Flipping a Computational Modeling Class: Strategies to Engage Students and Foster Active Learning

Ortega-Alvarez, Juan David; Vieira, Camilo; Guarín-Zapata, Nicolás; Gómez, Juan David

doi:10.1109/fie44824.2020.9273890

Cited by 2 publications

(3 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%

“…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%

“…In addition, some criticism exists that computational notebooks can also propagate the dissemination of negative learning outcomes, such as poor coding practices [67]. As a result, a set of recommendations has been developed for the use of computational notebooks in science reproducibility [48]; it is important to make a clear distinction between the use of computational notebooks for the development of programming skills and work organization, which is their usual goal in computer science spaces [49,52], and their use for achieving specific learning goals in other areas of knowledge [5,44,53], [47,74].…”

Section: Computational Notebooksmentioning

confidence: 99%

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Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding

Sánchez‐Peña

Vieira

Magana

2022

Comp Applic In Engineering

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This study implements a computational cognitive apprenticeship framework for knowledge integration of Data Science (DS) concepts delivered via computational notebooks. This study also explores students' conceptual understanding of the unsupervised Machine Learning algorithm of K‐means after being exposed to this method. The learning of DS methods and techniques has become paramount for the new generations of undergraduate engineering students. However, little is known about effective strategies to support student learning of DS and machine learning (ML) algorithms. The research questions are: How do students conceptualize their understanding of an unsupervised ML method after engaging with interactive visualizations designed using the computational cognitive apprenticeship approach? How do the affordances of the interactive visualizations support or hinder student knowledge integration of an unsupervised machine learning method? Design‐based research allowed for the iterative design, implementation, and validation of the pedagogy in the context of a working classroom. For this, data collection methods often take the form of student artifacts. We performed a qualitative content analysis of students' written responses and reflections elicited during the learning process. Results suggest that the computational cognitive apprenticeship promoted knowledge integration. After interacting with the computational notebooks, most students had accurate conceptions of the goal and the nature of the method and identified factors affecting the output of the algorithm. Students found it useful to have a concrete representation of the method, which supported its conceptual understanding and showcased the acquisition of strategic knowledge for its appropriate execution. However, we also identified important misconceptions students held about the algorithm.

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