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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
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Educational Researcher
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National Science Foundation
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  1. Background/Context:

    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:

    This study focuses on the exploration of the kind of relationship that bilingual Latinx students developed with themselves and computer programming and mathematics (CPM) practices through their participation in a CPM after-school program, first as students and then as cofacilitators teaching CPM practices to other middle school peers.


    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 indicated that students had continued/increased motivation and confidence in CPM as they engaged in a journey as cofacilitators, described through two thematic categories: (a) shifting views by personally connecting to CPM, and (b) affirming CPM practices through teaching. The shift in connecting to CPM practices evolved as students argued that they found a new way of learning mathematics, in that they used mathematics as a tool to create videos and images that they programmed by using Python while making sense of the process bilingually (Spanish and English). This mathematics was viewed by students as high level, which in turned helped students gain self-confidence in CPM practices. Additionally, students affirmed their knowledge and confidence in CPM practices by teaching them to others, a process in which they had to mediate beyond the understanding of CPM practices. They came up with new ways of explaining CPM practices bilingually to their peers. In this new role, cofacilitators considered the topic and language, and promoted a communal support among the peers they worked with.


    Bilingual middle school students can not only program, but also teach bilingually and embrace new roles with nurturing support. Schools can promote new student roles, which can yield new goals and identities. There is a great need to redesign the school mathematics curriculum as a discipline that teenagers can use and connect with by creating and finding things they care about. In this way, school mathematics can support a closer “fit” with students’ identification with the world of mathematics. Cofacilitators learned more about CPM practices by teaching them, extending beyond what was given to them, and constructing new goals that were in line with a sophisticated knowledge and shifts in the practice. Assigned responsibility in a new role can strengthen students’ self-image, agency, and ways of relating to mathematics.

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  2. Objectives We examine the community epistemologies in youth’s iterative refinements of STEM-rich inventions across settings and time. Iteration in STEM-rich engineering/invention work refers to re-thinking ideas/designs within prototyping processes (Cunningham & Kelly, 2017). The objective of this paper is to examine the political dimensions of iteration through a) how iteration involves pre- and post-design “lives” of inventions especially towards new social futures, and b) the intentional incorporation of cultural epistemologies towards advancing new forms of legitimate inventor knowledge/practice (Yosso, 2005). Framing We draw from critical justice and consequential learning studies. Critical justice focuses on recognizing diversity and addressing structural inequalities perpetuated through systemic racism and classism. It seeks re-shifted relations of power and position within multiple scales-of-activity in learning, intersected with historicized injustices in learning environments. Consequential learning examines what matters to people, and how associated values and practices, when coordinated through social activity, allows for imagining new social futures (Gutierrez, 2012). Viewing the iterative process of inventing through a justice-oriented consequential lens calls into question traditional modes of knowing, and challenges/expands who and what areas of expertise are recognized and valued. Methods Our study takes place in two community makerspaces in mid-sized cities. Both center community engagement and support youth in designing/inventing to address problems they and their communities care about. Both also support minoritized youth in inventing through engagement with a wide range of community/STEM stakeholders. In researcher-educator roles, we collaborated with both makerspaces to establish programs supporting youth in sustained engagement in STEM and making/inventing in culturally-sustaining ways. In our two-year, longitudinal critical ethnography, data were generated in weekly community making sessions between 2016-2018. Data include artifacts, youth conversation groups, and videos capturing youth interaction with STEM and community experts at various stages in their design process. Analysis involved multiple stages and levels of coding based on open-coding and constant comparison procedures. Findings We ground our paper in four in-depth longitudinal cases of youth’s iterative design work: Nila’s light-up #stopracism sign; Su’zanne’s massaging slipper, Sharon’s geodesic play dome, and Jazmyn’s portable fan. Across cases, we illustrate three findings. First, youth located broader injustices within local making/inventing discourses with support from community and STEM allies, suggesting youth drew from multiple epistemologies, some grounded in community cultural wealth, others in STEM. For example, Su’Zanne drew from a familial culture of care and resistance in recognizing injustices nested in homelessness while iterating a way to make her slipper “more massaging.” The geodesic dome youth-makers drew from collective solidarity/resistance in making a structure for younger peers due to unjust lack of play infrastructure. Second, iterative engagement involving community wealth afforded further design and inventing experiences and expanded ownership over inventions across many stakeholders. For example, youth turned Nila’s #stopracism sign on during group discussions when they felt that racism needed to be foregrounded. Third, the afterlife of youth invention processes impacted the emergent inventor-maker culture through influencing the iterative process. Significance Iterations expand hybridization of cultural knowledge/practice and STEM-rich inventing, re-shaping whose cultural knowledge matters, and fostering justice-oriented collective outcomes. 
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  4. 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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