Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding
Data science knowledge integration: Affordances of a computational cognitive apprenticeship on student conceptual understanding
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数据科学知识整合：计算认知学徒期对学生概念理解的启示
DOI：
10.1002/cae.22580
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发表时间：
2023
影响因子：
2.9
通讯作者：
Magana, Alejandra J.
中科院分区：
文献类型：
--
作者：
Sánchez‐Peña, Matilde;Vieira, Camilo;Magana, Alejandra J.
本研究实施了一个计算认知学徒框架，用于通过计算笔记本提供的数据科学（DS）概念的知识整合。本研究还探讨了学生在接触 K-means 无监督机器学习算法后对该方法的概念理解。 DS 方法和技术的学习对于新一代工程本科生来说已变得至关重要。然而，人们对支持学生学习 DS 和机器学习 (ML) 算法的有效策略知之甚少。研究问题是：在使用计算认知学徒方法设计的交互式可视化之后，学生如何概念化他们对无监督机器学习方法的理解？交互式可视化的可供性如何支持或阻碍学生对无监督机器学习方法的知识整合？基于设计的研究允许在工作课堂的背景下迭代设计、实施和验证教学法。为此，数据收集方法通常采用学生工件的形式。我们对学习过程中学生的书面回答和反思进行了定性内容分析。结果表明，计算认知学徒期促进了知识整合。在与计算笔记本互动后，大多数学生对目标和方法的性质有了准确的概念，并确定了影响算法输出的因素。学生们发现对该方法进行具体表示很有用，它支持其概念理解并展示了其适当执行所需的战略知识的获取。然而，我们也发现了学生对该算法的重要误解。
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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