Learning Processes in Engineering Courses

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The Challenges of the Digital Transformation in Education (ICL 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 917))

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Abstract

Teaching and learning processes can benefit from the use of online resources, enabling the improvement of teachers and students productivity and giving them flexibility and support for collaborative work. Particularly in engineering courses, open source tools, such as Jupyter Notebook, provide a programming environment for develo** and sharing educational materials, combining different types of resources such as text, images and code in several programming languages in a single document, accessible through a web browser. This environment is also suitable to provide access to online experiments and explaining how to use them. This article presents some examples of online resources supported by Jupyter Notebook, in subjects of an Informatics Engineering course, seeking to contribute to the development of innovative teaching methodologies.

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Notes

  1. 1.

    http://jupyter.org/ (last accessed: July 20, 2018).

  2. 2.

    https://github.com/albjlcardoso/python_examples/blob/master/Udometer_online_v2.ipynb (last accessed: July 20, 2018).

References

  1. Garrison, D.R., Vaughan, N.D.: Blended Learning in Higher Education: Framework, Principles, and Guidelines. Wiley (2008)

    Google Scholar 

  2. Pérez, F., Granger, B.E.: IPython: a system for interactive scientific computing. Comput. Sci. Eng. 9(3), 21–29 (2007)

    Article  Google Scholar 

  3. Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C.: Jupyter development team: Jupyter notebooks—a publishing format for reproducible computational workflows. In: ebook “Positioning and Power in Academic Publishing: Players, Agents and Agendas”, pp. 87–90 (2016)

    Google Scholar 

  4. Iverson, K.E.: A Programming Language, New York, NY. Wiley, USA (1962)

    Google Scholar 

  5. Spence, R.: APL Demonstration, Imperial College London (1975). Available from: https://www.youtube.com/watch?v=_DTpQ4Kk2wA. Last Accessed 20 July 2018

  6. Cardoso, A., Leitão, J., Gil, P., Marques, S.M., Simões, N.E.: Using IPython to demonstrate the usage of remote labs in engineering courses—a case study using a remote rain gauge. In: Proceedings of the 15th International Conference on Remote Engineering and Virtual Instrumentation (REV2018), pp. 683–689 (2018)

    Google Scholar 

  7. Raju, A.B.: IPython notebook for teaching and learning. In: Natarajan R. (eds) Proceedings of the International Conference on Transformations in Engineering Education. Springer, New Delhi (2015)

    Google Scholar 

  8. Hamrick, J.B.: Creating and grading IPython/Jupyter notebook assignments with NbGrader. In: Proceedings of the 47th ACM Technical Symposium on Computing Science Education—SIGCSE’16, pp. 242–242 (2016)

    Google Scholar 

  9. Un**co, J.: Python for Signal Processing. Springer (2014). Available from: https://github.com/un**co/Python-for-Signal-Processing. Last Accessed 20 July 2018

    Book  Google Scholar 

  10. Johansson, R.: QuTiP Lectures as IPython Notebooks. Available from: https://github.com/jrjohansson/qutip-lectures. Last Accessed 20 July 2018

  11. Dhar, V.: Data science and prediction. Commun. ACM 56(12), 64 (2013)

    Article  Google Scholar 

  12. Leek, J.: The key word in “data science” is not data, it is science. Simply Stat. (2013)

    Google Scholar 

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Acknowledgements

This work has been partially supported by the Portuguese Foundation for Science and Technology (FCT) under the project UID/EEA/00066/2013 and the Ph.D. grant SFRH/BD/122103/2016.

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Correspondence to Alberto Cardoso .

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Cardoso, A., Leitão, J., Teixeira, C. (2019). Using the Jupyter Notebook as a Tool to Support the Teaching and Learning Processes in Engineering Courses. In: Auer, M., Tsiatsos, T. (eds) The Challenges of the Digital Transformation in Education. ICL 2018. Advances in Intelligent Systems and Computing, vol 917. Springer, Cham. https://doi.org/10.1007/978-3-030-11935-5_22

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