Jupyter Notebooks for the study of advanced topics in Fluid Mechanics

Castilla, R.; Peña, Marta

doi:10.1002/cae.22619

Cited by 8 publications

(5 citation statements)

References 38 publications

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“…This study is innovative in its use of Jupyter Notebook as a pedagogical tool for the instruction of epidemiology and, to the best of the author’s knowledge, is the first study to do so. However, previous studies in other fields [ 9 , 19 - 22 ] have revealed that Jupyter Notebook is an effective tool for teaching data analysis.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Learning About Epidemiological Data Analysis Using R for Graduate Students in Medical Fields With Jupyter Notebook: Classroom Action Research

Kumwichar¹

2023

JMIR Med Educ

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Background Graduate students in medical fields must learn about epidemiology and data analysis to conduct their research. R is a software environment used to develop and run packages for statistical analysis; it can be challenging for students to learn because of compatibility with their computers and problems with package installations. Jupyter Notebook was used to run R, which enhanced the graduate students’ ability to learn epidemiological data analysis by providing an interactive and collaborative environment that allows for more efficient and effective learning. Objective This study collected class reflections from students and their lecturer in the class “Longitudinal Data Analysis Using R,” identified problems that occurred, and illustrated how Jupyter Notebook can solve those problems. Methods The researcher analyzed issues encountered in the previous class and devised solutions using Jupyter Notebook. These solutions were then implemented and applied to a new group of students. Reflections from the students were regularly collected and documented in an electronic form. The comments were then thematically analyzed and compared to those of the prior cohort. Results Improvements that were identified included the ease of using Jupyter R for data analysis without needing to install packages, increased student questioning due to curiosity, and students having the ability to immediately use all code functions. After using Jupyter Notebook, the lecturer could stimulate interest more effectively and challenge students. Furthermore, they highlighted that students responded to questions. The student feedback shows that learning R with Jupyter Notebook was effective in stimulating their interest. Based on the feedback received, it can be inferred that using Jupyter Notebook to learn R is an effective approach for equipping students with an all-encompassing comprehension of longitudinal data analysis. Conclusions The use of Jupyter Notebook can improve graduate students’ learning experience for epidemiological data analysis by providing an interactive and collaborative environment that is not affected by compatibility issues with different operating systems and computers.

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Section: Discussionmentioning

confidence: 99%

Enhancing Learning About Epidemiological Data Analysis Using R for Graduate Students in Medical Fields With Jupyter Notebook: Classroom Action Research

Kumwichar¹

2023

JMIR Med Educ

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“…Related to this are more computationallyfocused web tools such as CodeRunner (Lobb and Harlow 2016) for rapid unit testing of student-written programs, as well as the growing use of Jupyter Notebooks (Cardoso et al 2019) for exploring a variety of different areas of scientific computing in interactive ways. Jupyter Notebooks have even been developed to explore areas of computational fluid dynamics (Castilla and Peña 2023) and reaction-transport processes (Golman 2019), showcasing the versatility of these web-based tools even for complex scientific computing tasks such as solving partial differential equations. We direct the interested reader to the work of Engelbrecht et al (2020) for a broad overview on the role of internet technologies in transforming educational environments.…”

Section: Historical and Pedagogical Contextmentioning

confidence: 99%

VisualPDE: Rapid Interactive Simulations of Partial Differential Equations

Walker,

Townsend,

Chudasama

et al. 2023

Bull Math Biol

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Computing has revolutionised the study of complex nonlinear systems, both by allowing us to solve previously intractable models and through the ability to visualise solutions in different ways. Using ubiquitous computing infrastructure, we provide a means to go one step further in using computers to understand complex models through instantaneous and interactive exploration. This ubiquitous infrastructure has enormous potential in education, outreach and research. Here, we present VisualPDE, an online, interactive solver for a broad class of 1D and 2D partial differential equation (PDE) systems. Abstract dynamical systems concepts such as symmetry-breaking instabilities, subcritical bifurcations and the role of initial data in multistable nonlinear models become much more intuitive when you can play with these models yourself, and immediately answer questions about how the system responds to changes in parameters, initial conditions, boundary conditions or even spatiotemporal forcing. Importantly, VisualPDE is freely available, open source and highly customisable. We give several examples in teaching, research and knowledge exchange, providing high-level discussions of how it may be employed in different settings. This includes designing web-based course materials structured around interactive simulations, or easily crafting specific simulations that can be shared with students or collaborators via a simple URL. We envisage VisualPDE becoming an invaluable resource for teaching and research in mathematical biology and beyond. We also hope that it inspires other efforts to make mathematics more interactive and accessible.

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“…These notebooks with explanations can become computational essays that incorporate code to support their theses [50]. Notebooks are used in various pedagogical scenarios and fields [48], [53], with different applications and systems involving student and teacher collaboration [54], used to create educational web apps [48], and research web apps [49].…”

Section: Notebooksmentioning

confidence: 99%

“…Jupyter [16] is an open-source project that develops an environment of products around notebooks, with Jupyter Notebook as the core product [14]. Jupyter Notebook is Jupyter's document format and notebook environment, which is considered the standard and most commonly used computational notebook for education and research [15], [46], [54]. The open nature of Jupyter allows the possibility of extending the software base capabilities [15] but also manipulating the notebooks themselves with custom tools, given the open nature of the document format [14], which is stored in plain JSON format.…”

Section: Notebooksmentioning

confidence: 99%

Remote Labs Meet Computational Notebooks: An Architecture for Simplifying the Workflow of Remote Educational Experiments

Vanegas-Guillén,

Parra-Rosero,

Muñoz-Antón

et al. 2023

IEEE Access

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Online laboratories and computational notebooks have established themselves as essential tools in the fields of engineering and science education, significantly enhancing the teaching and learning experience. Despite the benefits of remote laboratories, and although architectures, models, and tools have been proposed to facilitate the work involved in their implementation, challenges remain in the development lifecycle. This paper proposes a novel architecture that simplifies the development and management of remote experiments using a publisher-subscriber communication paradigm to securely and efficiently integrate WebAssembly computational notebooks. This approach eliminates the need for additional infrastructure for communication at the lab stations. It also avoids the need for servers to deploy notebooks, because all operations, calculations, and data processing run locally in the user's web browser. To achieve this, a remote laboratory management system (RLMS) interoperable with virtual learning environments was designed and implemented, including notebook-based authoring, learning scenarios, and grading tools. As a case study, remote experiments were developed to characterize and evaluate the performance of heat exchangers using a thermal fluid systems test bench built for this project. Several quality criteria categorized into technical, educational, and adaptability groups were quantitatively evaluated using the responses of 70 participants from two different universities. The analysis highlighted both the advantages and challenges of the proposal, with a significant emphasis on interaction, scalability, reusability, interoperability, and accessibility. The results confirmed the effectiveness of the notebooks in each phase of the remote experiments, demonstrating an increase in students' active and autonomous participation and exploration.

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Jupyter Notebooks for the study of advanced topics in Fluid Mechanics

Cited by 8 publications

References 38 publications

Enhancing Learning About Epidemiological Data Analysis Using R for Graduate Students in Medical Fields With Jupyter Notebook: Classroom Action Research

Enhancing Learning About Epidemiological Data Analysis Using R for Graduate Students in Medical Fields With Jupyter Notebook: Classroom Action Research

VisualPDE: Rapid Interactive Simulations of Partial Differential Equations

Remote Labs Meet Computational Notebooks: An Architecture for Simplifying the Workflow of Remote Educational Experiments

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