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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Centering Minoritized Students' Perspectives: What Makes CS Learning Consequential
Taking a justice-oriented approach to equity in Computer
Science (CS) education, this paper questions the dominant
discourse in CS education and asks what truly makes CS
learning consequential from the perspective of youth. We define
CS learning as consequential by focusing on its transformative
impact on youth identity, agency, and perceptions of the world
within and beyond CS classrooms, regardless of whether or not
they pursue CS in the future. Our research-practice partnership
used qualitative data, specifically longitudinal interview data
with 30 students up to three years after they first experienced a
high school CS class in a large public school district on the west
coast serving majority Latinx, urban, low-income students. Our
findings suggest that in order for CS learning to be meaningful
and consequential for youth, learning must involve: 1) freedom
for youth to express their interests, passions, and concerns; 2)
opportunities for youth to expand their views of CS and self; and
3) teacher care for students, learning community, and subject
matter. The findings have significant implications for the broader
“CS for All” movement and future efforts to reform policy
agendas aiming for a more justice-centered CS education.
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- NSF-PAR ID:
- 10420454
- Date Published:
- Journal Name:
- SIGCSE
- Page Range / eLocation ID:
- 666 to 672
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Efforts to broaden participation in computing address how systemic school structures, educator preparation, and curriculum can provide inclusive learning spaces for all students. The emerging multiplicity of scholarship in computer science (CS) education forwards diverse voices, perspectives, and positionalities, and together, provide a rich set of evidence-based narratives that can transform K-12 policies and practices. The four projects featured in this panel bring together CS education efforts with varying methodologies focused on equity-oriented pedagogies and learning for all youth across the US. This panel will focus not only on sharing the multi-pronged efforts of the featured projects, but also on developing a shared vision among participants and panelists for what equity" can and should be in the future of both SIGCSE and CS education as we celebrate SIGCSE's 50th anniversary. By highlighting the work of projects rather than individuals in this panel, audience members will have the opportunity to learn about how collaborative efforts create and examine contexts for equity in CS education across diverse stakeholders, while also providing a richer base for constructing visions of equity that go beyond mere platitudes, toward action items for broadening participation in computing.more » « less
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Abstract To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making.
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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Abstract We find ourselves at a time when the need for transformation in science education is aligning with opportunity. Significant science education resources, namely the Next Generation Science Standards (NGSS) and the Ambitious Science Teaching (AST) framework, need an intentional aim of centering social justice for minoritized communities and youth as well as practices to enact it. While NGSS and AST provide concrete guidelines to support deep learning, revisions are needed to explicitly promote social justice. In this study, we sought to understand how a commitment to social justice, operationalized through culturally sustaining pedagogy (Paris, Culturally sustaining pedagogies and our futures.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3545945.3569878
