2. Background
2.1. Modeling as an Epistemic Practice of Science and in Science Classrooms
Scientists work within the norms of their disciplines that are socially agreed upon by the members of that discipline
[24]. The epistemic nature of scientific practices emphasizes how scientists work within the cultural norms of their disciplines to construct scientific explanations
[25,26][25][26]. Gouvea and Passmore
[27] assert that a shift in K-12 classrooms toward emphasizing epistemic goals is particularly important for the practice of developing and using models. The traditional use of models in science classrooms reflects the known components of scientific phenomena (i.e., models of), such as students imitating the structure of DNA by focusing on its representational nature. Instead, Gouvea and Passmore
[27] suggest a modeling practice in classrooms where there is a focus on learning the functions and relationships of the components of DNA, and in which the model is a tool for relationship building to explore and explain the nature of scientific phenomena (i.e., models for). The focus on the latter (models for), helps students understand the epistemic functions of the models. These epistemic functions help students understand how scientists develop, evaluate, and represent evidence through their models
[28]. Researchers argue that shifts such as these (where students collectively work towards epistemic goals) require an immense amount of instructional support
[29], and teachers need support to learn to scaffold these instructional environments
[30]. Despite these calls for an instructional shift toward epistemic understanding, researchers found that the modeling practice where students actively contribute to the development of the models (models for) has rarely been integrated into science classrooms in elementary and middle schools. Most models continue to be used as illustrative or communicative devices (models of) that do not incorporate students’ contributions
[7].
WResearche
rs therefore chose to build
ourresearchers' module around the use of explanatory models that allow students to integrate their ideas (models for) to support their learning regarding the impact of climate change.
To support teachers and students in developing high quality explanatory models in science classrooms, it is crucial to understand the epistemic norms highlighted in what are characterized as “good models” in scientific disciplines
[31] (p. 486). Philosophers of science found that good models are evidenced-based and have explanatory power, with appropriate details and complexity of ideas that are directly related to the topic studied (e.g.,
[32,33,34,35][32][33][34][35]). Most effective models also communicate a variety of modes of representation, such as pictures, diagrams, and tables (e.g.,
[34,36][34][36]), to sequence and connect ideas
[37] and integrate data
[38].
Learning progression studies on the modeling practice contribute to another important area of research that informs the epistemic aspects of the modeling practice for science classrooms
[39,40,41][39][40][41]. These studies provide a framework that can be used by teachers to engage their students in epistemic considerations. These considerations include how well student models use evidence relevant to the claim, how coherently they explain the related scientific phenomena, how diverse semiotic tools (e.g., written or drawn discourses) are utilized to effectively communicate to audiences, and how a model is revised to improve its explanatory or predictive power. In addition, these studies highlight how models can represent a variety of scientific phenomena that are meaningfully connected for a coherent explanation. Bamberger and Davis’ study
[39] provided a framework where models progressed from static representations of visible components of a phenomenon to mechanistic representations where both the visible and invisible features were used to explain the relationships and processes related to the phenomenon. In the mechanistic models, students also showed comparisons of different situations, such as models of the smell of air fresheners in warmer and cooler temperatures, and specifically labeled representations to clarify abstract components of models, such as labeling the air modules.
For successful mechanistic models, it is important to allow students to use multiple representations (e.g., drawing, writing) in communicating their ideas
[42,43][42][43]. Drawn models have shown advantages in promoting creative ways of expressing scientific ideas and relationships between the ideas. The drawn models can also remove language barriers, resulting in a more equitable and inclusive learning tool
[44]. While written models tend to be more restricted in the ways ideas can be expressed, written information on models with drawings can enhance the clarity of the drawn representations
[45]. To respond to the unique learning demands of individual students, researchers suggest giving students the freedom to select the form of their expression
[44]. Students who can take advantage of using different modes of representation can have the opportunity to show a “deeper understanding” of phenomena
[46] and develop epistemic agency while taking part in shaping knowledge construction on the models
[29].
Despite more than a decade of research on epistemic aspects of modeling practices, teachers and students still hold a limited understanding of this practice. Therefore, they struggle to engage in modeling as an epistemic practice where their initial ideas and observed evidence are iteratively built into a mechanistic model. The student models often lack the representation of abstract components and the connections between ideas to show a coherent explanation
[16,47][16][47].
WResearche
rs propose that an in-depth analysis of student-developed models can provide insight into why teachers and students continue to struggle with attending to the epistemic aspects of modeling in science classrooms.
2.2. Engaging in Models for a Systems Thinking Approach to Climate Change
The climate crisis is one of the top concerns for today’s youth
[48]. The youth are already experiencing the impacts of climate change in their daily lives and are exposed to conflicting claims about the causes and impacts of the changing climate
[49]. While NGSS positioned science classrooms as a space where the youth can learn about climate change, such instruction is limited due to a lack of teacher education and knowledge regarding the complexity of the issue
[21]. Understanding complex climate-related phenomena requires a systems thinking approach that attends to the interconnectedness of the ecological systems and living things
[50]. While there is not a single definition of systems thinking, Senge
[51] defines it as a “framework for seeing interrelationships rather than things, for seeing patterns of change rather than static ‘snapshots’” (p. 68). Adding on to this definition, Davidz and Nightingale
[52] view systems thinking as an approach to analyze, interpret, and understand these interrelationships and patterns, rather than their pieces and parts
[53]. By seeing the world as a whole, systems thinking provides a means to tackle the world’s most complex problems from interdisciplinary and multidisciplinary perspectives
[54], and using a systems thinking approach to solve complex issues in today’s world requires multidisciplinary knowledge. For example, experts in economics, life sciences, bioengineering, sociology, and psychology work together to improve public health
[55]. Similarly, many disciplines and stakeholders are involved in climate adaptation. While climate data collected by scientists over the years are critical in determining climate action, these data sets will only translate to meaningful adaptation plans when human and social systems are considered
[56].
Due to the abstract nature of many system components and interrelationships, qualitative and quantitative modeling are both practices that can help scientists in making systems thinking visible
[57]. While qualitative models may help learners grasp initial concepts
[58] and explain system-wide phenomena
[59], quantitative models can be used to focus on mathematical relationships that predict the behavior of the system
[60]. The framework
[8] and the NGSS
[9] highlight the importance of studying systems and the modeling of these systems as some of the key crosscutting concepts for K-12 science education. The performance expectations of NGSS across K-12 grades draw attention to systems at different scales, e.g., from the circulatory system to the ecosystem, and highlight the importance of scaffolding students as they learn about these systems with their boundaries. The framework
[8] suggests that students show their understanding of a system and raise questions about the system while engaging in the modeling practice. Further, “student-developed models may reveal problems or progress in their conceptions of the system, just as scientists’ models do” (p. 94). Drawing from the framework’s focus on systems modeling, NGSS highlighted some performance expectations that call for designing activities where models are used to explore and explain systems. These performance expectations were implemented across various disciplines of science, including the ones related to Earth Sciences. For example, middle school science (standard 2–6) expects students to engage in modeling to investigate the “unequal heating and rotation of the Earth” and its impacts on “patterns of atmospheric and oceanic circulation that determines regional climates”.
3. Discussions and Future Implications
Building coherent and gapless explanations is crucial for rigorous scientific learning
[68,69][61][62]. Explanatory models can be used as a tool to promote students’ cohesive explanations and provide them with feedback on the construction of these explanations
[15]. The iterative process of explanatory models offers opportunities for epistemic learning, such as supporting or revising ideas with competing evidence. Despite the suggested use of ‘models for’ to support students’ conceptual and epistemic learning, there is a concern that the traditional use of modeling (models of), persists in many classrooms. This limits the role of modeling to visualization and memorization of system components.
In tResearch
is study, we ers looked at how students used explanatory models to construct evidence-based explanations about how ocean acidification affects oyster development. The modeling process was scaffolded through a series of activities that provided evidence for the key ideas needed to build an explanation to answer the driving question. Based on
ourthe findings from 14 classes,
weresearchers rarely saw student models with ‘Extensive’ explanations that attended to all key ideas with evidence from multiple resources. Furthermore, the majority of the student models showed insufficient explanations that were missing key ideas and references to the related evidence. In order to unpack what leads to models with extensive explanations, or their lack thereof,
weresearchers developed a coding scheme to qualitatively look at the conceptual elements and epistemic discourses across all 150 explanatory models.
The following subsections will discuss our findings and the related implications for research and practice.
3.1. Scaffolding Students for Engagement in Epistemic Aspects of Modeling
For several decades, research has suggested that eliciting students’ reasoning and ideas is more effective than the traditional school science approach of knowledge transmission
[70][63]. However, recent studies on modeling, where students actively present their ideas, suggest that many teachers struggle with this approach and are not able to effectively support their students in identifying and connecting ideas to the scientific phenomena that models aim to explain
[16]. In addition to struggling to attend to the specific ideas and explanations that students bring to their models, teachers are also limited in their capacity to provide critical feedback to improve student learning about the scientific phenomena and the modeling practice.
WResearche
rs suggest that the coding framework
wethey developed in this
studyresearch can be used as a feedback tool for teachers to formatively assess and scaffold students’ progress during the modeling practice. As
ourthe framework is aligned with integrated three-dimensional science learning
[8], it directly supports the NGSS-aligned
[9] curriculum design. In developing
ourthe coding framework,
weresearchers first looked at ‘Key Ideas Based on Evidence’ related to the disciplinary core ideas being taught (such as ‘pH and Acidification’ in
ourthe module) along with ‘Discourse Modality of Evidence’ (i.e., how students’ modeling practice presents evidence from their student-driven investigations [e.g., ‘Data Table’, ‘Drawn’]). Second,
weresearchers decided to highlight students’ use of ‘Systems Thinking’ approaches (e.g., connections to the atmosphere, other living organisms) as it is a crosscutting concept that strengthens the explanation of scientific phenomena. Third, to explore the epistemic aspects of the modeling practice,
weresearchers examined how students used ‘Scientific Representations’ that are epistemic discourses specific to scientific disciplines in the form of language or symbols
[71][64]. Specifically,
wresearche
rs looked at how students used scientific representations, grounded in specialized epistemic discourses, such as arrows, to show processes or symbols for chemical molecules.
WeResearchers suggest that teachers or teacher educators can use these categories as a guide to developing a framework to support students on the iterative revisions of models that seek to explain other phenomena. Using this framework, teachers can follow students’ evolving understanding of the key ideas and identify the related evidence students use in developing extensively coherent explanations of the phenomena being studied. In addition, focusing on how students make decisions around which ‘Scientific Representations’ they use can provide opportunities for classroom discussions on epistemic cultures of scientific disciplines that develop norms around when and how to implement these scientific representations.
In scaffolding the development of conceptual understanding of key ideas in students, it may be important to consider the types of activities in which students are engaged. Key ideas that were integrated into explanatory models are most frequently related to activities where students are socially engaged in practices of science. For example, in the activity that focused on the key idea of ‘pH and Acidification’, students designed their own protocols to answer the investigative question while making decisions as a group. This activity also involved students analyzing and interpreting data in groups or pairs. Key ideas that mostly relied on readings and videos as major sources for the activity, such as ‘Oysters Filter Water,’ were present less often in explanatory models and appeared in fewer instances.
3.2. Diverse Discourse Modes for Building Cohesive Models
The findings from this
res
tudyearch contribute to the literature on the diversity of discourse modalities students choose to communicate their ideas. Previous research highlighted the richness of students’ use of multiple literacies that are critical for learning and participating in science
[72][65]. Scholars suggest that the use of both written and drawn modalities improves deeper thinking, epistemic agency, and clarity of explanations. These scholars also highlight that written text can help improve the clarity of drawn models and provide a sequential logic to scientific processes. Further, models that allow multiple representations can be a more equitable and inclusive assessment tool for science classrooms
[14] as they provide space for students who might be challenged with one of the modalities. For example, writing in a logical and sequential manner in models can be demanding for emergent bilinguals.
In our sStud
y, students were encouraged to use a variety of discourse modes in building their explanatory model.
OurThe findings showed that most models were a combination of written and drawn modalities. It is important to note that both models with mostly ‘Drawn’ components and models with mostly ‘Written’ components can achieve extensive explanations. As can be seen in the examples provided in the findings, the textual information is most often used to name drawn images, explain processes, provide a sequence to events, and link different key ideas.
Beyond written text and drawing,
wresearche
rs noticed that some students used another modality, ‘Data Tables’ that included data from their own investigations. In some cases, students complemented data tables with textual explanations and visual drawings. In the Ocean Acidification and Oysters module, students collected two sets of data: (1) during the student-designed investigation during the ‘Carbon Dioxide and pH’ activity and (2) during the ‘Carbonate Challenge’ activity.
WeResearchers noticed that this use of a ‘Data Table’ as a modality was rare among the modalities chosen by students. While the role of data is significant in computational models,
weresearchers see that explanatory models can be a tool to help students integrate what they learn from their data. Future studies can explore meaningful integrations and representations of data as a discourse modality in explanatory models. Data representations can help strengthen the evidence that supports the key ideas in the models.
3.3. Systems Thinking beyond Oceans and Oysters
OurThe study tackles the process of learning about a critical climate change impact, ocean acidification. According to scientists, unprecedented changes in ocean acidification are related to past mass extinctions. The evidence is alarming scientists who claim recent changes in the oceans can lead to decreased populations of shellfish and then impact the food chain in the marine ecosystem
[73][66]. Due to Earth’s complex environmental system, understanding this phenomenon requires focusing on a systems thinking approach. During professional learning with teachers,
wresearche
rs encouraged teachers to make a connection between the atmospheric changes and changes in the oceans. Specifically,
our the researchers' goal was to support an understanding of how changing levels of CO
2 in the atmosphere lead to changing CO
2 levels and related changes in acidification levels in the oceans.
OuThe researcher
s' intention was to support students in learning about the impact on oyster development.
OurThe videos and readings showed limited connections to how other marine organisms and humans can be affected by the changing oceans and their impact on the food chain.
Despite the limited representation of a variety of species and their links to human ecologies,
wresearche
rs saw that many student models communicated a decrease in the number and diversity of marine species beyond oysters. For instance, they drew or wrote about fish, algae, and clams. Some models depicted humans with reduced access to food sources and living in environments where there is less green space or clean water. Although
ourresearchers' materials did not highlight the impact on land animals and plants,
weresearchers noticed that some students did attend to multispecies impacts and their relation to humans in their models. This finding reminds us that youth perceptions of climate injustice can move beyond the traditional anthropocentric view that exceptionally centers humans. Recent research on climate change emphasizes that these human-centric views of climate justice are no longer enough to attend to the climate emergency the world is witnessing today (e.g.,
[74][67]). These scholars therefore suggest a multispecies lens regarding climate justice that will acknowledge the past and future destruction of living and non-living things (e.g., animals, rivers) and consider the importance of the relationships
wresearche
rs all require to thrive and overcome environmental grand challenges
[75][68]. Inspired by this observation,
wresearche
rs suggest that future research on modeling climate change impacts with a systems thinking approach consider a multispecies lens for a more inclusive understanding of the impacts of changing climate.