This fundamental research in pre-college education engineering study investigates the ways in which elementary school students and their teacher balance the tradeoffs in engineering design. STEM education reforms promote the engagement of K-12 students in the epistemic practices of disciplinary experts to teach content.1,2,3 This emphasis on practices is a paradigm shift that requires both extensive professional development and research to learn about the ways in which students and teacher learn about and participate in these practices.
Balancing tradeoffs is an important practice in engineering but most often in classroom curricula it is embedded in the concept of iteration1,4; however, improving a design is not always the same as balancing trade-offs.1 Optimizing a multivariate problem requires students to engage in a number of engineering practices, like considering multiple solution, making tradeoffs between criteria and constraints, applying math and science knowledge to problem solving, constructing models, making evidence-based decisions, and assessing the implications of solutions5. The ways in which teachers and students collectively balance these tradeoffs in a design has been understudied1.
Our primary research questions are, “How do teachers and students make decisions about making tradeoffs between criteria and constraints” and “How do experiences in teacher workshops affect the ways they implement engineering projects in their classes.” We take an ethnographic perspective to investigate these phenomena, and collected video data, field notes, student journals, and semi-structured interviews of eight elementary teachers in a workshop and similar data from two of the workshop teachers’ classes as they implemented the curriculum they learned in the workshop. Our analyses focus on the disciplinary practices teachers and students use to make decisions for balancing tradeoffs, how they are supported (or impeded) by teachers, and how they justify these decisions. Similarly, we compared two of the teachers wearing their “student hat” in the workshop as well as their “teacher hat” in the classroom5.
Our analyses suggest three significant findings. First, teachers and students tended to focus on one criterion (e.g. cost, performance) and had few discussions about trying to minimize cost and maximize performance. Second, curriculum design significantly impacts the choices students make. Using two examples, we will show the impact of weighting criteria differently on the design strategies teachers and students make. Last, we noted most of the feedback given was related to managing classroom activity rather than supporting students’ designs. Implications of this study are relevant to both engineering educators and engineering curriculum developers.
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Epistemic tools in engineering design for K‐12 education
Engineering design provides unique ways to include epistemic tools to support collaborative sense‐making, reasoning with evidence, and assessing knowledge. Engineering design processes often require students to apply science concepts to solve problems. We draw from five engineering curricular units that engaged students in specific epistemic practices of engineering: constructing models and prototypes, making trade‐offs between criteria and constraints, and communicating through uses of conventionalized verbal, written, and symbolic models. Through analysis of curriculum products, student artifacts, and classroom discourse, we show how engaging in such practices requires the use of epistemic tools that shape, and are shaped by, the knowledge construction work of the members of the classrooms. The epistemic tools foster creating, sharing, and assessing knowledge claims. Six principles of practice for education demonstrate how such tools can be educative. These principles evince how epistemic tools support goal‐directed, concerted activity that can support the learning of disciplinary knowledge and practice and offer the potential to increase student agency.
more » « less- NSF-PAR ID:
- 10089069
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Science Education
- Volume:
- 103
- Issue:
- 4
- ISSN:
- 0036-8326
- Page Range / eLocation ID:
- p. 1080-1111
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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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).
