1. http://www.transitionburnie.com.au/ category/climate-change/
Poster 2: Curriculum Materials Supporting 3-dimensional Learning about the Global Carbon Cycle
Joyce Parker1, Craig Kohn2, Beth Covitt3, May Lee2, and Charles Anderson2
1Department of Earth and Environmental Sciences, 2Department of Teacher Education, Michigan State University
3spectrUM Discovery Area, University of Montana
Overview
To understand global climate change, students need to be able to interpret representations of atmospheric CO2 levels (Keeling curve) and pool-and-flux models of the global carbon cycle (NOAA diagram).
Keeling curve NOAA diagram
However, we found that many students tended to conflate changes in flux (e.g., reduction in fossil fuel use) with size of the carbon pool.
We used diagnostic questions (MAP SCENARIOS, HALF FF, KLG EXPLANATION, KLG PATTERNS, DENIER ARGUMENT) to assess students’ responses to curricular materials, asking them to explain their mappings between the Keeling curve (data representation), NOAA diagram (diagrammatic model), and carbon-transforming phenomena.
Our research questions are: How do students map the pool-and-flux carbon cycle model and atmospheric carbon dioxide data representation to carbon transforming phenomena? How do they use those mappings to predict effects of disturbances or changes in carbon transforming phenomena?
Methods
We interviewed 27 undergraduate students, all non-science majors.
Interview protocols included diagnostic questions asked before and after each curricular materials, which were developed to scaffold students’ difficulties differentiating between pools and fluxes.
Responses were coded using our learning progression framework.
Curricular Materials
Framework and Findings
FALSE COLOR ANIMATION
Few students see the atmosphere as continuous. Most predict CO2 data for different locations by noting similarities between locations.
To address connectivity of atmosphere, this animation demonstrates changes in global carbon levels for one year.
After viewing this animation, about two-thirds of the students said that Hawaii and Michigan appeared to have similar levels of atmospheric carbon dioxide. Some students described the atmosphere as being “global.”
Phenomena → Model
Model → Phenomena
Data Representation → Phenomena
Model → Data Representation
Uses model to explain phenomena
Associates phenomena with model components
Interpret data
Identify trends
Uses model to explain and
predict changes in data
Level 4
Maps phenomena to
appropriate pools and fluxes.
Associates pools with appropriate carbon-containing substances. Accurately associates fluxes with processes.
Accurate quantitative
descriptions of
short-term variations
and long-term trends.
Uses net fluxes from model to explain graph
or make predictions.
Level 3
Incomplete or
inaccurate mapping
of phenomena to model.
Makes mistakes associating pools with carbon-containing substance, in addition to associating fluxes with processes.
Indicates awareness of
full geographic area
represented by data.
Accurate qualitative
description of annual cycle
or long-term trend.
Uses fluxes to give a qualitative or
incomplete quantitative explanation
of graph or make predictions.
Level 2
Identifies pools and fluxes,
but explanations for mappings
are independent of model
(narrative, often force dynamic or covering law).
Associations between model and phenomena are based on pictures, labels from model, or are inappropriate references (e.g., water cycle).
Determines generalizability
of data by reasoning about
similarities and differences
among locations.
Uses vague language that may describe reasonable patterns,
but could be misinterpreted. Description is incomplete or contains mistakes.
Uses model arrows without
associated meaning or incorrect fluxes
to explain graph or make predictions.
Level 1
Does not reference model.
Fails to associate model with phenomena.
Interprets data as either local or global without explicit reasoning about generalizability.
Fails to identify (accurate) trend.
Sees graph and model as unrelated due to
differences in scale or subject.
Diagnostic Questions
MAP SCENARIOS
IDENTIFY POOLS / IDENTIFY FLUXES
HAWAII vs. MICHIGAN
KLG PATTERNS
KLG EXPLANATIONS
DENIER ARGUMENT
Which pools and fluxes
explain what happens when: tree grows, pizza is delivered, incandescent bulbs are replaced with LEDs, nuclear plants replace coal plants, leaves compost, biofuels replace gasoline, field turns into town?
What carbon-containing substances would you find in each carbon pool?
What process is represented by each arrow?
Does the data (Keeling curve) tell us anything
about carbon dioxide concentrations in Michigan?
Describe a pattern you see in the graph (Keeling curve).
What do you think caused that pattern
(in Keeling curve)?
Some people argue we don’t need to worry about our fossil fuel use since carbon flux from fossil fuels into the atmosphere is small (6 gt/yr) compared to fluxes from the ocean (88gt/yr) and biosphere (119 gt/yr). Do you agree?
Sample Student Responses
ASHLEY POST 1-TURTLE SIMULATION
I: Okay. So why don’t we go back to that NOAA chart. So does that change any of your answers or responses to this? So, for example, we had the argument, well this is small because — and it doesn’t matter because these are so much bigger.
A: I guess it wouldn’t really matter. Like the numbers wouldn’t really matter because, as long as there’s as much going in and going out, it’s the same, then it doesn’t really matter.
Level 3: qualitative response using pool-and-flux model
OLIVIA PRE FALSE COLOR ANIMATION
I: Do you think that this graph about carbon dioxide concentration in Hawaii could tell us anything about the carbon dioxide concentration in Michigan? Looking at this, could this help us talk about carbon dioxide here?
O: Not specifically, because the climate is so different in Hawaii, and because the climate is different, people probably live a little bit different throughout the year. It could say something about the world in general, but maybe not as much Michigan, specifically. If we wanted to know about Michigan, we should do something in Michigan.
Level 2: determines generalizability of data by reasoning about similarities and differences between locations
OLIVIA POST FALSE COLOR ANIMATION
I: Thinking back, you had mentioned, for this annual cycle, you weren’t quite sure if it would apply to Michigan or not. Do you still think that, or do you think that this annual cycle maybe you could apply it, it might be happening in other places? 
O: Yes, I think, definitely, it would apply to all the places, after seeing that. I think I thought t was more localized than it actually is.
Level 3: demonstrates awareness of full geographic area represented by data
ASHLEY PRE 1-TURTLE SIMULATION
I: Okay. All right. So one of the arguments is that this — so you mentioned this like one-way arrow, but one of the arguments is that this doesn’t matter because the 6.3 arrow is so much smaller than 88 or 119. Would you agree or disagree with that?  
A: I would kind of disagree, kind of, because I think fossil fuels have a bigger impact than what’s coming out of the ocean or vegetation and soils.
I: Why would you say that?
A: I just feel like they would have more of an impact with the CO2 released into the atmosphere.
Level 1: not using pool-and-flux model, speculating about size of impact
Carbon-Transforming
Phenomena
Pool-and-Flux Carbon Cycle
Model
(NOAA diagram)
Atmospheric CO2
Data Representation
(Keeling curve)
Use model to explain
phenomena
(MAP SCENARIOS,
HALF FF)
Map phenomena onto model
(IDENTIFY POOLS/ IDENTIFY FLUXES,
Use model to explain and predict changes in CO2 levels
(KLG EXPLANATION, DENIER ARGUMENT, HALF FF)
Interpret data/ identify trends
(KLG PATTERN,
1-2-3 TURTLE (BOARD GAME and SIMULATION)
When explaining or predicting phenomena, most students: (1) cite natural/unnatural or good/bad factors, (2) conflate changes in flux with changes in pool size, or (3) reason using only one flux.
This game/simulation lets students control flux rates between inorganic carbon in atmosphere and organic carbon in biosphere to explore their effects on pool size.
After using the simulation, all students responded that opposing fluxes of the same magnitude does not lead to changes in pool size, improving their explanations and predictions by at least one level.
However, their explanations and predictions of changes caused by disturbances in carbon transforming processes generally did not improve.
HALF FF:
Suppose the world miraculously cut their use of fossil fuels in half and held it at that level.
Can you predict (draw on Keeling graph) what would happen to the atmospheric carbon dioxide levels? Explain your reasoning.
Conclusions
Initially none of the high school students (previous studies) and undergraduate students were completely successful in connecting global data, models, and phenomena. Most of them could be located at Learning Progression Level 2, with a small number located at Level 3.
During the interview, curricular materials that combined coaching with additional representations improved student understanding, but not all curricular materials were equally successful.
We are testing these and other curricular materials in the Carbon TIME Human Energy Systems unit.
Carbon: Transformations in Matter and Energy
This research is supported by grants from the National Science Foundation: A Learning Progression-based System for Promoting Understanding of Carbon-transforming Processes (DRL ), and Sustaining Responsive and Rigorous Teaching Based on Carbon TIME (NSF ). Additional support comes from the Great Lakes Bioenergy Research Center (DOE Office of Science BER DE-‐FC02-‐07ER64494), funded by the United States Department of Energy, from Place-based Opportunities for Sustainable Outcomes and High-hopes, funded by the United States Department of Agriculture. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation, the United States Department of Energy, or the United States Department of Agriculture.