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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Supporting Student System Modelling Practice Through Curriculum and Technology Design
Developing and using models to make sense of phenomena or to design solutions to problems is a key science and engineering practice. Classroom use of technology-based tools can promote the development of students’ modelling practice, systems thinking, and causal reasoning by providing opportunities to develop and use models to explore phenomena. In previous work, we presented four aspects of system modelling that emerged during our development and initial testing of an online system modelling tool. In this study, we provide an in-depth examination and detailed evidence of 10th grade students engaging in those four aspects during a classroom enactment of a system modelling unit. We look at the choices students made when constructing their models, whether they described evidence and reasoning for those choices, and whether they described the behavior of their models in connection with model usefulness in explaining and making predictions about the phenomena of interest. We conclude with a set of recommendations for designing curricular materials that leverage digital tools to facilitate the iterative constructing, using, evaluating, and revising of models.
more » « less- Award ID(s):
- 1842035
- NSF-PAR ID:
- 10307091
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Journal of Science Education and Technology
- Volume:
- 31
- Issue:
- 2
- ISSN:
- 1059-0145
- Page Range / eLocation ID:
- p. 217-231
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract 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.
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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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This paper discusses the potential of two computational modeling approaches in moving students from simple linear causal reasoning to applying more complex aspects of systems thinking (ST) in explanations of scientific phenomena. While linear causal reasoning can help students understand some natural phenomena, it may not be sufficient for understanding more complex issues such as global warming and pandemics, which involve feedback, cyclic patterns, and equilibrium. In contrast, ST has shown promise as an approach for making sense of complex problems. To facilitate ST, computational modeling tools have been developed, but it is not clear to what extent different approaches promote specific aspects of ST and whether scaffolding such thinking should start with supporting students first in linear causal reasoning before moving to more complex causal dimensions. This study compares two computational modeling approaches, static equilibrium and system dynamics modeling, and their potential to engage students in applying ST aspects in their explanations of the evaporative cooling phenomenon. To make such a comparison we analyzed 10th grade chemistry students’ explanations of the phenomenon as they constructed and used both modeling approaches. The findings suggest that using a system dynamics approach prompts more complex reasoning aligning with ST aspects. However, some students remain resistant to the application of ST and continue to favor linear causal explanations with both modeling approaches. This study provides evidence for the potential of using system dynamics models in applying ST. In addition, the results raise questions about whether linear causal reasoning may serve as a scaffold for engaging students in more sophisticated types of reasoning.more » « less
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The Next Generation Science Standards [1] recognized evidence-based argumentation as one of the essential skills for students to develop throughout their science and engineering education. Argumentation focuses students on the need for quality evidence, which helps to develop their deep understanding of content [2]. Argumentation has been studied extensively, both in mathematics and science education but also to some extent in engineering education (see for example [3], [4], [5], [6]). After a thorough search of the literature, we found few studies that have considered how teachers support collective argumentation during engineering learning activities. The purpose of this program of research was to support teachers in viewing argumentation as an important way to promote critical thinking and to provide teachers with tools to implement argumentation in their lessons integrating coding into science, technology, engineering, and mathematics (which we refer to as integrative STEM). We applied a framework developed for secondary mathematics [7] to understand how teachers support collective argumentation in integrative STEM lessons. This framework used Toulmin’s [8] conceptualization of argumentation, which includes three core components of arguments: a claim (or hypothesis) that is based on data (or evidence) accompanied by a warrant (or reasoning) that relates the data to the claim [9], [8]. To adapt the framework, video data were coded using previously established methods for analyzing argumentation [7]. In this paper, we consider how the framework can be applied to an elementary school teacher’s classroom interactions and present examples of how the teacher implements various questioning strategies to facilitate more productive argumentation and deeper student engagement. We aim to understand the nature of the teacher’s support for argumentation—contributions and actions from the teacher that prompt or respond to parts of arguments. In particular, we look at examples of how the teacher supports students to move beyond unstructured tinkering (e.g., trial-and-error) to think logically about coding and develop reasoning for the choices that they make in programming. We also look at the components of arguments that students provide, with and without teacher support. Through the use of the framework, we are able to articulate important aspects of collective argumentation that would otherwise be in the background. The framework gives both eyes to see and language to describe how teachers support collective argumentation in integrative STEM classrooms.more » « less
