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Title: A Call for a Humanistic Stance Toward K–12 Data Science Education
There is growing interest in how to better prepare K–12 students to work with data. In this article, we assert that these discussions of teaching and learning must attend to the human dimensions of data work. Specifically, we draw from several established lines of research to argue that practices involving the creation and manipulation of data are shaped by a combination of personal experiences, cultural tools and practices, and political concerns. We demonstrate through two examples how our proposed humanistic stance highlights ways that efforts to make data personally relevant for youth also necessarily implicate cultural and sociopolitical dimensions that affect the design and learning opportunities in data-rich learning environments. We offer an interdisciplinary framework based on literature from multiple bodies of educational research to inform design, teaching and research for more effective, responsible, and inclusive student learning experiences with and about data.  more » « less
Award ID(s):
1900606
NSF-PAR ID:
10314699
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Educational Researcher
Volume:
50
Issue:
9
ISSN:
0013-189X
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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     Purpose/Focus of Study:

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    Abstract Practitioner notes

    What is already known about this topic

    Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models.

    While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data.

    There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data.

    What this paper adds

    Results show that students developed nuanced understandings of models learning patterns in data for automated decision making.

    Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models.

    Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in.

    Implications for practice and/or policy

    It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies.

    Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources.

    To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).

     
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