Participants were given the initial opportunity to develop perceptual inferences related to the greenhouse effect by describing simulated situations. Each participant began by noting various elements of the simulation (e.g., the sun, ground, labels, interactive elements) and realized quickly that the simulation was intended to model the Earth’s atmosphere. After the “play” button was clicked, participants made perceptual inferences, were guided to attend to various aspects of the simulation, formulated causal rules and rearticulated their understanding of the greenhouse effect in terms of those rules and perceptions. In what follows, we compare participants’ articulated models of the greenhouse effect before and after the intervention and then present a case study that illustrates reasoning processes that facilitated these changes. While the pedagogical effectiveness of this approach cannot be determined through this study, we find reason for cautious optimism.
3.1. Pre-Post Comparisons
To answer the first research question—Would participants’ models of the greenhouse effect change as a result of engaging with an interactive visualization?
—we compared frequencies of codes related to participants’ explanations and predictions for how climate change works (i.e., the greenhouse effect) before and after the intervention (see Table 1
), we also present qualitative evidence to show that conceptual change occurred.
More than half (11 of 20) of the participants initially exhibited a misconception about the greenhouse effect. Of them, nine conflated ozone depletion with the greenhouse effect and six mentioned visible “pollution” or “smog” as the main contributor to climate change (four of these participants incorporated both ozone and pollution into a description of their model). These are well documented climate change misconceptions [58
]. There was no evidence that participants in our sample believed the “solar radiation hypothesis,” or the “natural variation hypothesis,” also common misconceptions documented in the literature [7
]. After the interview, only 2 participants exhibited a misconception of the 11 that initially held a misconception (both confused ozone depletion with the greenhouse effect) suggesting the intervention was successful in reducing misconceptions. A test for equality in proportions revealed significant differences in mention of ozone depletion (χ2
= 20) = 6, p
= 0.014) and visible smog or pollution (χ2
= 20) = 6.9, p
= 0.009) between statements made before and after the intervention.
We also found general improvement in the mention of normative mechanisms involved in the greenhouse effect and a reduction in misconceptions. Participants initially referenced normative mechanisms in their description of how climate change works. They mentioned CO2 (10 of 20), other greenhouse gasses (8 of 20), increases in temperature (14 of 20), human involvement (10 of 20) and suggested that heat is somehow “trapped” our atmosphere (10 of 20). No participants mentioned processes of sunlight absorption/emission or infrared radiation in their initial model.
After engaging with the simulation, participants tended to use more normative language when re-describing how climate change works. At the end of the interview, they mentioned CO2 (15 of 20), other greenhouse gasses (14 of 20), increases in temperature (16 of 20), human contributions to climate change (5 of 20), “trapping” of heat (16 of 20), infrared radiation (20 of 20) and processes of sunlight transforming to infrared (17 of 20). A test for equality in proportions found significant differences in mention of greenhouse gasses other than CO2 (χ2 (1, N = 20) = 4.8, p = 0.027), the trapping of heat in the atmosphere (χ2 (1, N = 20) = 3.9, p = 0.05), infrared radiation (χ2 (1, N = 20) = 39, p < 0.001) and sunlight transformed into infrared (χ2 (1, N = 20) = 29, p < 0.001) between statements made before and after the intervention. Notably, all participants mentioned infrared radiation after the intervention compared to two participants before. However, also notable is the decrease in participants’ mentions of human involvement in their description of how climate change works.
Qualitative evidence from interviews also support our claim that conceptual change occurred. When students were asked to reflect on whether anything from the interview “changed their mind,” one student who initially held a misconception about visible pollution said that she “changed [her] mind about how the process of global warming… how the Earth absorbs visible light and gives off infrared and how CO2 keeps increasing and it’s affecting the temperature and things like that.” Statements of this sort suggest that students changed their misconceptions about the greenhouse effect and were also aware of this change.
However, not all students reported changing misconceptions about climate change given that nine students initially held no misconceptions. For many of these students, changes in students’ conceptions of the greenhouse effect were evidenced by greater facility with scientifically normative language. For example, when a student who did not initially hold a misconception was asked to report whether anything from the interview changed her mind, she said “I was like 85% right” about how the greenhouse effect works. When probed further she said, “I guess like, because I always see the diagram of the sunlight coming off and bouncing back, [but my instructors] never explained that the Earth is the one that produces the infrared rays. That was new. I didn’t know that greenhouse gases absorb them, I just thought they reacted, I didn’t really know which way.” Another student said “I mean, I already knew that global warming exists. I guess some of the basic concepts of global warming were refreshed... Now I have more information. And if someone would ask me how to explain it, I’m sure I would be able to explain it in much more detail than I would have been before.” These excerpts further support what we found in the quantitative evidence: students generally used more scientifically correct vocabulary and alluded to scientific processes when articulating the greenhouse effect, even for those who did not initially hold a misconception.
3.2. Case Study
To answer the second research question—What role did participants’ perceptual inferences of a visualization play in their conceptual change process?—we traced the development of one of the 20 participant’s evolving conception of the greenhouse effect as he engaged with the simulation. Daniel (a pseudonym), was chosen to highlight dimensions of participant thinking that are characteristic of many of the participants interviewed. Namely, we highlight instances where Daniel exhibited a misconception, made perceptual inferences, was guided to attend to details that he did not attend to previously (i.e., temperature, CO2 and infrared radiation), formulated rules and fit these rules together when finally articulating his model of the greenhouse effect. We conclude with an evaluation of the design framework in light of its pedagogical affordances and constraints for science teachers.
3.2.1. Daniel’s Persistent Misconception
Daniel was a 23-year-old junior that identified as Latino. He believed that climate change is real and expressed a sense of urgency to take action, yet was not confident in his understanding of how it works. Despite his good intentions and concern for the wellbeing of the planet, stating that “...our country should be doing a better job of sustaining the environment to stop global warming and to prevent climate change,” he held a common misconception. When prompted to explain how climate change works, Daniel made the following statement:
- I don’t know a whole lot about it. I just know that pollutants in the air kind of erupt, I don’t think it’s the right word but break down the ozone layer that covers our atmosphere and protects us from the harmful rays of the sun being too much. The more that that breaks down, the sun’s able to just, the planet is overheating from the energy from the sun that is able to pass through the atmosphere more clearly without having that protective layer that sustains life on the earth. The more that that breaks down and our planet overheats, eventually over time it could be not sustainable to life.
Like a majority of participants in the sample, Daniel held a misconception regarding the primary mechanisms of climate change (i.e., the greenhouse effect). Namely, he exhibited two widely held misconceptions that global temperature gains are due to (a) general pollution (not primarily CO2
) and (b) the depletion of ozone which allows UV light to enter the atmosphere and warm the planet [5
]. Yet, unlike some participants who demonstrated only a surface-level misconception (e.g., using the word ozone instead of the word CO2
, saying “ozone traps heat”), Daniel held a deeper misconception, evidenced by his fairly coherent description of atmospheric temperature changes via UV light entering the atmosphere through ozone holes. Daniel’s prior conception shaped how he initially viewed the simulation.
When initially shown the simulation in static state, the interviewer asked Daniel, “What do you see here?” Daniel replied, “The sun. And these look like particles [referring to particles on the ground]. I don’t know what they’re supposed to simulate.” Upon clicking the play button and asked to describe what he saw, he replied with a description that utilized his initial ozone misconception.
- Okay, what do you see?
- UV rays from the sun are coming down hitting the ground, exciting the ground and making it warmer…The more that keeps happening, the more kinetic energy, the more excited the particles are and then the hotter the ground gets and the temperature rises.
Daniel described what he saw on the screen and formulated the intuitive rule: “more motion, more temperature” [49
]. Despite this normative inference, his use of the term “UV” also suggests that his initial conception involving ozone and ultraviolet light shaped how he viewed and named the objects in the simulation. In this situation, the interviewer made notice of this confusion and guided him to attend to a legend on the screen with labels for the various representations that appear on the screen. Daniel then initiated a brief conversation with the interviewer about the differences between UV and sunlight, then continued to summarize what he saw on the screen without mention of UV.
- Sunlight is traveling in waves to the ground, exciting the particles on the ground, which is causing them to have an increase in kinetic energy and give off heat as a result, so the temperature of the ground and the earth is rising.
With guidance, Daniel attended to visual details that allowed him to prioritize and anchor important visual cues that he periodically referred to when re-articulating his model of the greenhouse effect. In this case, the visual cues that helped Daniel to correct his use of the term “UV” were the labels on the screen highlighting important terms that were showcased in the animation.
3.2.2. Perceptual Inferences Become Causal Rules
As the interview progressed, Daniel made inferences based on what he saw in the simulation which subsequently led him to revise his existing model. Sometimes these model revisions occurred rapidly with one quick observation and other times they occurred iteratively over multiple observations and revisions. For example, during the second stage of the simulation Daniel initially incorrectly predicted that “sunlight will excite CO2
molecules,” but quickly revised this hypothesis after observing simulated sunlight pass through CO2
. He seemed to effortlessly correct his initial hypothesis, noting that sunlight “...travel[s] through the atmosphere and CO2
...There’s really not much of a reaction between them.” Other times, however, the inferences were not quick and effortless. For example, as he progressed through the third stage of the simulation, he was prompted to make a prediction regarding how infrared rays might interact with CO2
. The following dialogue highlights the dynamic interplay between perceptual inferences, cognitive conflict, rule formulation and model revision that was typical in most interviews. The following segment occurred immediately after Daniel inferred that infrared rays “crash into” the CO2
, as he changes his mind, then pauses multiple times to observe what is happening in the simulation.
- You said that you noticed that the infrared was interacting with the CO2 this time?
- Actually, no. I changed my mind. I don’t think the infrared is interacting with the CO2. [pause] Actually, maybe it is. [pause] I think when the infrared rays are passing through [the CO2] it’s exciting them somehow, so yeah I guess it is interacting with them. I don’t know if it’s necessarily interacting with them or if it’s just the presence of the infrared rays is causing the CO2 molecules to move quickly but they’re interacting with each other, not with the infrared, if that makes sense. It’s like having it in the atmosphere is causing them to interact with each other.
- [Prompts Daniel to put the visualization in slow motion and attend to the “glowing” of CO2] What do you notice this time?
- When they’re glowing, they’re sucking up the infrared rays. When they’re not glowing they’re exerting the infrared rays.
Here, we see Daniel generating and weighing the plausibility of multiple rules to explain what he sees in the simulation. He had initially noted that infrared crashes into CO2 but after making a closer observation, changed his mind and formulated a rule: “the mere presence of infrared is exciting the CO2 particles.” Then, with guidance, he attended to the glowing of the CO2 particles and searched for a reason for why they were glowing. He therefore changed his mind and asserted a new rule: “Infrared is ‘sucked up’ by the CO2 and later ‘exerted’ again” to account for these new observations. Through this feedback loop between observation, rule generation, model revision and instructor guidance, he quickly rotated through a number of hypotheses and eventually arrived at what he perceived to be the most plausible explanation: that CO2 absorbs and re-emits infrared and gains some energy in the process. Daniel restructured his mental model of the greenhouse effect as he made perceptual inferences from the visualization and recursively revised his causal hypotheses.
3.2.3. Synthesis of Causal Rules
As Daniel progressed through the intervention, he formulated and revised multiple rules to explain what he saw in the simulation. As he progressed, he inferred that (a) sunlight passed freely through CO2
, (b) sunlight was absorbed by the ground and was partially re-emitted as infrared, (c) infrared was ‘sucked up’ and later ‘exerted’ by CO2
and (d) all particles gained kinetic energy (and therefore temperature) in the process. When prompted to make a prediction for what would happen on the final screen (“Sun on Ground and CO2
”), he synthesized the rules mentioned above to predict how the sun and the ground might interact in the presence of atmospheric CO2
- I think that sunlight will travel down in rays, hit the ground, excite the ground, the ground will release infrared into the atmosphere. The infrared will be absorbed by the CO2 molecules, exciting the CO2 molecules, causing there to be more kinetic energy in the atmosphere which rises the temperature. As these CO2 molecules absorb the infrared lights and get more energy, they’ll then release them back aimlessly in different directions. Yeah.
Here we see a coherent integration of his previous inferences in a mechanistic model involving normative mechanisms of the greenhouse effect. The scaffolded nature of this particular simulation enabled Daniel and other participants to discretely establish targeted scientific hypotheses in sequence, building up to a moment where they are able to synthesize the pieces into a coherent (although not always accurate) story. Compared to his initial description, in this final description of the greenhouse effect he clearly mentions infrared, CO2
and processes of absorption and emission that lead to an overall increase in temperature. Moreover, he no longer mentions ozone depletion and later—after viewing an instructional video—comments on his change of mind:
- I was just like, “Oh, the ozone layer is breaking down, thus the sun has more of an impact on the earth.” I don’t know, that’s not necessarily the case. It is but it’s more so the methane, carbon dioxide and greenhouse gases in the atmosphere that are interacting with infrared that are causing the actual increase in temperature, which scientifically and physically makes sense.
Above we see Daniel coming to articulate an increasingly skilful and more accurate description of the greenhouse effect. This build-up occurred as Daniel made perceptual inferences, was guided by the interviewer to make predictions and attend to relevant details present in the simulation and iteratively formulated and revised intuitive rules and explanations for what he saw. This culminated in a moment of synthesis where Daniel re-articulated his model of the greenhouse effect in terms of the perceptual inferences and intuitive rules that he derived throughout the intervention.
Like many participants in the sample, Daniel accepted that climate change was happening but walked into the interview with several misconceptions about how it works. As he engaged with the simulation, he anchored his descriptions of climate change in the physical/visual representations and actions that he viewed in the simulation, then gradually revised his model as he observed new relationships therein. Daniel’s case illustrates how this grounded learning experience offered opportunities for participants to rearticulate their model of the greenhouse effect in terms of their visual experiences, eventually leading to more normative, episodic descriptions of the greenhouse effect with substantially more scientific accuracy than their original conceptions. This process involved an iterative interplay between observations and model revision that culminated in explanations that Daniel found increasingly more plausible. Daniel can be said to have repeatedly abducted causal rules to speak scientifically about multiple relationships that he observed in the visualization [59