Joe Dauer: How do natural resource students organize and make sense of a flood of biology concepts?
Dr. Joe Dauer is a member of UNL's School of Natural Resources working in scholarship and teaching of life sciences education. His work is at the intersection of biology (including genetics, ecology, and evolution), education and cognitive psychology. Joe joined the Biennial Conference on University Education in Natural Resources hosted by the University of Nebraska-Lincoln in March 2022 to share his experiences in his keynote: “How do natural resource students organize and make sense of a flood of biology concepts?”
icon search Searchable Transcript
Toggle between list and paragraph view.
[00:00:00.660]Thanks for joining us after that short break.
[00:00:05.280]I'm really excited about our next speaker.
[00:00:07.950]He's a colleague here at the University of Nebraska.
[00:00:12.450]Joe Dauer describes his research focus simply
[00:00:15.600]as I focus on how undergraduate students learn biology.
[00:00:19.440]If we could all put our focus in a sentence like that,
[00:00:22.980]I guess that's what we're supposed to do
[00:00:24.150]with our elevator talks to our colleagues,
[00:00:26.490]but he is works on the scholarship and teaching of life
[00:00:31.860]sciences education here in the School of Natural Resources
[00:00:35.370]at the University of Nebraska.
[00:00:36.690]And his work is at the intersection
[00:00:39.000]of the biology that includes genetics, ecology,
[00:00:41.970]and evolution with education and cognitive psychology.
[00:00:45.900]And those three things really make him
[00:00:48.300]a really interesting colleague to talk with.
[00:00:50.700]I've had the pleasure of working with Joe to develop
[00:00:53.010]curriculum for a new institute
[00:00:54.690]of conservation agriculture in Rwanda.
[00:00:57.630]And I grew a lot from that interaction personally.
[00:01:01.230]He's a broad collaborator.
[00:01:03.360]He has a very interesting approaches to his work,
[00:01:06.120]and I think it's critical if we're going to be inclusive
[00:01:09.870]in our teaching, that we understand how our students learn.
[00:01:13.320]And so that's why we asked Joe to share with us,
[00:01:16.650]and I'm really happy
[00:01:17.730]that he's able to today with his keynote,
[00:01:19.920]how do natural resource students organize and makes sense
[00:01:22.770]of a flood of biology concepts?
[00:01:25.110]So Joe floor is yours.
[00:01:27.780]Great. Thank you so much.
[00:01:30.540]I really appreciate
[00:01:31.903]the opportunity to come and talk with you today.
[00:01:34.170]And it has been a great time with Larkin.
[00:01:38.730]I've enjoyed my interactions
[00:01:40.320]with him and appreciate the invitation to come.
[00:01:43.650]I also think it's very challenging to follow my friend
[00:01:46.680]and colleague Brian Dewsbury.
[00:01:48.916]He's just been fantastic.
[00:01:51.240]And even while he was talking,
[00:01:53.010]I was thinking maybe I should change a few of my slides.
[00:01:55.320]So I'll try to incorporate some of the ideas
[00:01:57.060]in here that he brought up.
[00:01:59.940]Me, share my screen here.
[00:02:04.684]All right. Are you able to see that okay?
[00:02:07.590]Yes. Looks great, Joe. Thanks.
[00:02:10.215]So this is going to be quite different than,
[00:02:15.390]than the talk that Brian gave and that I'm gonna be focusing
[00:02:18.540]really specifically on the idea of learning.
[00:02:21.930]So less about the idea of motivation or how that impacts it.
[00:02:26.550]Although that's true in the work that I've been doing,
[00:02:29.820]but I've been really focused on how students
[00:02:32.460]in our classrooms are thinking about the ideas that we're
[00:02:35.820]presenting to them and how they actually
[00:02:38.970]store those, that information,
[00:02:41.070]and they retrieve that when we ask them to.
[00:02:45.120]So my background, oops.
[00:02:50.850]My back, oops, come on.
[00:02:54.840]It didn't advance, did it?
[00:02:57.330]Oh, there we go.
[00:02:58.200]Got it. Okay.
[00:02:59.370]So I, my background is actually in applied ecology.
[00:03:03.330]I did my work as a weed scientist,
[00:03:06.210]and I was really interested in how seeds were moving
[00:03:08.370]around the field.
[00:03:09.203]And this was my passion for a long time,
[00:03:12.000]was focusing on plants.
[00:03:14.370]And at some point, while I was teaching as a postdoc,
[00:03:17.580]I had a similar experience as Brian did,
[00:03:20.190]in that I was standing there in front of the classroom,
[00:03:22.800]realizing I just did not have the,
[00:03:24.900]the understanding of what I was doing.
[00:03:27.690]And at that time I really started to think about what
[00:03:31.230]the students were, their,
[00:03:34.770]what they were doing in the classroom.
[00:03:36.240]I was really focused on the learning
[00:03:39.270]and, more so than the teaching side of it.
[00:03:42.840]I was interested in how I could change my teaching,
[00:03:44.820]but I was really thinking about what was happening
[00:03:46.650]in their head.
[00:03:48.120]And so the field that I participate in
[00:03:51.420]is this discipline-based education research.
[00:03:53.970]So I do work that is focused on learning,
[00:03:57.090]but it is all grounded in my background in ecology.
[00:04:00.840]That's the, in biology broadly.
[00:04:04.860]So I use the methods of educational research
[00:04:07.290]and think about the science, like natural sciences,
[00:04:11.430]that or natural resources that we worked in.
[00:04:14.220]And specifically, I'm thinking about the neuro,
[00:04:18.270]the neuro side of things, the cognitive side of things,
[00:04:21.330]and the behavioral outputs
[00:04:23.430]so that students are producing in our classrooms.
[00:04:26.760]And so I really think about this interface between teaching
[00:04:31.770]and how the students are receiving that information.
[00:04:35.040]So I've been privileged here
[00:04:36.870]at UNL to have these have great collaborators.
[00:04:40.380]And so this work that I've been I'm presenting today
[00:04:43.140]is a reflection of the years of working
[00:04:46.920]with these individuals, as well as,
[00:04:50.100]and even probably more so the postdocs graduate students
[00:04:52.980]and undergraduate students have really supported this work.
[00:04:58.260]So more than 10 years ago,
[00:05:00.210]now the AAAS put out this document,
[00:05:03.390]the vision and change for undergraduate
[00:05:06.270]or in undergraduate biology education.
[00:05:08.490]And if you haven't taken a look at this,
[00:05:09.930]this has really been bribing the biology curriculum
[00:05:13.140]for many years.
[00:05:14.940]This was the output of discussions
[00:05:19.230]between 500 faculty science and yeah,
[00:05:25.020]500 scientists around the country to identify
[00:05:28.110]what are the core concepts that our students need to know,
[00:05:31.380]and what are the core competencies
[00:05:33.480]or the skills that these students need.
[00:05:36.420]One of those that I'm gonna be talking about mostly today
[00:05:39.270]is this one about modeling and simulation.
[00:05:43.410]And so my focus,
[00:05:45.120]my research focus has been on how students learn
[00:05:48.780]through models and through computational models
[00:05:52.230]or simulation modeling.
[00:05:55.380]So as experts, we all use models a lot,
[00:05:59.940]but in very different ways.
[00:06:01.680]So some of us are looking to understand the complex systems
[00:06:05.010]that we interact with,
[00:06:06.090]the socio scientific systems, the ecosystems.
[00:06:09.840]We look to understand what's unknown in those,
[00:06:13.278]maybe we're looking at molecular side of things
[00:06:16.350]or molecular pathways.
[00:06:18.540]We try to make predictions about what will happen,
[00:06:20.790]how is climate going to impact certain interactions?
[00:06:24.210]And we try to make these causal explanations,
[00:06:27.000]mechanistic understanding of what's going on
[00:06:29.430]in the environment.
[00:06:31.890]And so in that way we are, modeling is our way
[00:06:36.930]of knowing science.
[00:06:38.220]When we go to a conference in person usually, or, but,
[00:06:41.400]you know, often even in this conference,
[00:06:43.080]you'll see this throughout that models
[00:06:45.660]are how we communicate.
[00:06:47.430]We talk about 'em, we see 'em on the posters.
[00:06:50.940]We see 'em in talks.
[00:06:51.840]We see 'em in our textbooks.
[00:06:53.700]Models are how we think about it.
[00:06:55.500]Their representations of this system, the complex systems.
[00:06:59.130]And that is really what we want to convey.
[00:07:03.480]It's how we convey our information.
[00:07:07.710]But this is, so for experts,
[00:07:10.230]we really tend to build these models that are for a purpose,
[00:07:14.580]that is that we have models that we link between.
[00:07:17.520]We link evidence and we link to those models.
[00:07:20.910]We have multiple models.
[00:07:22.410]We use model selection criteria to kind of determine
[00:07:26.010]which ones are the best models to fit the information.
[00:07:29.580]We look at the try to,
[00:07:31.100]to determine the mechanisms that are inherent in those,
[00:07:34.530]the mechanistic models that we build.
[00:07:38.040]But students, many of the students
[00:07:39.930]that are coming into our intro,
[00:07:41.280]especially at the intro level, introductory level,
[00:07:43.650]but I would say throughout their undergraduate career.
[00:07:48.090]They're really thinking about models of a phenomena.
[00:07:50.460]We have asked them to understand
[00:07:53.790]what's the model of photosynthesis.
[00:07:56.610]You know, can you write out this equation
[00:07:58.380]or tell me a, you know, draw a food web.
[00:08:03.150]They're building a model of a phenomenon.
[00:08:05.790]So they're able to organize components to some extent,
[00:08:09.330]but they often are thinking about this in a linear fashion.
[00:08:13.560]And my work has been, although or I can,
[00:08:17.430]distill it down to that very nice sentence, I guess.
[00:08:21.750]But is really on this,
[00:08:23.190]around this question about
[00:08:25.050]how does model-based reasoning develop
[00:08:27.540]for university students.
[00:08:29.190]So I'm really focused on how those stepping stones.
[00:08:32.400]How students can go from this novice approaches,
[00:08:36.030]novice ways of thinking about models
[00:08:38.100]of a phenomena towards this idea of models for a purpose.
[00:08:42.810]And so that's what my research focuses on,
[00:08:45.270]how we get them there or how they're,
[00:08:48.420]how they're developing in that process.
[00:08:50.730]We do have students by the end of their undergraduate
[00:08:53.220]career, for sure who are acting much more like experts,
[00:08:56.820]and I'll be talking about some of that work today.
[00:09:01.200]So in order to really understand that,
[00:09:03.060]we have to understand how we as instructors
[00:09:07.350]and people in general store and retrieve knowledge.
[00:09:11.850]So this is going to get into the nitty gritty of the brain.
[00:09:18.570]And one of the things that's key is that students,
[00:09:21.750]many students or novices in an area store knowledge
[00:09:26.010]in a very isolated way.
[00:09:28.140]That is that, and you see this all the time.
[00:09:30.900]When you ask students a question on exam or something,
[00:09:34.530]and you get a long list of all the information
[00:09:38.100]they know about that topic.
[00:09:39.690]Maybe not what that question was asking them,
[00:09:41.760]but they tell you everything they know.
[00:09:44.070]And what you're seeing is that they have
[00:09:45.840]a lot of isolated information.
[00:09:47.610]They have a lot of experience, right.
[00:09:51.150]And, but much of this information is stored
[00:09:54.690]in a way that's not easy to retrieve.
[00:09:57.810]So this is a schematic obviously of a, like a neighborhood.
[00:10:02.370]And the thing about this is
[00:10:04.350]it kind of represents what I'm trying to get at,
[00:10:06.930]which is that if you store knowledge in this kind
[00:10:09.180]of cul-de-sac or this, you know, this offshoot,
[00:10:12.360]there's very few ways to retrieve it.
[00:10:15.300]Because the way that the knowledge is stored in your brain
[00:10:17.910]is the way you retrieve it.
[00:10:19.650]So if you store knowledge in these little isolated areas,
[00:10:22.590]it's very difficult to retrieve,
[00:10:24.930]and it's definitely not connected
[00:10:26.580]to other pieces of information.
[00:10:29.730]Now that really varies for others, for experts.
[00:10:33.690]And for those who have
[00:10:35.906]a much more connected pieces of knowledge,
[00:10:39.150]we think about 'em as chunks of knowledge.
[00:10:41.580]And this goes back, as Brian was saying at the end there,
[00:10:44.850]question and answer,
[00:10:45.683]when you have students going outside
[00:10:47.970]and experiencing some of these events,
[00:10:51.990]or they experience the science.
[00:10:54.390]What you're doing is providing opportunities for them
[00:10:57.060]to connect those knowledge, those pieces of knowledge.
[00:11:00.120]And that's what experts have.
[00:11:01.560]They have lots of interconnected pieces of knowledge,
[00:11:04.950]and that allows multiple access points.
[00:11:07.680]So if one is not really strong
[00:11:09.810]or might be slightly incorrect, there's other ways
[00:11:13.200]to get around that and to still access the information.
[00:11:18.270]So we want to move students from isolated knowledge
[00:11:22.200]to connected knowledge.
[00:11:23.460]The knowledge isn't disappearing,
[00:11:25.590]it's just being reconnected.
[00:11:27.450]So we have to transform this neighborhood from this one,
[00:11:30.720]that's very isolated into one that's much more connected.
[00:11:35.310]So the first study I'm talking about is really focused
[00:11:38.190]on determining how behavioral
[00:11:41.610]and neurocognitive effects
[00:11:42.930]of reading versus simulating models.
[00:11:45.390]So reading can be thought of as very similar
[00:11:48.180]to what we students would be experiencing,
[00:11:50.160]when we ask them to read the textbook
[00:11:52.500]versus simulating biological systems
[00:11:55.500]where we're asking this is becoming much more common.
[00:11:58.590]We have a lot of technology that allow textbooks
[00:12:01.140]have simulations that are, you know,
[00:12:04.500]ancillary simulations that you can use.
[00:12:09.240]So I'm, we're trying to get at
[00:12:11.100]what are students doing differently behaviorally,
[00:12:14.160]and neuro-cognitively when they're reading versus simulating
[00:12:17.430]the same, the same system.
[00:12:21.450]So the background on this is that simulations
[00:12:24.570]do tend to help students because they,
[00:12:28.770]it helps in terms of the content knowledge,
[00:12:31.890]but it also helps them in terms of processing skills,
[00:12:35.730]like thinking about the system,
[00:12:37.740]understanding maybe using it as an analogy.
[00:12:41.933]And we know that physical actions.
[00:12:45.570]So things like this is a study of a physics, at a physics
[00:12:49.320]where pulling a spinning wheel to think about inertia.
[00:12:54.810]That actually activates different parts of the brain
[00:12:58.080]than even watching somebody do that or reading about it.
[00:13:01.710]So we were very interested in combining these pieces
[00:13:04.290]of information to think about how students
[00:13:06.810]are interacting with biological system models.
[00:13:11.760]So the case here,
[00:13:12.593]and I know this isn't necessarily a case
[00:13:14.400]that it'd be relevant to many of our areas of study,
[00:13:17.880]but this was done in introductory biology.
[00:13:21.630]So this is a very common case study
[00:13:23.460]to look at prokaryotic regulation of gene expression.
[00:13:27.360]And so we looked at E coli in the bacteria,
[00:13:30.690]in your gut and ask students questions
[00:13:32.640]about how environmental conditions affect the
[00:13:35.907]whether genes are activated or inactivated,
[00:13:40.020]or active or inactive.
[00:13:42.147]And in this case, we had students do two things.
[00:13:45.570]The students were in one or another
[00:13:47.550]of these treatments.
[00:13:50.280]Simulating students, they
[00:13:52.872]well, all students read about the E coli
[00:13:56.250]and did a little, a basic task.
[00:13:58.770]And then the simulating students went through
[00:14:00.420]a process of predicting and explaining, simulating,
[00:14:03.690]looking at the results, trying to make,
[00:14:05.790]compare their predictions to their results.
[00:14:09.270]Things that we would consider best practices
[00:14:11.460]in this process of simulating.
[00:14:14.520]Now, the students in the read condition,
[00:14:17.070]they were provided with all of the same outputs.
[00:14:20.220]So we presented them with all of the same results.
[00:14:24.090]The graphs, the models that they could could use.
[00:14:30.180]And then we actually provided expert analysis.
[00:14:32.580]So not just answered the questions,
[00:14:34.440]but we provided additional analysis.
[00:14:37.290]You know, this would be not dissimilar from
[00:14:40.380]a scientific paper that you might read
[00:14:43.380]or even textbooks that are very detailed or detailed.
[00:14:47.670]And then they had to, we placed the students in a FMR,
[00:14:52.110]so functional magnetic resonance imaging machine,
[00:14:55.650]like the one you see here in this bottom picture,
[00:14:59.220]and we asked them a series of questions about the system.
[00:15:05.040]So for many of you might not be familiar with
[00:15:08.250]what the FMR is measuring.
[00:15:10.380]And so I'll just give you a little background on that.
[00:15:13.410]So FMR measures the change in magnetic properties
[00:15:18.210]of the blood.
[00:15:19.590]So it looks at the blood oxygenation level.
[00:15:22.740]And so oxygenated, blood and deoxygenated blood
[00:15:26.160]have different magnetic properties.
[00:15:28.080]So the FMR is a very strong magnet,
[00:15:31.020]and it is taking images of your brain to,
[00:15:36.330]it takes a like slices of the, sliced images of your brain.
[00:15:40.980]And in little three dimensional what they call voxels,
[00:15:44.130]it's measuring this change in blood oxygenation level,
[00:15:47.880]between when you have like a baseline question
[00:15:50.760]versus your control question or the.
[00:15:54.060]You know, so when you ask them the question versus
[00:15:56.910]some of these baseline things where you can see
[00:15:58.710]in these images, like there's sometimes just gray screen
[00:16:01.380]or a dot to kind of let your neurons go back to this,
[00:16:07.148]to their stable state. And then they're activated.
[00:16:10.260]So activated neurons are going through cellular respiration
[00:16:13.560]and using that oxygen.
[00:16:15.330]And then, and therefore they're de-oxygenating the blood.
[00:16:18.150]And so that changes the areas of the brain that are active
[00:16:22.320]are, have more deoxygenated blood
[00:16:25.200]and have a different signal
[00:16:26.370]than areas that are not active during a question.
[00:16:30.090]So that's what we were looking at.
[00:16:31.410]That's what, what the FMR was doing.
[00:16:34.110]And then the task was a series of questions for them.
[00:16:37.920]Some of the questions were system specific.
[00:16:39.900]So we were asking them specifically about gene regulation,
[00:16:43.440]and some of the questions were system general in which
[00:16:47.580]we were asking them to use analog reasoning.
[00:16:50.970]So compare, like transfer the ideas from this system
[00:16:54.960]to a general system where you can see we've just replaced
[00:16:58.470]that there's no components or enzymes or anything like that.
[00:17:02.220]It's just As, Ds, Cs and DS.
[00:17:04.710]But they have,
[00:17:05.730]they interact in the same ways in terms of inhibition
[00:17:08.730]or activation of each other.
[00:17:11.040]So one of the limitations of an FMR is that you only,
[00:17:16.320]because you're, it's such a strong magnet,
[00:17:18.330]you obviously can't have any metal on your body.
[00:17:21.810]And so the questions,
[00:17:23.283]they have a very special keypad
[00:17:26.100]and you only get two options.
[00:17:27.810]So these questions may seem a little simplistic,
[00:17:30.210]but that's because we always had
[00:17:31.440]to ask the questions with only two possible answers.
[00:17:35.040]So there's a correct and an incorrect in this case.
[00:17:40.650]So when we looked at behavioral differences,
[00:17:42.540]so these would be like accuracy, right.
[00:17:44.940]So how correct were they?
[00:17:47.280]And you'll notice that in the system specific
[00:17:51.480]on the system specific questions,
[00:17:53.340]they did better than chance,
[00:17:54.690]but there's no difference between reading and simulating.
[00:17:57.570]And there's also no difference between reading
[00:17:59.040]and simulating in the system general.
[00:18:01.230]And they also did it about chance.
[00:18:03.480]So basically those questions
[00:18:05.010]were very challenging and they were,
[00:18:07.380]they did as well as desk on the system general questions.
[00:18:11.610]So the important thing here is that there was no behavioral
[00:18:14.160]differences between reading and simulating.
[00:18:17.310]So they were performing the same way.
[00:18:19.530]So this intervention or this idea that simulating
[00:18:22.178]was providing some benefit
[00:18:24.840]was not being born out in behavioral responses.
[00:18:30.480]But we do see some differences
[00:18:32.760]when we look at the neurobiology.
[00:18:35.730]And in particular, on the system specific ones,
[00:18:38.610]the students who were in the simulating treatment,
[00:18:41.460]they had significantly more activation of some of these
[00:18:44.730]regions of the brain that have been associated in a lot
[00:18:48.060]of studies with hypothesis, generation and causal reasoning.
[00:18:52.080]So they were doing something different,
[00:18:55.260]even though they're providing the same results.
[00:18:58.830]Now we don't see the same pattern in the system general.
[00:19:03.720]And in fact, we had hypothesized, we would see all this,
[00:19:07.290]we would see activity in areas of the brain
[00:19:09.360]that use analogical reasoning.
[00:19:11.700]Because remember we, that was the whole,
[00:19:13.440]that was the whole point for setting them up this way.
[00:19:16.530]But we didn't,
[00:19:17.363]we actually saw increased activity in these regions that
[00:19:20.250]are associated with sensory motor.
[00:19:21.750]The same ones actually as holding a spinning wheel.
[00:19:25.350]So we're not exactly sure if maybe the students were
[00:19:28.980]thinking about the simulations and like a very embodied way.
[00:19:32.790]Like they were part of the simulations.
[00:19:34.230]We don't know exactly.
[00:19:35.250]This was not a result that we were expecting.
[00:19:40.710]One of the key things that I'm gonna be talking about today
[00:19:43.650]was kind of came from this other part of the study,
[00:19:47.310]which is, if we look at,
[00:19:48.660]regardless of the reading versus simulation treatment,
[00:19:51.300]we looked at all the students.
[00:19:53.760]We noticed that students choosing the correct response,
[00:19:56.880]activate particular part of the brain
[00:20:00.120]that experts use when they are answering questions,
[00:20:05.280]which is this part called the anterior singular cortex.
[00:20:08.850]And that's represented
[00:20:11.040]here right in the middle of the screen.
[00:20:13.680]It's kind of deep underneath the frontal lobe,
[00:20:17.640]kind of that part of your brain.
[00:20:20.250]And this is the part that is associated
[00:20:22.230]with detecting errors and inhibiting those errors.
[00:20:26.430]So detecting errors is a really important skill
[00:20:30.180]in model evaluation.
[00:20:32.010]So determining whether something is correct or incorrect.
[00:20:36.390]And experts use this very fluently.
[00:20:38.760]This is what's something,
[00:20:39.750]that this is one of the distinguishing characteristics
[00:20:42.180]between novices and experts.
[00:20:46.290]So the conclusion of this study was that reading
[00:20:48.630]and simulating, they activate different parts of the brain,
[00:20:52.620]even though the behavioral results are the same.
[00:20:55.890]So when you, as an instructor
[00:20:58.050]are getting the same results from students,
[00:21:00.690]you might be seeing something that looks like these students
[00:21:04.200]are on par with each other, but they pop they're.
[00:21:07.290]It's possible that they're doing
[00:21:08.760]very different cognitive things in the background,
[00:21:12.600]and there's substantial differences, individual differences.
[00:21:16.118]So that student performance is not necessarily explained by,
[00:21:21.390]or may not explain the variation
[00:21:22.920]in the student's brain activity, right.
[00:21:24.513]That some of 'em are actually much more engaged
[00:21:27.840]in deep reasoning than others.
[00:21:31.470]And so we wanted to back that up.
[00:21:32.880]So we have a,
[00:21:33.713]we have additional studies that we were interested in.
[00:21:37.560]Oops, I'm sorry.
[00:21:40.500]Step ahead of myself.
[00:21:43.980]And so we set out on another study in which we were really
[00:21:48.720]interested in this idea of misconceptions.
[00:21:52.230]And misconceptions are ideas that are incorrect,
[00:21:57.990]that are stored in your, in your memory.
[00:22:01.740]That may prevent you from actually using
[00:22:06.750]the gathering or obtaining the correct, correct response.
[00:22:11.580]And so not all incorrect written responses on an exam
[00:22:16.350]for example, are misconceptions, but some of them are.
[00:22:20.010]And we all, all people store
[00:22:23.160]these misconceptions and have misconceptions.
[00:22:26.550]And the reality is that when we learn,
[00:22:29.370]when we learn anything that we,
[00:22:32.670]we sometimes through experience or knowledge
[00:22:35.130]that we gain from reading or some other way,
[00:22:38.490]we have stored some incorrect ideas and they don't go away.
[00:22:43.080]And that's, that's kind of a problem,
[00:22:44.880]but it's just an evolutionary fact about
[00:22:47.400]how our brains store knowledge.
[00:22:50.370]It's just that experts
[00:22:51.480]are really good at getting past those.
[00:22:54.450]So we've gathered
[00:22:55.290]the correct information about certain ideas.
[00:22:58.680]And instead of pulling up this wrong idea,
[00:23:01.590]we actually inhibit that idea from coming to the core
[00:23:04.860]and saying something, and we say the correct thing,
[00:23:08.910]or we write the correct thing.
[00:23:11.460]And so we have to help students think about how
[00:23:14.670]can they activate that part of their brain.
[00:23:16.680]How can we, how can we teach towards that?
[00:23:22.043]So that made us think about these data
[00:23:24.630]that we had seen, that we had well seen,
[00:23:28.230]we had been a part of.
[00:23:30.300]Two studies that we really couldn't
[00:23:32.520]explain the mechanisms of.
[00:23:35.201]This was a study, one study that I had done when,
[00:23:38.100]while I was a postdoc and the second study
[00:23:40.140]that was done by Steve Bennett,
[00:23:43.110]who is a graduate student at Michigan State
[00:23:45.510]following my time there.
[00:23:47.580]And in both of these studies, we saw the same results.
[00:23:51.600]And so this figure best represents it.
[00:23:54.210]This is from Steve Bennett's work.
[00:23:56.850]Which is at the end of the course,
[00:23:58.860]an introductory biology course that
[00:24:00.780]has used a lot of models.
[00:24:03.510]We find that the students
[00:24:05.430]in the lower performing, middle performing
[00:24:07.950]and the higher performing trial tiles of the class tend
[00:24:11.610]to have the same or representative performance on models.
[00:24:17.850]So this case, we're looking at approaches to modeling,
[00:24:20.160]like how do they think about models,
[00:24:22.380]but also in terms of the model,
[00:24:24.600]the quality of the models that they can create.
[00:24:27.390]Higher performing students tend
[00:24:28.620]to produce better models and things.
[00:24:31.830]But the most important result from this was that if you
[00:24:35.460]came back and you asked students one or two years later
[00:24:39.900]about the same kinds of questions as in the class.
[00:24:42.750]So you asked them to create models,
[00:24:45.300]you asked them about how they approach models or modeling.
[00:24:48.960]We find that the middle performing students
[00:24:51.960]have the lowest attrition of knowledge,
[00:24:54.090]in fact, no attrition of knowledge.
[00:24:56.010]Whereas the high and low performing students
[00:24:58.140]tend to drop off a lot.
[00:25:01.590]And it really led us to wonder, why is it,
[00:25:04.410]what are the middle performing students doing,
[00:25:07.920]that's allowing them to retain knowledge more than others?
[00:25:12.811]You'll notice also the other thing
[00:25:16.237]that's really been interesting for us
[00:25:18.510]is that the low and middle performing students,
[00:25:21.750]there's a lot of variability.
[00:25:23.820]There's a lot of variability in that knowledge
[00:25:27.210]that those students have after a year.
[00:25:29.790]And so we're kind of really interested in that individual
[00:25:32.580]variability in terms of long term knowledge retention.
[00:25:38.700]So it led us to ask the question about whether or not
[00:25:44.040]students engage in this error detection
[00:25:46.470]and inhibition during modeling evaluation.
[00:25:49.020]So are they, are some students using that,
[00:25:52.050]that part of the brain,
[00:25:52.980]the anterior singular cortex, more than others?
[00:25:56.070]And how does that affect their short term accuracy
[00:25:58.530]and their long term knowledge retention?
[00:26:01.050]And just a forewarning,
[00:26:02.850]I'm not going to actually be able to finish this story today
[00:26:07.200]because we are currently in the process of doing
[00:26:09.810]the interviews with the students one year after the class.
[00:26:13.410]So we don't have the long term retention.
[00:26:15.330]So I just wanna burst that bubble before we get there.
[00:26:20.250]So we have 34 students who participated
[00:26:25.260]in a modeling-based instruction course last spring.
[00:26:29.130]So spring of 2021.
[00:26:31.740]And they, those students came in to the lab and they,
[00:26:37.203]they evaluated correct and incorrect versions of models.
[00:26:41.850]So we had 12 concepts that we had,
[00:26:45.240]and they covered genetics
[00:26:47.160]and evolution, ecology, and physiology.
[00:26:50.190]And then that, and there were,
[00:26:52.170]there was one correct model and two incorrect versions
[00:26:55.500]of each of those concepts.
[00:26:56.850]So there's 36 total models,
[00:26:59.100]and they saw 'em in a random order.
[00:27:01.380]And while they're doing that, they are participate.
[00:27:04.950]They're in an FMRI scanner as well.
[00:27:08.790]And they're seeing, they have two tasks.
[00:27:11.760]And the first task is this what we would call
[00:27:16.020]the gold standard
[00:27:17.100]of understanding error detection and inhibition.
[00:27:20.570]So this has been around for years and years and years.
[00:27:24.030]And in this case, it's called the go, no go.
[00:27:26.910]And when students see a word like red
[00:27:30.870]and it's in a different color, they have to say, no go.
[00:27:34.410]But if it's in red and if it's the word is red
[00:27:37.530]and it's in the color red, then they say go,
[00:27:39.990]or they press, they press buttons.
[00:27:43.350]And if you see it too, in the same in a, in consecutive one,
[00:27:47.790]so red, but like these two red, red ones,
[00:27:51.450]then you have to inhibit your response
[00:27:54.810]that push the button the second time.
[00:27:56.910]It's a little confusing to explain,
[00:27:58.770]but this has been around for a long time to,
[00:28:01.260]and it always activates this part of the brain,
[00:28:03.300]the anterior singular cortex.
[00:28:05.940]And then students participated in this error detection task
[00:28:08.850]that similar to the one before where they would ask,
[00:28:12.300]they ask questions about whether
[00:28:14.220]the model has an error or no error.
[00:28:17.490]And then we asked them about confidence.
[00:28:20.010]How confident are you that, that your response is correct?
[00:28:26.070]And I'm not gonna talk a lot about the confidence rating
[00:28:28.380]because we're still working on the analysis of that,
[00:28:30.420]but it's actually proving to be quite intriguing.
[00:28:32.790]And so I'm happy to,
[00:28:34.140]if we have time to talk about it in the discussion section.
[00:28:39.000]So maybe as no surprise,
[00:28:42.300]there is according if you look at the percent
[00:28:46.050]of questions that they,
[00:28:48.360]that the students were accurate in which meant
[00:28:51.240]that they determined whether or not
[00:28:52.650]that correctly determined whether or not there was an error.
[00:28:55.800]There is some relationship with the GPA,
[00:28:58.260]although it's slight.
[00:28:59.580]So students who on average answered 23 out of
[00:29:02.927]the 36 models correctly.
[00:29:05.310]And there is some increase for students who are,
[00:29:08.430]have had higher GPA in the class.
[00:29:13.350]it's not reflected in their confidence score.
[00:29:16.320]So there seems to be a slight
[00:29:19.230]but very slight negative relationship.
[00:29:21.780]And you'll notice the most confident students
[00:29:23.400]are not the highest GPA students.
[00:29:26.685]So students with the highest GPA do perform slightly better,
[00:29:29.910]but they're no more confident.
[00:29:33.960]But as you might expect,
[00:29:35.850]it really starts to get interesting
[00:29:37.410]when we think about the,
[00:29:40.530]the neural side of things.
[00:29:42.870]Which is that when we look at the overlap between
[00:29:45.330]that go, no go that gold standard and our task,
[00:29:48.660]we find that there is a significant overlap
[00:29:51.180]in this one particular region, the anterior singular cortex.
[00:29:55.200]And that's crucial.
[00:29:56.400]That tells us that those students,
[00:29:58.620]that the task we created is something
[00:30:01.110]that is activating, is doing the same thing.
[00:30:04.650]And so it does suggest that there test,
[00:30:06.690]that there's this general mechanism of learning.
[00:30:09.480]Which is that students are inhibiting responses or stopping
[00:30:13.890]themselves from saying, something's from making an error.
[00:30:18.420]That's also at play
[00:30:20.190]when they're evaluating models for errors.
[00:30:22.980]So when they're looking at models,
[00:30:24.780]that's really important that they're doing
[00:30:26.160]this model evaluation task,
[00:30:29.010]and they're activating that part of the brain.
[00:30:33.960]So behaviorally the students are accurate
[00:30:36.810]in line with their GPA,
[00:30:37.920]just like we found in these previous, these earlier studies.
[00:30:41.730]And, you know, the part that I'm excited, you know,
[00:30:43.950]one of the things I'm very excited about is that we
[00:30:46.350]are really curious, what's gonna happen after one year,
[00:30:48.930]if that's going to hold true,
[00:30:50.760]or if it's gonna look like the previous data,
[00:30:53.070]or if it's gonna look different.
[00:30:55.503]But cognitively the self-awareness about how they evaluate
[00:31:00.570]models does not seem to be connected with the GPA.
[00:31:03.570]We're interested to see how this plays out in that
[00:31:07.380]after one year, in terms of their confidence in year one,
[00:31:10.590]and whether that, that has an impact later on.
[00:31:14.070]But neuronally, if we looked at the neurons
[00:31:17.010]and the parts of the brain that are active,
[00:31:18.450]there's a lot of variation in model evaluation ability.
[00:31:22.860]That is that there's some students who are activating
[00:31:27.090]these parts of the brain when they're,
[00:31:29.310]they're answering the questions correctly.
[00:31:31.590]And that's the part that's,
[00:31:33.810]that's really exciting for us that these students
[00:31:36.603]that are might be doing things in the classroom
[00:31:39.630]or in this study.
[00:31:41.580]That is, even though
[00:31:43.320]it's not showing up necessarily behaviorally
[00:31:46.230]it's showing up in their brain,
[00:31:47.700]and it might have a real long term effect.
[00:31:51.030]So what we're seeing might be masking the cognitive
[00:31:54.360]processes that students are,
[00:31:56.340]that some of the students are actually acting expert like,
[00:31:59.970]even if their behaviors or their results that you're seeing
[00:32:03.000]are not the same, are not expert like,
[00:32:06.540]or maybe they don't produce the best answers.
[00:32:08.910]But they're processing it in a different way.
[00:32:13.560]So I wanted to talk about these,
[00:32:17.640]these principles of conceptual change
[00:32:19.980]that are quite important for teachers to really grasp.
[00:32:25.800]One is that this encoded knowledge,
[00:32:27.960]the way you store knowledge, it's a permanent thing.
[00:32:30.600]So when students, students learn, we don't,
[00:32:33.000]we have no control over their experiences
[00:32:36.000]before they come into our class.
[00:32:37.950]Well, okay, so that's not completely true,
[00:32:40.320]but you know, mostly true.
[00:32:43.530]And so they might be experiencing knowledge
[00:32:47.490]in different ways or incorrect ways, but that's stored.
[00:32:50.760]And it's important to recognize that students and us
[00:32:54.240]all have that knowledge still in their brain.
[00:32:58.650]And when you ask a question,
[00:33:00.600]they'll activate both incorrect
[00:33:02.820]and correct pieces of knowledge.
[00:33:04.590]So those are both being tossed
[00:33:07.500]around in their working memory at the time.
[00:33:11.550]So the new knowledge has to be integrated
[00:33:14.520]into that prior knowledge.
[00:33:15.900]So it, that prior knowledge is incorrect,
[00:33:18.150]you have to get that new knowledge that's correct
[00:33:21.270]into that old prior knowledge.
[00:33:23.700]It has to be fit together.
[00:33:25.980]And that's where more connections can be really helpful.
[00:33:30.810]So having more experiences and more repe,
[00:33:33.750]this is where, and this last point.
[00:33:36.180]The repetition of getting new ideas that are correct
[00:33:40.020]in there are going to allow for it to outcompete,
[00:33:43.710]that prior knowledge that may be incorrect.
[00:33:46.560]So it doesn't go away.
[00:33:47.580]That prior knowledge, that's incorrect, doesn't go away,
[00:33:50.370]but you're actually providing a way to outcompete it,
[00:33:53.550]because you're strengthening certain neural connections
[00:33:57.060]through that repetition.
[00:34:00.540]So we present students with a ton
[00:34:03.930]of different types of models.
[00:34:05.160]These are just, you know, these are all over the,
[00:34:07.577]the spectrum in terms of their content.
[00:34:10.380]And students have to reason
[00:34:12.750]with these in a lot of different ways.
[00:34:15.660]And this is really challenging.
[00:34:17.400]There is a lot of information in here.
[00:34:19.620]There's a lot of knowledge.
[00:34:21.660]A lot of it is isolated knowledge for students,
[00:34:24.447]and we need them to really move
[00:34:27.270]to that towards that connected knowledge.
[00:34:31.470]A few years ago,
[00:34:33.750]there was this what's called the bio skills guide.
[00:34:37.320]The competencies that is an important
[00:34:46.230]guide for instructors that are related to those core
[00:34:50.130]competencies identified in the vision and change document.
[00:34:54.900]And when I looked at the modeling one,
[00:34:56.850]when you'll notice that the words that they use for things
[00:35:00.990]that students need to be able to do are things
[00:35:03.810]like build, evaluate and revise models.
[00:35:08.130]There is interpretation there,
[00:35:10.740]but there is a lot that has to be done
[00:35:12.930]in terms of students construction of these models,
[00:35:16.260]and in terms of working with them.
[00:35:19.050]And that's really consistent with what we know about
[00:35:22.290]how people learn biology through model.
[00:35:26.760]Which is that you have this mental model,
[00:35:28.920]you have this prior knowledge that you pull into this,
[00:35:31.590]you have a model that's running around in your head
[00:35:33.480]about some concept.
[00:35:35.700]And when we ask them to build a model,
[00:35:37.607]that's some visualization.
[00:35:39.930]Now that is super important for an instructor
[00:35:42.810]to see that or for peers,
[00:35:45.180]but it's very important for the person
[00:35:47.250]who constructs that model.
[00:35:48.960]Because they, that provides the feedback
[00:35:51.750]that's essential to them to start to think about
[00:35:55.680]the evaluation and revision process.
[00:35:58.380]Nobody ever produces a perfect model the first time.
[00:36:02.580]It doesn't happen, right.
[00:36:04.920]And students need to recognize
[00:36:07.290]that that's true for us as well.
[00:36:09.900]I do this really powerful activity with students
[00:36:12.720]the first week of class, where we learn about how you learn.
[00:36:16.830]We just, we talk about how neurons form, the strength,
[00:36:20.070]they strengthen those relationships.
[00:36:22.260]And one of the things that I do during that is I show them
[00:36:26.100]a video about that you can find,
[00:36:28.800]you can just, you can search it on YouTube.
[00:36:31.860]And I show them the model that I created the first time
[00:36:35.340]I watched the video before I watched it again,
[00:36:37.560]not in slow motion or anything like that.
[00:36:40.410]And then I show them what happens
[00:36:43.740]after I watched the video again.
[00:36:45.750]And I go back and revise that model,
[00:36:47.550]because the first model was wrong.
[00:36:49.410]I had the wrong components in different parts.
[00:36:53.430]I had the wrong relationships between some of these things.
[00:36:56.640]But that was me building a model
[00:36:59.430]so that I could get feedback and inform my mental model.
[00:37:03.750]And I think showing students how you,
[00:37:06.420]how you as experts learn through this process
[00:37:10.170]is really important.
[00:37:12.000]Just showing them the final product
[00:37:13.680]that's all glossy, that's great,
[00:37:16.830]but that doesn't show them how messy it is
[00:37:19.440]in all this revision process.
[00:37:21.810]So I've done some work on model revision
[00:37:25.560]and building and revision.
[00:37:27.810]And I just wanna talk about that briefly.
[00:37:29.970]So a lot of this work with Dr. Tom Hellicar,
[00:37:33.300]has been using this cell collective software.
[00:37:37.440]And we use this a lot in introductory biology.
[00:37:39.510]All of our introductory biology students
[00:37:41.250]go through and use this process of these modeling lessons.
[00:37:45.720]Some of these might be relevant to other courses.
[00:37:48.450]So they might be,
[00:37:49.410]it might be worth by picking a gander
[00:37:51.540]and seeing if they're helpful for your class.
[00:37:54.720]They're really available lessons for modeling.
[00:37:59.040]And in these studies,
[00:37:59.910]there was a series of studies where we were comparing
[00:38:01.980]students who were doing simulations
[00:38:04.620]to ones who were building the model.
[00:38:06.360]And in this case, we're still using that prokaryote,
[00:38:08.600]in this one,
[00:38:09.433]we're still using that prokaryotic gene expression lesson.
[00:38:14.100]So the simulating students were like I had described before.
[00:38:16.830]So they read about it and they did the predicting
[00:38:19.740]of there being the results.
[00:38:21.870]And the building students did something slightly different,
[00:38:24.953]where they, before they did all that, predicting,
[00:38:27.690]observing the results, et cetera,
[00:38:30.480]they had to construct the model.
[00:38:32.010]So this was a computational model.
[00:38:34.410]So these are freshmen who are just learning
[00:38:37.440]about this system, and this is a struggle.
[00:38:40.410]And that's part of what's beneficial about this
[00:38:43.440]is that it takes time and it's tough,
[00:38:46.650]but they also have to validate that model.
[00:38:48.690]So you can't just create it. You have to validate it.
[00:38:51.470]So I just wanna show you briefly
[00:38:52.950]what the software looks like.
[00:38:54.720]So this is for photosynthesis.
[00:38:57.060]Although, so it's not the, the exact thing that we did,
[00:39:01.253]but I just wanted to give you,
[00:39:03.810]show you what the software looks like.
[00:39:06.000]And then the building students,
[00:39:08.760]they went through something slightly different, which,
[00:39:11.610]before they did that simulation,
[00:39:12.930]which is they created this model.
[00:39:14.370]So they created an image, a box scenario type model
[00:39:18.710]to try to relate things.
[00:39:20.610]Then they had to translate that into this network model.
[00:39:23.910]And most importantly,
[00:39:25.230]they had to do this evaluation and revision,
[00:39:27.720]which is to test these environmental conditions.
[00:39:31.050]And we provided like your model under these conditions
[00:39:34.800]amount of whether glucose or lactose is present
[00:39:38.760]or absent in the cell.
[00:39:40.680]And whether the lac operon
[00:39:42.000]these genes are activated or not.
[00:39:44.610]So your model should generate these results
[00:39:48.750]and students could go back then and revise their model.
[00:39:51.570]Now I will say only half of the students,
[00:39:55.367]half the students created a correct model the first time,
[00:40:00.180]and half the students did not.
[00:40:02.130]And very low percentage of those half that did not,
[00:40:05.610]they would try to revise and that process is messy.
[00:40:11.070]Going back and evaluating and revising your model.
[00:40:14.940]So that is something we're very interested in still
[00:40:19.020]trying to better understand that.
[00:40:20.700]But we do see some differences in these students.
[00:40:25.710]We see first off that the behavioral outcomes.
[00:40:29.310]So if you ask them both content knowledge,
[00:40:32.190]and you ask them to tell you
[00:40:34.440]about create models pre and post,
[00:40:37.770]that the differences between building and simulating
[00:40:39.900]are very small.
[00:40:41.400]And this should be kind of a warning.
[00:40:44.490]If you do, if you make changes to your class,
[00:40:48.480]the behavioral differences,
[00:40:49.680]the behavioral outcomes for small interventions,
[00:40:51.930]one time interventions are not big,
[00:40:54.720]probably because of that repetition principle.
[00:40:57.240]You need to be doing this a lot.
[00:41:00.870]But we do see differences in terms of cognition,
[00:41:04.290]and we see differences in how they interacted
[00:41:06.450]with each other.
[00:41:07.283]So some of these students,
[00:41:08.550]some of the times they were working in groups,
[00:41:11.700]and when they work in groups,
[00:41:14.070]the simulating students spend more time
[00:41:16.530]just talking about the model,
[00:41:18.330]and they have very, I would say
[00:41:20.280]kind of lower order discussions about the model,
[00:41:23.430]the surface reasoning.
[00:41:25.200]They're thinking about the components and the relationships.
[00:41:27.930]They're talking about them,
[00:41:29.430]but they're not really getting into the model,
[00:41:31.920]probably the way that we expect students to be.
[00:41:35.160]While the building students tend to spend a lot of time
[00:41:37.770]on the building and revising.
[00:41:38.940]This takes probably 30 more minutes,
[00:41:41.070]probably, you know, a third more time
[00:41:43.500]than the regular simulating.
[00:41:46.530]But that process of doing that really leads
[00:41:49.680]to some deep reasoning and discussion
[00:41:52.290]about the mechanisms that are underlying
[00:41:54.780]what they're seeing.
[00:41:56.340]Oh, sorry. I forgot to go ahead again.
[00:42:01.170]Yeah. So the building students, again, like the first story,
[00:42:04.650]the other stories, same behavioral outcomes.
[00:42:08.580]We're seeing students.
[00:42:10.350]What we see is not necessarily
[00:42:13.230]what's going on in their head.
[00:42:14.790]What we're seeing is output and as, and it kind of,
[00:42:19.560]it kind of masks this deep reasoning
[00:42:22.350]that might be going on for some of the students.
[00:42:26.820]So a lot of people will ask, well,
[00:42:28.680]how do we teach modeling effectively for students?
[00:42:31.530]How do we take advantage of these?
[00:42:32.700]And I would suggest this resource here.
[00:42:36.630]And so this is where I've taken these, these points from.
[00:42:40.920]But these are really tied to how students learn cognitively.
[00:42:44.850]So providing that context.
[00:42:46.950]You know, the other activity
[00:42:48.240]that I find extremely fruitful is something
[00:42:52.290]that Brian even alluded to.
[00:42:53.550]Going outside, gathering some information, experiencing it.
[00:42:57.780]And then you come back inside
[00:42:59.370]and create the model about what you just saw or observed.
[00:43:03.300]That is really powerful.
[00:43:05.010]You have made a lot of connections or you are in the process
[00:43:08.070]of making a lot of connections in that.
[00:43:10.831]Helping students to figure out what is the audience?
[00:43:14.070]Who are you making this model for?
[00:43:15.360]Is it for yourself?
[00:43:16.380]Is it for the teacher?
[00:43:17.370]Is it for a lay audience?
[00:43:20.100]Helping them figure out what is the purpose of that model?
[00:43:23.610]Identifying the components of that system is very difficult.
[00:43:27.270]We sometimes ask students the,
[00:43:29.190]we give 'em a scientific paper
[00:43:30.690]and we ask them to construct or, you know,
[00:43:33.780]think about the model that they might have.
[00:43:36.390]That's very hard for students to piece together
[00:43:40.410]what's going on.
[00:43:41.880]What's in that author's head when they were,
[00:43:45.060]when they were creating that mathematical model
[00:43:47.220]or that schematic or that yeah.
[00:43:49.920]Any kind of model that's represented there.
[00:43:52.770]And then this most importantly,
[00:43:55.020]is this reflection
[00:43:56.130]and the feedback that students are receiving.
[00:43:58.140]How do they know that the model?
[00:44:00.870]So we have to teach them how to do this model evaluation.
[00:44:05.190]The first work that I presented about the neural,
[00:44:07.410]I mean, we're really looking at how students are learning.
[00:44:10.680]But so much of it seems to be that we really need
[00:44:13.710]to provide feedback on some of these processes,
[00:44:16.380]like model evaluation and revision,
[00:44:20.250]and we need to get them building those models.
[00:44:25.770]So I just wanna conclude by,
[00:44:28.290]by kind of talking about the fact that science learning
[00:44:32.010]and learning in general, it's just a very complex process,
[00:44:35.700]but we didn't become scientists
[00:44:37.380]because we like simple processes, right.
[00:44:39.630]All of the processes we work on are very complex,
[00:44:43.200]but that doesn't mean they can't be understood.
[00:44:45.720]And individuals have a lot of variation
[00:44:48.960]in terms of the knowledge that they bring to the class,
[00:44:52.500]their ability to organize information,
[00:44:55.530]even their reasoning abilities.
[00:44:57.030]Not because like certain students are better
[00:44:59.820]at reasoning than others, but they,
[00:45:02.280]they are farther along in that development
[00:45:04.620]of some of those reasoning processes,
[00:45:08.010]probably because they've been provided
[00:45:09.600]with opportunities for that and their motivations,
[00:45:12.750]which really impacts whether or not they're going to attend
[00:45:16.680]to the information in your class.
[00:45:18.930]That is very different for a lot of students.
[00:45:22.380]There are a lot of similarities in students' brains
[00:45:25.110]in terms of these fundamental functions.
[00:45:27.630]Like we were showing that certain parts of the brain are
[00:45:30.330]activated when you're actually doing particular tasks,
[00:45:34.740]and certain parts of the brain do function
[00:45:37.830]in similar ways across all people.
[00:45:40.986]And so knowing some of that,
[00:45:43.530]about how people learn
[00:45:45.270]and how people store and retrieve knowledge.
[00:45:47.850]What your question might be doing or how that might be
[00:45:51.330]activating certain information
[00:45:54.570]for a student or certain prior knowledge,
[00:45:57.450]that is something that we need to be aware of as teachers.
[00:46:01.650]So experts use modeling because that is the way
[00:46:06.120]we communicate and use and we think about science.
[00:46:10.470]It also provides these insights
[00:46:12.450]into how students are learning.
[00:46:13.740]So that we have the experts and we have all these
[00:46:16.740]insights into how students
[00:46:18.390]are thinking and what their mental models are.
[00:46:21.810]And it's something that we can all do.
[00:46:24.120]It's something that even though it might seem
[00:46:27.300]like this should be left for the 400 level classes.
[00:46:31.380]This is something you can do and should do
[00:46:33.780]at all levels of undergraduate education.
[00:46:37.500]Because it helps students to organize the information.
[00:46:40.320]It helps them to activate those parts of the brain
[00:46:43.110]that are necessary to be functioning
[00:46:45.240]more like the scientists that
[00:46:47.100]we want them to become.
[00:46:49.470]And so with that, I'll conclude and I'm sure,
[00:46:53.220]and I'm happy to take questions that you might have.
[00:47:02.880]Awesome. Thank you very much, Joe.
[00:47:05.130]Lots to think about there.
[00:47:07.530]And I thinking about what my brain must look like
[00:47:11.250]as I'm listening to your talk, right.
[00:47:15.120]Questions can go in the chat, or if you can raise your hand,
[00:47:21.570]as people are thinking about something they want to ask.
[00:47:26.610]What's one example of how you might,
[00:47:31.260]when you get to the end of one of these units
[00:47:33.000]where you've done that, what do you use for an assessment?
[00:47:36.570]Is it the actual model or can you give us
[00:47:38.940]an example of like the test?
[00:47:41.490]Yeah. Yeah. I mean, I do different things.
[00:47:44.400]Sometimes I ask students.
[00:47:47.370]I used to do a lot more
[00:47:48.630]of having students create models on exams.
[00:47:53.400]But one of the things that I've found about that is,
[00:47:56.587]that it's very stressful.
[00:47:58.620]Like I think modeling helps you organize information.
[00:48:02.550]And so I think there needs to be an understanding
[00:48:06.900]that asking students to create something on an exam,
[00:48:13.628]that you're gonna be judged on or graded on is very hard.
[00:48:19.110]Now, and so what I've tried to move towards is providing
[00:48:23.940]some models and doing inter.
[00:48:25.860]So that we do a lot of model construction
[00:48:28.590]during the class and on assignments,
[00:48:31.920]but during the, the exam it's model interpretation.
[00:48:36.120]So explain why changes in this component are going to affect
[00:48:41.730]the relationship with these other components
[00:48:44.250]or help me under, you know, use that,
[00:48:47.640]to relate back to some of the narrative
[00:48:49.890]or the discussion, or, you know, introductions
[00:48:52.440]to that case study or to a, to a different case study.
[00:48:55.350]How would that be relevant to another case study?
[00:48:58.530]So it's a lot more of interpretation on the exam to try to,
[00:49:04.040]to lower the stress level and to help students
[00:49:08.778]kind of develop more of the using
[00:49:12.240]the model for some purpose.
[00:49:15.000]Like why, how does that help you understand
[00:49:18.330]the biology of the system, not just the model itself?
[00:49:23.160]So that's why I've tried to do with my,
[00:49:24.780]at the end of the exam.
[00:49:26.040]Although I used to do a lot more asking
[00:49:27.650]in the model on the, on exams.
[00:49:31.470]And that's variable.
[00:49:32.460]I mean, you see really good things and you see really,
[00:49:35.220]you know, not great things,
[00:49:36.053]and that, that kind of feels bad, honestly.
[00:49:40.380]The other thing I was thinking about is you were talking
[00:49:42.300]about incorrect information, but also how you had the,
[00:49:47.520]the students and the MRI looking at like,
[00:49:51.660]which one of these models is the correct one.
[00:49:56.310]Should we be, a lot of times we do give them like,
[00:49:59.910]here's the model, you know, from the textbook,
[00:50:02.430]it has the model in it, right.
[00:50:04.950]Should we be earlier than the test,
[00:50:09.690]if we're going to use the model on the test,
[00:50:12.570]be providing them with the,
[00:50:14.640]with incorrect versions to compare
[00:50:16.920]or is giving them an incorrect model bad,
[00:50:19.860]because then they've got that in their brain?
[00:50:22.860]Yeah. I mean, that's,
[00:50:24.090]that's an ongoing discussion right now, honestly.
[00:50:26.820]So I can offer my thoughts on that, but I don't,
[00:50:31.200]it's definitely not settled.
[00:50:34.140]And that is comparing things is usually better.
[00:50:40.140]You know, when we present things to students,
[00:50:42.570]they think that it's, it's all correct,
[00:50:45.000]because it's a canonical, here it is, this is the,
[00:50:47.880]and because we usually show them the end result, right.
[00:50:50.550]Like somebody spent a lot,
[00:50:52.620]had a lot of incorrect, incorrect models
[00:50:55.560]before they created that correct model.
[00:50:57.938]And, yet always show them is the final version.
[00:51:01.440]So I think it's probably better to show some incomplete
[00:51:05.010]or partially complete or ones that were, have some errors,
[00:51:09.360]but also having.
[00:51:10.830]So having two that are incorrect,
[00:51:13.350]but in different places allows them to think about,
[00:51:15.870]well, you know, is this relationship, this one or that one.
[00:51:18.900]Because I can't, you know, I have to make sense
[00:51:21.690]of what goes on there.
[00:51:24.001]And, also like creating your own that's incorrect
[00:51:27.360]and comparing it to your peer.
[00:51:29.160]Right, so I create one, you create one,
[00:51:31.380]let's compare those.
[00:51:33.090]Neither one of those is probably perfect.
[00:51:36.000]You know, can we get the best of both of those.
[00:51:39.420]Doing that before you show the canonical model
[00:51:42.840]is always gonna to be helpful to think about,
[00:51:46.919]you know, where it is helping identify errors,
[00:51:49.590]but it's also like you have to,
[00:51:52.890]you have to continue with that process of saying,
[00:51:54.930]you know, we all start with errors, right.
[00:51:57.270]Like they have to know that it's okay,
[00:51:59.610]and that it's expected that you are going
[00:52:02.100]to produce incorrect models to begin with.
[00:52:04.140]You're not going to produce gold.
[00:52:07.800]Tracy's got a question here in the chat about data
[00:52:11.370]on the high GPA learners having no more confidence
[00:52:14.070]than peers on their answers.
[00:52:16.620]She said, that was interesting.
[00:52:18.180]Do you see this in your classroom as well?
[00:52:21.360]Oh yeah, that's.
[00:52:25.350]I don't know.
[00:52:26.840]I, you know, I feel like I, I was going to say
[00:52:30.090]that I think you have some overly comp.
[00:52:34.350]I think you tend to see more overconfidence
[00:52:37.350]in mid to lower performing students,
[00:52:40.830]but I think there's a lot of variability in this.
[00:52:47.910]Yeah. I just think that there's, it's hard to, I mean,
[00:52:52.320]and that's one of the things that we're kind
[00:52:53.670]of curious about, because it's,
[00:52:55.440]it's all about this metacognition of like,
[00:52:58.470]do I really know the answer to this.
[00:53:00.390]And it's, and I think it's context specific.
[00:53:02.340]Some students are very strong in particular areas.
[00:53:05.250]And so we're still trying to kind of tease that apart.
[00:53:07.650]Are there things that they're more confident in,
[00:53:09.630]more subject areas that they're more confident
[00:53:11.730]or less confident.
[00:53:12.900]So there's probably an interaction between their GPA
[00:53:16.830]and the context area.
[00:53:19.710]Excellent. Thank you.
[00:53:20.580]And Chris has a hand up,
[00:53:22.800]so we'll let him ask a question here.
[00:53:25.320]Yeah. I wasn't sure if you'd see hands up on this.
[00:53:27.480]So I really liked your talk, Joe, and yeah.
[00:53:33.180]What you're describing is hard work, and I'm just wondering.
[00:53:37.470]I'm just thinking about how Americans learn versus say,
[00:53:40.980]Japanese students or something like that and their,
[00:53:45.493]and how quickly they give up.
[00:53:47.640]And I'm just wondering how much time do you give them
[00:53:51.480]to struggle before you help them out,
[00:53:57.390]or if you help them out at all,
[00:53:59.640]and if you have them work in teams?
[00:54:03.390]Yeah. So that, that's part of the reason we, we try.
[00:54:10.560]I have like all these different thoughts on this.
[00:54:13.380]You know, the struggle is important,
[00:54:17.790]but you're right, that you,
[00:54:19.710]if it goes on too long, that there is the giving up that's,
[00:54:24.720]you know, problematic.
[00:54:26.310]And, and so finding that fine line,
[00:54:30.120]and especially in these kinds of like lessons where this is
[00:54:33.270]more of a turnkey, like here's a lesson, right.
[00:54:37.560]So you're trying to accommodate a lot of different types
[00:54:40.470]of students in that situation.
[00:54:43.560]In my own classroom,
[00:54:44.790]I mean, I think that this happens all the time, too,
[00:54:46.980]where you're trying to balance helping,
[00:54:49.560]you know, creating that struggle,
[00:54:50.730]but also help making sure it doesn't go on too long.
[00:54:53.490]And I think that groups, working in groups
[00:54:55.950]can be very productive in this way.
[00:54:58.140]So helping students struggle a little bit individually
[00:55:01.860]then throwing them the lifeline of working with each other,
[00:55:05.130]especially before calling out students like asking students
[00:55:09.960]to report to the class or something like that.
[00:55:12.930]Letting, giving them the lifeline of,
[00:55:15.240]okay, I'm gonna struggle,
[00:55:16.740]and, but there's an accountability.
[00:55:19.740]That's a, there's a, you know,
[00:55:21.090]if they know that there's some accountability to their group
[00:55:23.880]or to, at least their partner,
[00:55:26.790]I think that is motivating to some extent.
[00:55:29.790]And then, but making sure that they don't feel
[00:55:32.340]like they have nothing, right.
[00:55:35.700]Like, I think we have to get away from this idea
[00:55:38.580]that there's either all or nothing.
[00:55:40.170]Like that's either right or wrong.
[00:55:41.850]Like they all have parts of that information is correct.
[00:55:45.390]And we need them to present that information. right.
[00:55:47.970]That part that's correct, 'cause then we can build on that.
[00:55:50.880]And so many students we've our educational system,
[00:55:54.990]has hemmed people into this idea
[00:55:56.430]that it's either right or wrong.
[00:55:58.650]And, so I think that modeling is one way that we,
[00:56:04.080]that I think that is helpful to help.
[00:56:06.060]You know, here present some of your ideas.
[00:56:07.950]Some of these are correct,
[00:56:09.270]and some of these are incorrect,
[00:56:10.530]but show it to us, right.
[00:56:12.240]So often we grade an exam and it's like, you know,
[00:56:14.670]this many are wrong or this many are right.
[00:56:16.710]But that doesn't show us really the gradation of knowledge
[00:56:19.740]that's possible that there is
[00:56:21.720]a lot of correct information there.
[00:56:23.970]It's not just wrong.
[00:56:26.220]Sometimes, sometimes it is right.
[00:56:28.020]But often they have the right pieces of information
[00:56:31.350]in the wrong order or.
[00:56:33.120]And I think that gets helped out by working with some other
[00:56:36.720]people, like talking it out, presenting this.
[00:56:39.660]You know, we talk about socially mediated metacognition now.
[00:56:42.780]And that's a kind of a new thing that we've been talking
[00:56:45.120]about where you're kind of sharing your ideas and getting
[00:56:48.180]other feedback on that in order to revise your own mental
[00:56:52.380]model, your own model of what's going on.
Log in to post comments