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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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    Computer programming is rarely accessible to K–12 students, especially for those from culturally and linguistically diverse backgrounds. Middle school age is a transitioning time when adolescents are more likely to make long-term decisions regarding their academic choices and interests. Having access to productive and positive knowledge and experiences in computer programming can grant them opportunities to realize their abilities and potential in this field.

    Purpose/Focus of Study:

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    Setting:

    An after-school program, Advancing Out-of-School Learning in Mathematics and Engineering (AOLME), was held at two middle schools located in rural and urban areas in the Southwest. It was designed to support an inclusive cultural environment that nurtured students’ opportunities to learn CPM practices through the inclusion of languages (Spanish and English), tasks, and participants congruent to students in the program. Students learned how to represent, design, and program digital images and videos using a sequence of 2D arrays of hexadecimal numbers with Python on a Raspberry Pi computer. The six bilingual cofacilitators attended Levels 1 and 2 as students and were offered the opportunity to participate as cofacilitators in the next implementation of Level 1.

    Research Design:

    This longitudinal case study focused on analyzing the experiences and shifts (if any) of students who participated as cofacilitators in AOLME. Their narratives were analyzed collectively, and our analysis describes the experiences of the cofacilitators as a single case study (with embedded units) of what it means to be a bilingual cofacilitator in AOLME. Data included individual exit interviews of the six cofacilitators and their focus groups (30–45 minutes each), an adapted 20-item CPM attitude 5-point Likert scale, and self-report from each of them. Results from attitude scales revealed cofacilitators’ greater initial and posterior connections to CPM practices. The self-reports on CPM included two number lines (0–10) for before and after AOLME for students to self-assess their liking and knowledge of CPM. The numbers were used as interview prompts to converse with students about experiences. The interview data were analyzed qualitatively and coded through a contrast-comparative process regarding students’ description of themselves, their experiences in the program, and their perception of and relationship toward CPM practices.

    Findings:

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    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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