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## Best ways to introduce students to models

What are the best ways to introduce students to models and help them overcome resistance or disinterest? How do we keep them from being overwhelmed?

A good teacher knows his/her students and their capabilities. Physical, hands-on models are a good place to start. The entire concept of a model is something that they don't understand, so you need to be creative. Models, at best, simulate reality. I want students to understand that models based on physical processes, using tested equations, are our best approaches toward making predictions.

My concern is that climate models get complex very quickly. Still, at the core of every model, including GCM's, are a few basic equations. I think students should not be given a climate model until they understand those basic equations. This may limit the use of these models, at least as far as I am concerned, to more advanced classes.

I guess that I am struggling with complex climate models being offered up as "black boxes", as V. Ramanathan once lamented, without the student really knowing what is going on inside. However, Bob Mackay's approach of using those feedback loop diagrams could offer a solution. Building a model, one part at a time, with students developing a conceptual model of what each component is doing, offers me some hope.

However, I also don't want to teach my entire course on this one topic, so how do I do all of this in a few weeks of instruction?

I probably didn't answer the question posed here, but students are going to resist using something they don't understand, and will not have any faith in the predictive powers of the model until they are comfortable with it and embrace it.

My concern is that climate models get complex very quickly. Still, at the core of every model, including GCM's, are a few basic equations. I think students should not be given a climate model until they understand those basic equations. This may limit the use of these models, at least as far as I am concerned, to more advanced classes.

I guess that I am struggling with complex climate models being offered up as "black boxes", as V. Ramanathan once lamented, without the student really knowing what is going on inside. However, Bob Mackay's approach of using those feedback loop diagrams could offer a solution. Building a model, one part at a time, with students developing a conceptual model of what each component is doing, offers me some hope.

However, I also don't want to teach my entire course on this one topic, so how do I do all of this in a few weeks of instruction?

I probably didn't answer the question posed here, but students are going to resist using something they don't understand, and will not have any faith in the predictive powers of the model until they are comfortable with it and embrace it.

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I agree, just having students use climate models as "black boxes" would be inappropriate and would not benefit them. However, I do think that models can be used in lower level undergraduate classes effectively. Of course, they need to be set up properly by introducing the fundamental principles and assumptions. I found in past classes that students do appreciate their "predictive powers" without being able to understand all the basic equations (as long as they understand the general concepts of numerical model).

To answer the question: I am not deriving a lot of equations in class (except for the planetary energy balance and the importance of the atmosphere) and students are actually excited about using the models. The resistance is when higher math is involved.

To answer the question: I am not deriving a lot of equations in class (except for the planetary energy balance and the importance of the atmosphere) and students are actually excited about using the models. The resistance is when higher math is involved.

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Overall, I think models can be introduced in any level course, and as Mark says, the teacher knows her/his students best so I am trying to consider major simplifications for my own students. Obviously these simplifications won't be necessary for graduate level courses, or in upper level undergrad courses with students who have a strong math background. I am not in either of those situations, so I'm toying with some ideas to keep my students from becoming overwhelmed . . .

I agree that students, at least my students, would be more resistant to learning about models the more math that is involved. I also agree that it's best for students to understand the math that goes into these models, but that is not something that I will achieve with most of my students so I am considering ways around the complicated math for now. The level of detail about the models will obviously be tied to the lesson goals – with my introductory students, I would be more likely to introduce model outputs and have them reflect on what those tell us. My science majors (most of them) would be able to consider some of the math, but it's more important for my course goals that they understand the processes that go into the model (e.g., feedbacks) rather than the math behind it. (I personally love math, but my students do not share my passion for it.) The interactive model that Bob MacKay uses would keep my students interested – it seems that most students become engaged when we do computer-based labs, and this would certainly fit. But I can also see them becoming overwhelmed with the model at first, so I might first introduce students to the "very, very simple climate model", which I have used before – this one has students input a CO2 emission rate and then step forward to 2100. It's very basic but a good place to start because it would allow me to gauge whether the students can interpret this basic output based on what they put in. I would like to use the MacKay model in my majors Climatology course, and the way he has students make predictions prior to running each model, then testing their predictions, would likely keep them engaged. If students change one parameter at a time, that should keep them from getting too overwhelmed. I think models lend themselves to hands-on activities and inquiry-based lessons, but I envision having my students complete these activities with heavy teacher involvement so that frustration levels may be monitored during the lesson. But I do think that allowing students to interact with a computer based model is a good way to keep their interest and give them an understanding of how complicated models can quickly become – but that they still provide useful outputs that students themselves are able to interpret.

I agree that students, at least my students, would be more resistant to learning about models the more math that is involved. I also agree that it's best for students to understand the math that goes into these models, but that is not something that I will achieve with most of my students so I am considering ways around the complicated math for now. The level of detail about the models will obviously be tied to the lesson goals – with my introductory students, I would be more likely to introduce model outputs and have them reflect on what those tell us. My science majors (most of them) would be able to consider some of the math, but it's more important for my course goals that they understand the processes that go into the model (e.g., feedbacks) rather than the math behind it. (I personally love math, but my students do not share my passion for it.) The interactive model that Bob MacKay uses would keep my students interested – it seems that most students become engaged when we do computer-based labs, and this would certainly fit. But I can also see them becoming overwhelmed with the model at first, so I might first introduce students to the "very, very simple climate model", which I have used before – this one has students input a CO2 emission rate and then step forward to 2100. It's very basic but a good place to start because it would allow me to gauge whether the students can interpret this basic output based on what they put in. I would like to use the MacKay model in my majors Climatology course, and the way he has students make predictions prior to running each model, then testing their predictions, would likely keep them engaged. If students change one parameter at a time, that should keep them from getting too overwhelmed. I think models lend themselves to hands-on activities and inquiry-based lessons, but I envision having my students complete these activities with heavy teacher involvement so that frustration levels may be monitored during the lesson. But I do think that allowing students to interact with a computer based model is a good way to keep their interest and give them an understanding of how complicated models can quickly become – but that they still provide useful outputs that students themselves are able to interpret.

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...what about using a conceptual activity before any student even looks at a formal climate model? as in intro geo labs, when students are asked to group rocks or minerals and explain their groupings--before they are introduced to any formal classification scheme. Does anyone have a good warm up activity that just sensitizes students to the components of any climate system and starts to motivate them to think about how components interact? Get at the 'causal loop diagram' from an intuitive place.

i also wonder if this could be an opportunity for jigsaw, where students focus on one aspect/element of a simple climate model with peers (understanding the impact of a variable in real terms, natural variability, reasonable estimates of past/current/future values), then reorganize to evaluate a climate model from soup to nuts. or at least from soup to salad. this also mixes it up with more and less math-oriented or geo-oriented students having something to add to the small group conversations and helping catch others up.

?

i also wonder if this could be an opportunity for jigsaw, where students focus on one aspect/element of a simple climate model with peers (understanding the impact of a variable in real terms, natural variability, reasonable estimates of past/current/future values), then reorganize to evaluate a climate model from soup to nuts. or at least from soup to salad. this also mixes it up with more and less math-oriented or geo-oriented students having something to add to the small group conversations and helping catch others up.

?

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I've put my student directly into model building. This is the first time I've taught a course, so it's hard to say if it's working. . . After 3 weeks working as teams on components of an integrated model for minerals-energy, we've got a model up, running, and ready for refinement.

I started by teaching them systems thinking concepts for problem framing to chose our model topic. The next week I taught them about parameters, decision variables, and feedback, and causal loops. They dove into the modeling with a tool similar to Stella. I heard a lot of grumbling the first week and confusion. I dedicated a full 3-hour session to trouble-shooting live with the class on their model the next week and put them back on task to improve the model. This 3rd week they seemed proud of their progress and are asking good questions about how to move the model onward.

Can this be called experiential or collaborative learning in the classroom?

I started by teaching them systems thinking concepts for problem framing to chose our model topic. The next week I taught them about parameters, decision variables, and feedback, and causal loops. They dove into the modeling with a tool similar to Stella. I heard a lot of grumbling the first week and confusion. I dedicated a full 3-hour session to trouble-shooting live with the class on their model the next week and put them back on task to improve the model. This 3rd week they seemed proud of their progress and are asking good questions about how to move the model onward.

Can this be called experiential or collaborative learning in the classroom?

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Wow, Suzanne, that's an ambitious project for your class, I'm glad that you've got them past the grumbly/confused phase. I suppose my biggest fear would be that the students would get stuck in that phase and not be able to move past it.

I like Liz's description of having the students working with a model change one parameter at a time. I imagine that the model responses would be easier to interpret, and probably be fairly intuitive too.

I like Liz's description of having the students working with a model change one parameter at a time. I imagine that the model responses would be easier to interpret, and probably be fairly intuitive too.

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I am fairly new to using models in the class but I tried a in class activity this past week. I gave the students some background then let them work in small groups with me leading the activity. The students were quite engaged and they quickly figured out how to use the model output after I showed them a couple of manipulations they could try. I urged them to try to recreate the results from a paper we read to see if they could reproduce the results. This lead to more discussion and engaged the students in critical thinking.

Afterwards, we discussed the model and the results. This is a different approach from covering the model details first, I let them work with data output first then we discussed the details of modeling. Many of the students were able to question the results based on what they had learn earlier in the course. I wanted to show them that models were not "scary" but quite useful tools.

I agree that blackboxes can be dangerous, but they are still useful. We use "blackboxes" everyday without fully comprehending the details inside. For example, we drive cars, fly in airplanes, use computers and the internet, but we do not completely understand everything inside these devices that make them work!

Afterwards, we discussed the model and the results. This is a different approach from covering the model details first, I let them work with data output first then we discussed the details of modeling. Many of the students were able to question the results based on what they had learn earlier in the course. I wanted to show them that models were not "scary" but quite useful tools.

I agree that blackboxes can be dangerous, but they are still useful. We use "blackboxes" everyday without fully comprehending the details inside. For example, we drive cars, fly in airplanes, use computers and the internet, but we do not completely understand everything inside these devices that make them work!

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Kristine - Glad to hear your activity worked so well! (Which model did you use?) I have given my Paleoclimatology students simple excel spreadsheets to calculate radiative equilibrium - sometimes I do this before introducing the calculations....Often, students have questions about how the calculations are done once they've used the model...If I lecture too much about how the model works before they get a chance to play with it, they have a tendency to tune out. (The Paleo students don't all have the math background that some of my Meteorology students have...)

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I like very much the idea of a hands on model- watershed? then moving to a numerical model. I had not thought of this. At the intro level I usually give examples and images of conceptual models, then move to natural systems. For upper division classes we get to plotting data in excel and comparing output, but this is very stagnant- I think. But the students generally don't have a strong background in math or graphing in excel- hence my bringing it into almost every class now- scaffolding across classes to reinforce familiarity and frequent use.

I would like to know more about the "models" used in classes to which people are referring. Are there others besides stella and excel?

I would like to know more about the "models" used in classes to which people are referring. Are there others besides stella and excel?

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I found this activity example while looking for more info on the Richardson Forecast Factory -- it could be helpful in introducing students to climate models (e.g. what goes into them)

http://www.met.reading.ac.uk/outreach/richardson_fancy.pdf

(I also linked it to the Resources page with a link to a summary of the Forecast Factory) - look under "Other resources" -> Articles and Books

http://serc.carleton.edu/NAGTWorkshops/climatemodels/resources.html#org

http://www.met.reading.ac.uk/outreach/richardson_fancy.pdf

(I also linked it to the Resources page with a link to a summary of the Forecast Factory) - look under "Other resources" -> Articles and Books

http://serc.carleton.edu/NAGTWorkshops/climatemodels/resources.html#org

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