Joe Dauer - Modeling to Learn Biology

Joe Dauer Author

10/16/2017 Added

70 Plays

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Research on how students construct and interpret biological system models

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[00:00:00.966]So, thank you all for coming and this talk today is gonna
[00:00:04.680]be about the research that I've been doing on how students
[00:00:09.396]are learning biology using these schematic models
[00:00:14.486]and also some computational models.
[00:00:17.982]I want to make sure to acknowledge the people who have been
[00:00:19.818]really supportive of this work over the years that I've been
[00:00:23.757]working on it, and helped us get to where we are.
[00:00:27.214]So, a lot of these individuals have gone on to bigger and
[00:00:31.724]better things and then there are a lot of the
[00:00:34.643]colleagues who have been very important to this work.
[00:00:38.170]Tomas Helikar who is the person I have this grant with
[00:00:41.768]that we work on computational modeling, my postdoc
[00:00:45.940]Gretchen who is back here, and Sarah my graduate student.
[00:00:50.187]And then there is a couple of undergraduate UCARE students
[00:00:53.074]here, Heather she was the former postdoc and you'll see
[00:00:56.199]a lot of her work, I talk a lot about her work here,
[00:01:00.592]and then this work with Bob Mayes has been on quantitative
[00:01:05.297]modeling, so I probably won't get a chance to get to that,
[00:01:07.984]but if you have questions I'd be happy to talk more
[00:01:10.216]about our work on quantitative modeling.
[00:01:13.638]So, if you saw in the opening slide, my title
[00:01:16.751]is Assistant Professor of Life Science Education.
[00:01:19.971]For many people, that's not really clear what that means.
[00:01:22.713]Life Science Education research?
[00:01:24.928]And so I decided to, this is a quote from the National
[00:01:27.442]Research Council, a discipline-based education
[00:01:29.853]research report from a few years ago.
[00:01:32.649]So, this field combines the expertise of scientists
[00:01:35.558]and engineers with methods and theories that explain
[00:01:38.546]learning and it investigates learning and teaching
[00:01:41.550]in a discipline from a perspective that reflects
[00:01:43.746]the discipline's priorities, worldview,
[00:01:46.550]knowledge, and practices.
[00:01:48.863]And so for me, what that means
[00:01:50.241]is that it's at this intersection of where I kinda work
[00:01:53.762]is at this intersection of biology and mathematics
[00:01:56.463]because those are disciplines that I'm very interested in,
[00:01:59.573]and student learning, so where are students
[00:02:02.062]or how are students learning and then using that to inform
[00:02:06.459]our pedagogy, how we're teaching biology or life sciences.
[00:02:10.590]So that's just to kind of give you an idea
[00:02:12.225]of the landscape of where I am.
[00:02:14.741]So today, I'm gonna be talking about modeling
[00:02:17.154]as this essential process in science and how
[00:02:22.194]there's this need to improve modeling pedagogy
[00:02:25.100]for our undergraduate students.
[00:02:26.651]Talk a little bit about the research that we've been doing
[00:02:28.624]on how modeling can provide insights into students'
[00:02:31.535]knowledge about biological processes,
[00:02:33.981]and biological systems,
[00:02:35.398]and then talk about how technology has afforded us this
[00:02:40.702]ability to research some really particular parts of modeling
[00:02:45.524]including the social interactions during modeling and
[00:02:48.575]metacognition and getting more at what are students doing
[00:02:52.779]while they're in this process of constructing models.
[00:02:57.348]All right, so research in the 21st Century
[00:03:00.032]really is this cycle of innovation that's going on
[00:03:05.315]in terms of trying to understand biology
[00:03:10.509]from multiple perspectives.
[00:03:11.812]So, we have biology providing all these new data
[00:03:16.235]that can be answered involving new technologies.
[00:03:21.069]So that leads to more computation and development of new
[00:03:24.782]software in order to develop new hypotheses in biology.
[00:03:28.349]So there's this cycle of innovation that's going on,
[00:03:31.300]and answering and developing new questions in biology.
[00:03:35.111]And so this is where we are preparing our students to go,
[00:03:38.419]this is the workforce that they are going forward to.
[00:03:42.233]And I would say that for the most part,
[00:03:44.426]we spend the majority of our time focusing on this,
[00:03:47.044]in the biology realm, and trying to teach students
[00:03:50.200]with the expectation that later on,
[00:03:52.948]either in their undergraduate career or graduate career,
[00:03:58.424]they'll develop the interaction
[00:04:02.342]with technology and computation.
[00:04:04.412]And I would say that this is probably doing our students
[00:04:06.535]a disservice if we are not engaging our students
[00:04:08.605]in these other aspects of systems biology.
[00:04:15.306]Six years ago, the AAAS released this report
[00:04:20.866]that was a compilation of work from around 600 scientists
[00:04:26.405]trying to develop these core content areas,
[00:04:30.659]so they call it the Core Concepts
[00:04:32.276]and also Core Competencies, or the skills and abilities of
[00:04:35.459]students to understand these Core Concepts.
[00:04:39.260]And so this is a national call to action to improve
[00:04:43.356]biology education in the U.S.,
[00:04:47.873]and so I focused my time really on these Core Competencies
[00:04:51.624]trying to, in particular, focus on how students reason
[00:04:54.365]quantitatively and how they model and simulate
[00:04:56.608]relative to these different Core Concept areas.
[00:05:01.216]One of the things that this resulted in,
[00:05:04.900]this call to action, has been on the left
[00:05:08.512]is this table of these Core Competencies, and so for
[00:05:11.440]example, there's the ability to use modeling
[00:05:14.209]and simulation, that was the Core Competency
[00:05:16.080]that they identified, and the way that you would demonstrate
[00:05:19.079]that proficiency, is to use mathematical models
[00:05:22.505]and simulations to describe living systems.
[00:05:26.491]That's what they described as the demonstration
[00:05:28.958]of that competency.
[00:05:30.143]The reality is, for students, that they are provided
[00:05:32.897]with models, a lot of text books are loaded with models,
[00:05:37.904]either schematics like this or mathematical models,
[00:05:41.774]and asked to interpret them in some way.
[00:05:43.587]Sometimes there's some scaffolding,
[00:05:45.155]but rarely are we actually identifying that students
[00:05:49.471]are competent in being able to use modeling and simulations.
[00:05:55.377]So there's this significant gap between this call for action
[00:05:58.253]and reality for students in what they're actually doing.
[00:06:01.779]And I would say in order to get students closer to
[00:06:03.954]that understanding the system's biology,
[00:06:06.602]we need to be closing that gap significantly.
[00:06:11.633]So, we look around the room, we're all scientists
[00:06:14.912]in here, I think we're all scientists,
[00:06:17.758]I don't know, yeah, it looks like it.
[00:06:20.216]And we all use models in different ways.
[00:06:22.615]We maybe use them in multiple ways.
[00:06:25.670]But models are used by scientists to help explore
[00:06:29.117]these complex systems or unknown possibilities,
[00:06:32.859]to generate these causal explanations
[00:06:34.729]and make accurate predictions.
[00:06:36.922]But for most of our students, this is not transparent
[00:06:39.076]that this is the way models are used by everyday scientists.
[00:06:44.991]So they might, depending on our perspective,
[00:06:48.208]we might identify one or another of these
[00:06:50.862]and basically that's how we teach.
[00:06:52.675]We teach that as more important than the others.
[00:06:55.602]Not because we actually think that it's more important,
[00:06:57.817]but because we tend to get into our narrow focus,
[00:07:02.315]and it doesn't make it very transparent for our students.
[00:07:06.132]And so I would say that modeling for us as scientists
[00:07:10.122]is a way of knowing science.
[00:07:11.630]We think of it as a way we explore science,
[00:07:14.777]and as a way to understand it better,
[00:07:17.932]and probably to develop these, as Windschitl would point out
[00:07:21.296]to develop these defensible explanations
[00:07:23.124]of the way the natural world works.
[00:07:25.396]And I would say, if our goal then we need to really
[00:07:29.105]be thinking about how do we get students to understand
[00:07:32.352]modeling as this process, that it does all these things
[00:07:36.130]and that it's important as a way of knowing science.
[00:07:39.146]Not just as something that they do in the classroom
[00:07:42.169]because the teacher asks them to, or requires
[00:07:45.164]them to do that for the exam.
[00:07:47.311]But because that is the way we know science.
[00:07:51.898]So, my research program is really focused
[00:07:55.716]on this and investigates how we can transform the way
[00:08:02.482]undergraduate biology students
[00:08:03.912]learn biology and learn about complex living systems
[00:08:07.983]by trying to understand how they learn both the content
[00:08:10.791]and their modeling knowledge.
[00:08:12.521]And, in developing tools and pedagogy to improve
[00:08:15.590]modeling in the classroom.
[00:08:16.999]So I'm really interested in how we can
[00:08:19.535]develop this pedagogical way teaching tools
[00:08:22.761]based on the way students are learning both
[00:08:24.831]the content and modeling.
[00:08:27.973]And my study population, I do most of my work here
[00:08:31.087]with undergraduate students at UNL,
[00:08:33.285]and most often they're in introductory biology,
[00:08:36.016]and usually in a lab setting
[00:08:38.452]in which they're completing modeling exercises
[00:08:41.409]associated with the content of the course.
[00:08:43.877]So we're not asking them to be constructing models
[00:08:47.035]as I was saying modeling is part of this process
[00:08:50.536]of knowing science so we're not asking
[00:08:52.709]them to be doing it as a stand alone activity
[00:08:56.261]that's not related to the content.
[00:08:58.858]So it's not divorced from the content
[00:09:00.542]that they're learning in the class.
[00:09:02.517]And I think that's important because that's where you start
[00:09:04.728]to get that intersection of the biology
[00:09:06.802]and the student learning.
[00:09:10.067]I need to make a side trip here really quick
[00:09:12.646]because it's really pertinent to the way I
[00:09:14.994]think about the research that I'm doing.
[00:09:17.886]That is to describe how memories are formed
[00:09:20.758]and how we use that information relative to biology.
[00:09:25.281]If we think about any kind of input,
[00:09:29.098]any kind of sensory input, smells, or touches, or sight
[00:09:34.770]all those things go into the sensory memory,
[00:09:37.369]and until we attend to those
[00:09:39.239]they don't move into the working memory.
[00:09:40.835]You recognize right now that you are sitting
[00:09:43.407]in a seat, and you might have your hands on your desk,
[00:09:45.933]but until you attend to that,
[00:09:47.697]it's not actually being moved into your working memory.
[00:09:52.286]It's there, it's information that your brain recognizes,
[00:09:55.079]but until you attend to it
[00:09:56.432]it doesn't get moved into the working memory.
[00:09:58.739]And then additionally, until it's encoded in some way,
[00:10:01.549]it's not stored into your long-term memory.
[00:10:04.449]The encoding is a really important process.
[00:10:07.717]How we store it in the long term memory
[00:10:09.872]is important for how we're going to retrieve that memory.
[00:10:12.801]For example, how many people here know
[00:10:14.669]what they ate for dinner last night?
[00:10:18.624]That's it?
[00:10:20.099]Okay, how many people remember from two days ago?
[00:10:24.989]Okay, and how many people remember
[00:10:27.110]what you had for dinner last Wednesday?
[00:10:30.029]Okay, right?
[00:10:31.626]So there's no reason you would remember
[00:10:33.912]what you had for dinner last Wednesday.
[00:10:35.949]You may, and there may be if we had a larger crowd,
[00:10:38.263]we may have found a few people
[00:10:40.319]that actually had that memory.
[00:10:42.520]And the reason you would store that memory
[00:10:44.285]is probably associated with something relevant
[00:10:48.668]at that dinner.
[00:10:49.779]Maybe it was an anniversary, or maybe you had
[00:10:51.284]a special bottle of wine, maybe it smelled particularly
[00:10:54.136]good, but there was some reason, there was cues
[00:10:56.787]associated with that that allowed you to store it
[00:10:59.041]in long-term memory, and those are important because now
[00:11:02.097]that's how you retrieve it.
[00:11:04.537]I asked you questions about the dinner last Wednesday
[00:11:06.774]and you could try to retrieve
[00:11:09.140]that memory based on those cues.
[00:11:13.256]What that means for us in terms of biology
[00:11:16.183]is that I think about it in terms
[00:11:18.165]of these two neighborhoods here.
[00:11:20.620]If the method of encoding is equal to
[00:11:22.573]the method of retrieval,
[00:11:24.085]so the way you cue it either in storage
[00:11:26.543]or retrieval, that's really important.
[00:11:28.939]So if you store knowledge and if students
[00:11:31.465]store knowledge in these kind of cul-de-sacs
[00:11:34.036]in these little entities, small entities
[00:11:36.261]then the only way to retrieve it
[00:11:39.037]is by cueing perfectly back to that same location.
[00:11:43.698]There's very few ways to interconnect it
[00:11:47.584]to get back to that same piece of knowledge.
[00:11:51.693]Whereas if you have a very interconnected knowledge,
[00:11:58.079]so if things are connected to each other in different ways,
[00:12:00.662]as in this kind of neighborhood here,
[00:12:03.677]for example the dinner that you had last week
[00:12:05.812]you might be able to cue on the fact of the
[00:12:09.140]smell or how it tasted, or these particular dates,
[00:12:13.393]all these things that are important
[00:12:15.922]and you're more likely to retrieve that memory
[00:12:18.075]than if you had stored it as a single entity.
[00:12:22.845]That's really important because we think about this
[00:12:26.157]in terms of biology knowledge,
[00:12:27.915]if you store knowledge, if you store a fact,
[00:12:30.150]so you can store it in long-term memory
[00:12:32.120]but the only way you're gonna elicit that
[00:12:34.060]on a quiz, or an exam or later when you need it
[00:12:37.877]is if you cue it perfectly.
[00:12:40.459]That's often what we see with students,
[00:12:42.136]is that we aren't able to cue it perfectly.
[00:12:44.593]They have the knowledge, they probably have stored it
[00:12:46.289]in the long-term memory,
[00:12:47.630]but we're not cueing them appropriately.
[00:12:50.242]And so we think that, or I think,
[00:12:53.423]I don't want to draw anyone into this with me
[00:12:55.336]but that if you provide a lot of connections
[00:12:58.598]for that information, so if you provide
[00:13:00.788]multiple ways to understand, for example,
[00:13:03.113]the idea of an allele,
[00:13:04.753]that there's multiple things attributed to it
[00:13:07.339]so what leads to an allele, what does it do,
[00:13:09.664]those all provide access points or cues
[00:13:12.275]that get you back to that idea.
[00:13:15.215]So this is kind of the basis of the way I'm thinking
[00:13:18.325]about the cognition for students learning in this field.
[00:13:23.855]And I think about what I'm gonna talk about today
[00:13:26.180]are these three types of models, and as I think about them
[00:13:29.893]as hierarchical models, that there's this level one,
[00:13:32.466]which is concept mapping, so that's understanding that
[00:13:34.588]current model, what does that neighborhood actually look
[00:13:37.356]like, what do they actually know for information.
[00:13:41.772]I think about simulating systems, so how do they compare
[00:13:44.784]their mental models to data?
[00:13:46.883]So when they're representing data, and they're seeing
[00:13:49.006]this fluctuation of data, how does that influence what their
[00:13:54.960]mental model is, so how does it change a little bit.
[00:13:57.768]And then this last level is building these computational
[00:14:01.755]models, so this analysis and revision of your model of
[00:14:04.797]the system, how is that changing as you're inputting
[00:14:07.225]more and more data into your knowledge of it.
[00:14:14.483]So I'm gonna ask questions relative to each of these
[00:14:16.976]different types of models that we've been working on
[00:14:19.705]and investigating, and then at the end hopefully if there's
[00:14:22.205]more questions, to go in more depth,
[00:14:25.116]I would be happy to go into greater depth.
[00:14:27.526]But the first question is, can concept maps be used
[00:14:30.467]as a metric to quantify these changes
[00:14:32.850]in students' biology knowledge over time?
[00:14:35.418]So these concept maps provide this opportunity to
[00:14:38.406]really emphasize the relationships and functions of these
[00:14:42.312]biological systems, so we want to be able to use a metric,
[00:14:47.213]or have a metric that allows us to interpret whether
[00:14:49.999]how does that represent their biology knowledge?
[00:14:53.588]So this is a student model of a cellular respiration.
[00:14:57.763]There are multiple aspects to this, so there's the
[00:15:00.378]structures in boxes, they're connected with arrows that are
[00:15:03.672]labeled with these relationships among them.
[00:15:06.677]And then we asked students to build those models with
[00:15:10.850]relationships, but there are some cases where they don't
[00:15:13.871]describe the relationships, and so we look at the number of
[00:15:19.783]boxes, we look at the number of arrows,
[00:15:21.900]we look at the number of described arrows or relationships.
[00:15:24.661]We also look at the quality.
[00:15:27.871]So these numbers in brackets here, these represent the
[00:15:30.981]what we score the relationship in terms of whether it's
[00:15:36.677]incorrect, valid or interesting but not totally biologically
[00:15:41.142]correct, or three, which would be
[00:15:43.258]what we would expect a student might come up with
[00:15:46.308]as the best possible answer there.
[00:15:48.242]So we look at the average of their model, about the
[00:15:50.384]correctness of that model, and then we also looked at
[00:15:52.411]the connectivity of the model, and sort of look at whether
[00:15:55.281]or not, how did they build their models in terms of
[00:15:58.236]connectivity in terms of multiple arrows, multiple out
[00:16:02.090]arrows, like multiple effects or multiple causes of those
[00:16:04.674]of certain phenomena.
[00:16:10.683]And so the first context we'll be talking about is that of
[00:16:12.815]cellular respiration, so this just a rehash for those
[00:16:15.947]that aren't, it's not at the tip of your tongue.
[00:16:18.694]Basically it's the breakdown of glucose into energy,
[00:16:21.438]ATP to be used by the cell for work.
[00:16:25.280]And so you have these inputs of glucose and oxygen,
[00:16:27.586]and these products of ATP, carbon dioxide and water.
[00:16:34.134]So one of the first things we were interested in is
[00:16:36.721]understanding what is their current mental model about
[00:16:41.039]cellular respiration for introductory biology.
[00:16:43.288]So we asked students to create these models,
[00:16:45.966]and I'm interested in what does that tell us about
[00:16:48.519]what they know and where are their gaps in their knowledge.
[00:16:51.891]And one of the first things we found is that they
[00:16:54.084]really do have a pretty good understanding of glycolysis
[00:16:57.994]and the citric acid cycle, which for those of you who aren't
[00:17:00.330]as familiar, those are early processes in the cellular
[00:17:03.274]respiration, those are early on in the breakdown of the
[00:17:05.852]glucose, and the electron transport chain in fermentation
[00:17:09.919]show up less frequently and they have lower quality
[00:17:13.052]relationships in their student models.
[00:17:17.162]And I think this highlights both a students' knowledge
[00:17:19.599]and where our instructional practices are.
[00:17:22.142]Our instructional practices, from a different study,
[00:17:24.045]we find that the instructors spend more time talking about
[00:17:29.217]glycolyses and citric acid cycle than they do
[00:17:32.755]the electron transport chain and fermentation.
[00:17:35.641]And I think that's reflected here, so as students'
[00:17:37.842]the repetition of it, and the abundance of time spent on it
[00:17:44.764]means that they just don't associate as well
[00:17:47.065]with those other processes.
[00:17:51.344]So I'm gonna talk about how simulating systems can provide
[00:17:56.101]these opportunities to emphasize the dynamics that are
[00:17:58.438]going on, so these biological systems are not
[00:18:00.638]static entities, so that cellular respiration obviously
[00:18:03.828]that's a system that's constantly in flux,
[00:18:06.332]and that is part of the reason we've been interested in
[00:18:08.789]the simulation models and building computational models
[00:18:12.533]to try and improve this students' understanding
[00:18:15.806]of the dynamics, and so we asked this question,
[00:18:18.526]"Can simulating models improve student's abilities
[00:18:21.587]"to explain these system dynamics?"
[00:18:23.676]So I need to talk about briefly this platform
[00:18:28.682]that we've been using, this was developed by Dr. Helikar,
[00:18:31.431]and this platform is a web-based platform.
[00:18:35.512]It's both used by researchers in biochemistry,
[00:18:38.741]as well as this learned collective portion of it is used by
[00:18:43.981]instructors for classroom dissemination.
[00:18:48.511]And you don't have to enter the mathematical expression.
[00:18:52.309]It uses Boolean logic models for those that are interested
[00:18:57.486]in the research side of it, it's definitely open to that
[00:18:59.555]and you can change all the expressions, etc. but you
[00:19:03.651]don't have to and so that's nice for students where they
[00:19:06.704]aren't required to necessarily apply the mathematics,
[00:19:11.332]or understand the mathematics
[00:19:13.963]that's going on behind the scenes.
[00:19:17.898]And so these models provide like many other simulations,
[00:19:21.993]there's the inputs over here where you can change things,
[00:19:26.168]and this is a food-web dynamic model where you can change
[00:19:29.414]things like the deer disease, and coyote disease,
[00:19:32.383]and then you have this network model where you can see
[00:19:34.867]these colors are changing as certain things are turned on,
[00:19:38.102]are activated or deactivated, and you have some output
[00:19:42.096]of data, this one representing what happens.
[00:19:45.499]I think the deer disease is turned on and all of a sudden,
[00:19:48.358]so what's the impact on different organisms,
[00:19:50.985]or in this case different organisms or different components
[00:19:53.492]of the system as you change, as you perturb the system.
[00:19:57.331]And there are a lot of these different available for
[00:20:02.166]dissemination, so if people are interested in these,
[00:20:04.769]we definitely have an interest in trying to get them
[00:20:08.720]out there and having people use them.
[00:20:10.519]There's different types, again we're looking at both
[00:20:12.699]concept map simulations and then also this construction
[00:20:16.184]portion of it, and I'll talk about that in a little while.
[00:20:20.716]So one of the first things we did with this software after
[00:20:24.829]we kind of have an understanding of where they are
[00:20:27.184]in terms of their cellular respiration knowledge,
[00:20:29.414]was to compare it to the program that was already in use
[00:20:32.012]in the classroom, in the lab settings at UNL.
[00:20:36.178]And that was this one on the left, this explorer which was a
[00:20:38.793]sim bio lesson that students did, so it's a simulation that
[00:20:44.628]students were doing in the classroom.
[00:20:47.751]There's a lot of similarities to the model that we have.
[00:20:50.714]There's simulation of these processes, they observed
[00:20:53.709]fluctuating data, there's also these cartoons about
[00:20:56.437]what's going on as enzymes move here and there, etc.
[00:20:59.444]And they had these multiple choice assessments of how
[00:21:02.972]students were working.
[00:21:04.961]The discover model that we put together is a little more
[00:21:10.234]based on how students are learning with the
[00:21:13.058]modeling process, so there's a lot more prediction,
[00:21:15.675]and observation, and mechanistic explanation
[00:21:19.482]that's available when you do these free response or
[00:21:23.132]open-ended questions, and then there was different types
[00:21:26.825]of outputs so that you were noticing there,
[00:21:28.777]there was numeric and graphical outputs of the data
[00:21:31.767]in order to get students thinking about
[00:21:33.826]how the system is working together.
[00:21:39.155]And again we did pre and post concept maps,
[00:21:42.553]or conceptual models, so this was a example from that
[00:21:46.360]study there in which we were looking at how their models
[00:21:51.351]change over time on all these metrics
[00:21:53.558]that I was mentioning earlier.
[00:21:57.011]And what we're seeing is that when we look at the
[00:22:01.705]pre post models, there's significant changes in a lot of
[00:22:06.491]the components of their model, so both the quantity
[00:22:09.165]and quality of their relationships are changing with
[00:22:13.506]different amounts of effect size.
[00:22:15.417]So we know that students are actually understanding some
[00:22:17.189]of the system dynamics and they're doing better.
[00:22:20.377]It's not totally surprising that after you spend time
[00:22:22.979]working on a lesson over time, that this would impact the
[00:22:27.880]size of your model, or the interconnectedness of the model.
[00:22:31.894]One interesting thing that we found was that
[00:22:33.869]if we look at how it impacted the long-term knowledge,
[00:22:37.513]so we would think about the long-term knowledge as
[00:22:40.052]how they performed on the exams, so we looked at actually
[00:22:42.991]comparing those two different groups,
[00:22:45.831]we find that there was actually, this is thanks to some
[00:22:50.573]work with Chris Jasinski here, we appreciate that,
[00:22:55.279]that there was this significant increase in an students'
[00:23:00.537]performance on the exams when they use this discover model
[00:23:03.972]with these more computational models.
[00:23:07.346]So we would say that it positively impacted their
[00:23:09.303]performance, even though it's a small amount.
[00:23:11.423]It is a small amount, so out of 20 questions
[00:23:14.828]an average student answered an additional one question
[00:23:19.629]correct, and so that's not a whole lot but it's not worse,
[00:23:24.433]which is great for us.
[00:23:26.117]But one of the key things is, okay, so it's not worse,
[00:23:28.735]but we think that they really learned some of these new
[00:23:31.608]skills about model interpretation that were important,
[00:23:34.642]and they're really important for the next study
[00:23:36.526]that we were doing, which is that during the series
[00:23:40.011]in the class, they actually do the cellular respiration,
[00:23:44.560]they do another module on cell cycle, and then they do
[00:23:48.307]one on the lac operon, which is one in which they
[00:23:52.266]actually have to construct a model.
[00:23:54.137]And so I'm gonna be getting to that point in a moment.
[00:23:57.668]One of the things that we also did was developed a really
[00:24:01.268]small set of multiple choice questions to look at their
[00:24:04.140]understanding of system dynamics, and in this one
[00:24:07.768]we find overall what you would expect I guess
[00:24:10.708]that students can predict these changes
[00:24:14.802]in stocks and flows, so we were looking at stocks and flows
[00:24:17.464]very general understanding of stocks and flows,
[00:24:20.703]and most students already get this idea that
[00:24:25.164]if you change the amount of glucose,
[00:24:26.913]how it impacts glycolysis or the citric acid cycle.
[00:24:30.801]Students don't have that difficulty in connecting very close
[00:24:33.660]a change in an inflow to the change in the stock.
[00:24:38.384]And that's represented here in this middle bar here.
[00:24:44.553]And then the last bar actually as well.
[00:24:48.895]Where there is a huge change is a relative to this question
[00:24:53.793]here, where there's a change in the outflow.
[00:24:56.842]So for example when you say how does a mutation
[00:25:00.103]in the electron transport chain, so that's the outflow,
[00:25:04.024]the production of the ATP, how does that impact glycolysis?
[00:25:07.560]So how does that change something that's going to impact it?
[00:25:10.419]So you're actually kind of going in reverse,
[00:25:12.582]if you have a build up of ATP, how does the system respond?
[00:25:18.629]Students do very poorly, but they definitely improve in
[00:25:21.694]their ability to understand how does that outflow impact the
[00:25:25.277]system, and also in explaining the mechanism of a phenomena.
[00:25:32.514]And so the last study I wanted to talk about was this one
[00:25:35.126]on building computational models, and I'm really interested
[00:25:38.850]in what the cognitive processes are that students
[00:25:41.676]go through when they're building these models.
[00:25:43.590]So what are they actually, how are they processing
[00:25:45.480]model-building in their head?
[00:25:47.815]And so, like I was mentioning, we changed the context here,
[00:25:52.084]so this context is relative to prokaryotic gene regulation
[00:25:55.617]and so you may be like me and not be particularly familiar
[00:25:59.499]with this topic, but you quickly learn this when you're
[00:26:02.879]working with an interdisciplinary group.
[00:26:04.950]So if there are some people here that this is their forte,
[00:26:09.931]I apologize (laughs), I'll probably not do it justice.
[00:26:14.635]So in prokaryotes, the case study that we used is that
[00:26:20.117]there is E. coli bacteria in your gut and they're responding
[00:26:22.347]to these environmental cues, both lactose and glucose,
[00:26:25.177]to turn on or off these genes.
[00:26:28.221]And in particular, in prokaryotes they have these groups
[00:26:32.307]of genes are called operons that are turned on or off
[00:26:35.033]collectively, and they're operated by these switches
[00:26:38.453]which are, where is this mouse.
[00:26:43.068]So there's two, they work in concert, so you have these
[00:26:47.675]repressors, or activators that operate, and those switches
[00:26:52.688]have to be turned on or off in order
[00:26:54.919]for these genes to be activated.
[00:27:00.368]So that's different than a eukaryotic system in which
[00:27:02.694]we have genes and multiple chromosomes that are turned on,
[00:27:05.473]they still have switches in eukaryotes,
[00:27:07.069]but they work slightly differently.
[00:27:09.147]And then the genes are in this particular case,
[00:27:13.633]the genes resulted in the proteins that import
[00:27:16.123]and metabolize lactose into glucose and galactose.
[00:27:18.982]So the switch being on or off is determined by the presence
[00:27:23.803]or absence of glucose and lactose.
[00:27:28.544]And the experimental setup was that students in some
[00:27:31.047]sections of the class were asked to investigate this model,
[00:27:35.380]so they were provided with a model, and some students
[00:27:39.254]were actually asked to build this lac operon model,
[00:27:42.788]and I'm gonna walk through what that means.
[00:27:44.636]And importantly to validate and revise their model
[00:27:47.367]as they were building it, so that's a common process
[00:27:49.598]for us when we're building models,
[00:27:52.064]is that you're gonna go through and revise it
[00:27:54.059]based on as much information as you have.
[00:27:56.099]For many students, building a model this is probably
[00:28:00.278]one of the first times they've built it.
[00:28:03.021]And we have the opportunity with the technology
[00:28:05.002]to go back and say whether or not that model is functional.
[00:28:08.645]This is one of the biggest changes in my research program
[00:28:11.970]has really been this movement from conceptual modeling
[00:28:15.535]which is very difficult to validate for a student,
[00:28:18.409]to a computational model, one where you can actually
[00:28:21.689]say yes, it's functional or not functional,
[00:28:24.162]and go back and revise your model until it is functional.
[00:28:27.569]So the students had some similarities between
[00:28:30.295]the two groups in terms of building a pre and a post
[00:28:33.264]conceptual model, and then both groups of students
[00:28:37.300]had this lesson in which they were predicting,
[00:28:39.626]explaining, observing, and then reflecting back
[00:28:43.660]on their predictions and simulations.
[00:28:47.617]So for the building students, this is what it looked like.
[00:28:50.969]They still built a conceptual model here,
[00:28:53.281]and there's the environmental glucose and
[00:28:57.285]environmental lactose levels, and how those would
[00:28:59.375]impact their model, and then they translate that into this
[00:29:02.421]network model in which they can simulate.
[00:29:06.311]And then they actually are supposed to test that,
[00:29:08.285]so you have different levels of glucose and lactose
[00:29:10.905]in this table, and there's the observed value
[00:29:14.116]whether it's supposed to be off or on the lac operon.
[00:29:18.021]And they're supposed to go through, and they can do up to
[00:29:20.202]five tests in which they report what their results are
[00:29:23.074]when they simulate it, and then if it doesn't match,
[00:29:26.387]they're supposed to go back and
[00:29:28.392]report their revisions to their model.
[00:29:30.448]So these are self-report, which is important,
[00:29:32.781]self-report revisions to their models.
[00:29:37.589]And so just to give you an idea of what that looks like,
[00:29:39.700]so this was actually, so the students in the investigate
[00:29:43.071]would have been provided with this model,
[00:29:44.707]and they would be provided with a network model.
[00:29:47.258]And eventually the build students at some point they say,
[00:29:49.986]"I'm done," you know, "I'm moving onto the lesson part,"
[00:29:53.362]and so they are also provided with this next setup.
[00:29:57.991]But they have to build their model before that.
[00:30:02.486]We thought about many times incorporating that
[00:30:04.556]where you can't move on until you have a functional model,
[00:30:07.089]and for some other pedagogical reasons,
[00:30:10.473]we haven't done that because we think that's very Draconian
[00:30:14.265]to say that you can't move on until you
[00:30:16.634]accomplished what we want you to do.
[00:30:18.960]So it's kind of an interesting thing.
[00:30:23.149]So in this model they build their concept map,
[00:30:26.605]and then they build their computational model here,
[00:30:29.125]so they're building their simulatable model,
[00:30:33.925]then they go in and they can simulate, they can change
[00:30:36.073]the amount of glucose and lactose,
[00:30:38.044]which is what this is doing here,
[00:30:40.259]and they get some results, they find that they change
[00:30:45.129]their glucose and the lactose level, and it goes on.
[00:30:50.253]Well, this isn't the proper functioning for this model.
[00:30:52.820]So they go back and they revise this model,
[00:30:56.873]they make some small change to that arrow to make it
[00:30:59.827]inhibitory instead of excitatory,
[00:31:02.083]then they go back in and test the simulation of that model
[00:31:05.027]with these environmental conditions, and they're gonna find
[00:31:08.431]that this works out the way it's supposed to.
[00:31:11.125]So because this was us building this model,
[00:31:15.093]this didn't take very long.
[00:31:16.563]Usually students spend about 30 minutes or so working on
[00:31:20.089]this particular portion of it, building a model,
[00:31:22.647]and simulating and evaluating their model.
[00:31:25.916]So this is a time-consuming portion of the lesson for sure.
[00:31:31.483]So we see these changes in their concept maps,
[00:31:34.923]but they're all changes from pre to post.
[00:31:37.131]There's no differences between building and investigating
[00:31:39.709]students in terms of their
[00:31:42.394]conceptual understanding of a system.
[00:31:44.381]So there's small changes you'll notice in here,
[00:31:46.731]but there's nothing is significant except from pre to post.
[00:31:50.362]All of these are significant changes from pre to post,
[00:31:53.055]but those are all changes, except for correctness.
[00:31:56.152]Well, the structures and relationships you would kind of
[00:31:59.375]expect again, like we discussed previously, but correctness.
[00:32:02.716]It's interesting that they actually improve in their
[00:32:04.987]correctness, so they actually understand better how all
[00:32:07.301]the relationships are going together in their model.
[00:32:10.287]But one of the things that we've been doing more recently
[00:32:13.897]is looking at whether or not they
[00:32:16.068]can actually build a functional model.
[00:32:18.220]So we can go back to some of the students' work,
[00:32:22.453]and we can figure out whether they built a model that
[00:32:25.430]actually meets those criteria like it was supposed to.
[00:32:28.203]Half the students can build a functional model.
[00:32:30.375]So that's great.
[00:32:31.406]So half of our students can build a functional model
[00:32:33.745]having never done this before,
[00:32:35.604]and I think that's encouraging.
[00:32:37.722]Half of our students are not able to do that,
[00:32:40.689]and approximately a quarter of the students, so about half
[00:32:43.530]of this group here, they were really only one or two steps
[00:32:46.575]away from actually being able to create a functional model,
[00:32:50.179]if they had just clicked, made something inhibitory,
[00:32:54.912]they would have gotten the correct model.
[00:32:56.218]So they were really close.
[00:32:58.081]And those ones, you can really work with them, because
[00:33:00.662]that's not hard to do.
[00:33:02.885]There are a quarter of the students
[00:33:04.952]that pretty much built nonsensical models.
[00:33:08.821]And so that's a tougher group, because we're a long ways
[00:33:12.406]away from actually getting to a functional model with them.
[00:33:18.668]As I mentioned, we had them report the revisions
[00:33:20.850]that they had in their model, and so those revisions,
[00:33:23.825]these are students who didn't report any revisions,
[00:33:28.522]and these are students who reported revisions.
[00:33:30.847]And we think that really, and what we have the technology
[00:33:34.265]to show now is that a lot of these students
[00:33:38.318]in the no revisions are probably ones that had revisions,
[00:33:44.175]they just didn't report them.
[00:33:45.267]So they're not writing them down, and now we've implemented
[00:33:48.625]in the setup where they have to actually type them in
[00:33:52.070]revisions before they can resimulate that.
[00:33:54.854]So that's actually improving our ability to determine
[00:33:57.445]whether they're making revisions, and so these 27 students,
[00:34:02.334]this is why I don't think that 41% of students would
[00:34:06.711]have gotten the correct model on
[00:34:08.044]the first time is very unlikely.
[00:34:11.201]I mean as a scientist who's tried modeling things before,
[00:34:14.809]like I just don't think that's very realistic.
[00:34:19.195]These four students over here, we think of these students
[00:34:23.224]as being really persistent, so they kept trying and trying
[00:34:25.912]and trying, and they were able to successfully
[00:34:27.612]generate a correct model.
[00:34:30.187]And then these 19 students, this is worrisome for us.
[00:34:33.436]So these students are reporting no revisions,
[00:34:35.743]so they might have revised it, but they didn't know
[00:34:38.576]that they had a wrong model.
[00:34:41.320]So either they saw that it was wrong, and they still
[00:34:44.220]chose to go on, or they weren't able to figure out
[00:34:47.450]that their model was wrong, even though the data are
[00:34:50.169]showing them that it's not meeting these observed values,
[00:34:55.232]or the expected values.
[00:34:56.742]So this is a group that is concerning from
[00:35:04.150]a teacher's perspective because they're
[00:35:05.473]not recognizing that they're incorrect.
[00:35:07.909]These 16 students, I think about as a group that,
[00:35:10.637]this is a group that we would like to target.
[00:35:13.831]They're actually trying hard to make the revisions
[00:35:16.108]to their model, and they're still not getting
[00:35:18.434]the functional model, so these students are really
[00:35:20.573]working and trying to do what they're supposed to be
[00:35:24.213]doing, but they're still not quite getting there.
[00:35:26.918]And so there's probably something in that group
[00:35:29.078]that's telling us about their interactions with the
[00:35:31.567]technology, or the lesson itself that's probably
[00:35:33.809]inhibiting their ability to completely perform
[00:35:38.152]and to develop a functional model.
[00:35:40.727]So students aren't great about reporting their revisions,
[00:35:43.351]which isn't a surprise.
[00:35:45.236]And some of the students don't recognize when their model
[00:35:48.277]doesn't function, so they are
[00:35:50.813]moving on for a whole host of reasons and deciding
[00:35:56.485]that they don't need to have a correct model.
[00:36:00.355]Some of that might be the fact that they learned from
[00:36:02.073]their friends that if you just keep going,
[00:36:03.647]you could do the rest of the lesson, I'm not sure.
[00:36:06.595]We're still trying to figure that out.
[00:36:09.047]But we've been very interested like I mentioned
[00:36:11.025]in the cognitive processes, so what are students
[00:36:13.267]actually doing during that process.
[00:36:15.037]They're working in pairs when they're building these models
[00:36:18.504]so we're interested in what the students are saying,
[00:36:20.724]or what they're doing, and also what are they discussing.
[00:36:25.595]So what parts of the model are they focusing on,
[00:36:29.608]are they focusing on the outputs, or the relationships,
[00:36:32.310]the components, and how are they
[00:36:35.323]going about analyzing their model.
[00:36:38.179]So just for example, and you don't have to know the details
[00:36:40.462]of this, but in this short clip here, it's says,
[00:36:44.080]"Camp activates cap and that's when environmental glucose
[00:36:48.187]"levels are low, so as glucose goes down," and the other
[00:36:50.745]person chimes in, "Yeah, the camp goes up."
[00:36:53.412]And the guy goes, "Well, camp goes up
[00:36:55.597]"which is a negative regulator, right?"
[00:36:57.549]And the other student says, "Yeah, right."
[00:36:59.796]Kind of wondering.
[00:37:02.593]So it's negative because as glucose goes down, camp goes up.
[00:37:05.873]If it was glucose going up, and camp going up,
[00:37:08.560]then it would be positive.
[00:37:10.115]So this C student here is having a really very positive
[00:37:15.038]impact on this interaction here, because they're actually
[00:37:18.631]providing a counterpoint to that initial discussion
[00:37:22.921]about the positive and negative regulator, and they're
[00:37:25.404]describing it in a way that hopefully
[00:37:27.700]it's going to clarify it for that other student.
[00:37:33.247]So we code these, we're coding their interactions right now
[00:37:36.993]of the students social interactions to try to better
[00:37:40.961]understand what they're doing.
[00:37:42.692]So this is extremely preliminary data,
[00:37:45.032]thanks to Gretchen, who's been working on this.
[00:37:48.090]Looking at some of the differences between the
[00:37:50.029]building and investigating students,
[00:37:52.276]and that they all used all these processes
[00:37:55.929]but the building students are using a lot more of these
[00:37:58.405]processes, which would be kind of what you would expect.
[00:38:02.641]They're trying to get feedback from each other,
[00:38:04.807]from their instructors, they're trying to analyze
[00:38:07.334]what's going on in the model more than if it's
[00:38:10.505]being provided to them, and we know that the building
[00:38:13.579]students are more focused on these components and
[00:38:15.764]relationships while the investigating students focus
[00:38:18.560]more on the output of the model, so the building students
[00:38:22.135]tend to be more interested at least in this small data set,
[00:38:26.507]more interested in how the model is going together,
[00:38:30.004]rather than just what the output is, which is positive.
[00:38:33.529]That's what we would like to see, that they're really
[00:38:35.402]focused on the system dynamics rather than
[00:38:40.510]just is it the correct answer or not the correct answer.
[00:38:45.823]So I think that this quote kind of encapsulates a lot of
[00:38:50.786]this approach to pedagogy and how we incorporate
[00:38:57.054]learning in there, because modeling and direct instruction,
[00:38:59.781]for example lecture, lead to these
[00:39:01.823]qualitatively different learning outcomes.
[00:39:04.775]These two modes of instruction cannot be compared on a
[00:39:07.084]single effectiveness measure.
[00:39:09.835]And so I kind of liken it to this idea about bike riding.
[00:39:13.141]So if I can ride a bike, and you can't ride a bike,
[00:39:15.670]if the metric is riding around on an obstacle course,
[00:39:19.055]riding a bike on an obstacle course,
[00:39:21.383]well you are at a disadvantage
[00:39:22.692]because you don't know how to ride a bike.
[00:39:24.628]So we can't compare our ability to go around on the
[00:39:26.962]obstacle course, if you can't do the skill.
[00:39:29.481]So in this case, we have a skill like modeling,
[00:39:32.230]and we can't compare it to just listening to information
[00:39:36.224]in a classroom, it's different.
[00:39:37.846]It's a different process than listening
[00:39:40.223]and answering it that way.
[00:39:43.045]So we have to look at a lot of different measures,
[00:39:45.288]so some of these are quantitative measures about the
[00:39:48.887]relationships within their concept maps
[00:39:51.473]and conceptual models, some of them are understanding
[00:39:54.081]more qualitative understanding of their abilities to model.
[00:39:57.955]So modeling provides us insight for instructors
[00:40:02.248]about gaps in their knowledge, because it emphasizes
[00:40:05.219]these relationships within a systems, and that's true.
[00:40:08.561]So for example in that food web dynamics model,
[00:40:13.761]it's really informed how I teach stability in the system
[00:40:17.583]relative to the number of organisms in the food webs.
[00:40:20.234]And it informs how we develop these modules going forward
[00:40:24.423]because we see how students are interacting,
[00:40:26.784]where their gaps are, for example with the electron
[00:40:30.060]transport chain and fermentation,
[00:40:32.205]we can emphasize those in the models going forward
[00:40:34.999]to try to help students overcome that gap.
[00:40:39.307]And this technology does allow us to start to look at
[00:40:42.212]some of these things, like the modeling process,
[00:40:44.353]the social interactions, which are a very
[00:40:48.177]under researched part of understanding modeling in academia.
[00:40:53.930]So I would like to make the case that we can improve
[00:40:57.321]our model-based reasoning modeling in the classroom.
[00:41:00.118]It's an authentic science practice.
[00:41:01.484]We all do it.
[00:41:02.685]We need to get our students to be doing it more frequently
[00:41:04.728]and allow students to focus on the system dynamics,
[00:41:08.885]rather than just what some of those pieces of facts.
[00:41:16.778]And so with that, all right.
[00:41:18.550]I think I'll end it.
[00:41:19.657]And I just have a few, if we don't have any questions,
[00:41:22.481]I have some questions for you to end it there.
[00:41:26.627]So we'll go to that.
[00:41:29.279](applause)
[00:41:31.419]When you were comparing the
[00:41:32.252]build and investigate in terms of this social engagement,
[00:41:36.080]it looked like you were also quantifying how often
[00:41:38.164]they engage with the instructor?
[00:41:39.861]Yes, that's true.
[00:41:40.857]And it looked like in the build,
[00:41:41.973]it was much much more engagement with the instructor.
[00:41:44.831]So it seems like there's that benefit too, is part of it
[00:41:47.490]is I know from teaching with you, students don't want to ask
[00:41:50.574]us questions a lot, and if you can provide them with a
[00:41:53.305]type of exercise where they actively seek out
[00:41:56.522]engagement with the instructor, it seems like there's a
[00:41:59.254]benefit to that as well.
[00:42:01.015]I couldn't agree more.
[00:42:02.950]I think that there is a big benefit to engagement
[00:42:05.727]with the instructor, so it probably I'm guessing that it
[00:42:09.700]takes some of that responsibility.
[00:42:12.040]So now when you provide a model to somebody, you're saying
[00:42:15.358]that this is from on high, this is the model, right?
[00:42:19.338]This is an expert, this is what it is.
[00:42:22.563]But when you actually say, "You need to build a model,"
[00:42:25.645]you gotta figure out how to do it.
[00:42:28.757]So now you're the one with the knowledge,
[00:42:32.150]and you get that feedback from the instructor,
[00:42:34.157]or people you're working with.
[00:42:38.032]And yeah, it really drives questioning and asking,
[00:42:41.876]engagement with it, because you don't have anything.
[00:42:45.682]You're starting with nothing.
[00:42:47.013]And I think that's a really key part of that whole process.
[00:42:51.701]'Cause we all start with, I mean when you start modeling
[00:42:54.631]you're starting with a blank page.
[00:42:55.958]It's completely overwhelming to think about
[00:42:58.544]what you're gonna do with this blank page.
[00:43:04.484]Any other questions?
[00:43:09.509]Do you give them, so the revision
[00:43:11.411]process it seems like maybe some students aren't doing it
[00:43:13.678]or they don't know they're doing it,
[00:43:16.036]and some of them are doing revisions
[00:43:17.123]and they're still not getting to the right model.
[00:43:21.294]How much do you talk to the students before they do this
[00:43:23.775]exercise about what it means to revise a model,
[00:43:26.601]and how to do it in a systematic way.
[00:43:28.862]Because I could imagine that students just go back in,
[00:43:30.561]and they're like, "Okay, I'm just gonna take this here
[00:43:32.654]"and I'm gonna reverse it, or I'm gonna make it
[00:43:34.907]"excitatory or inhibitory," in kind of a random way
[00:43:39.441]or they change too many things at once.
[00:43:41.156]So do you teach them how to do revision?
[00:43:45.449]I would say that is not something we are good at,
[00:43:48.209]is teaching them how to go about being systematic
[00:43:51.486]in that revision, and that is definitely one of the things,
[00:43:53.973]so we've started being able to
[00:43:59.762]gather that data, what are all of the changes that they
[00:44:03.075]made to their model, and how often?
[00:44:05.820]So did they do five things, and then they simulate?
[00:44:08.439]Or do they change one, are they systematic?
[00:44:11.229]So we're just starting to get that data,
[00:44:13.484]and that's something I'm really interested in
[00:44:15.777]because some people are really systematic
[00:44:17.585]and I would imagine that they're much more likely to get
[00:44:19.631]a correct model eventually than ones who are
[00:44:25.731]just putting arrows wherever and hoping, right?
[00:44:30.692]But yes, there is not much coaching on the revision process
[00:44:34.381]and that is one of the things that we've been going back
[00:44:38.002]and saying, look, maybe we need a feedback to the student
[00:44:42.186]that's saying, "Your model isn't quite right,
[00:44:45.249]"it's not functioning correctly, so maybe you need to
[00:44:49.962]"think about whether or not the lactose is being
[00:44:53.849]"imported correctly," or something like that.
[00:44:56.316]So trying to go piecemeal to get them closer to building
[00:45:00.021]that correct model so they are a little more systematic.
[00:45:03.177]I definitely think that's a failing there,
[00:45:05.705]that we need to do a better job of coaching that.
[00:45:08.326]It's never taught, right?
[00:45:09.820]I mean, we're barely ever teaching modeling
[00:45:11.725]and then to think about revising that mental model is hard.
[00:45:21.086]In your results, where you demonstrate
[00:45:23.553]they did better on exams when we had that ACT controlled
[00:45:26.861]in there, I was interested to hear you talk a little
[00:45:29.675]bit more about what that exam was.
[00:45:32.564]Like, what is the thing that you're asking them to do
[00:45:35.340]in that exam that they're performing better on.
[00:45:37.478]Right, so I would say that it was an indirect test
[00:45:42.899]because we went back, we asked all the instructors
[00:45:46.661]for exams and we compiled.
[00:45:50.210]So there's differing numbers of questions in each
[00:45:53.718]instructor, we didn't provide any questions.
[00:45:57.021]It was whatever they had that was related to
[00:45:59.514]cell respiration, and we looked at students'
[00:46:01.605]performance on that, so there's varying number of questions,
[00:46:04.232]there's varying difficulty, there's all sorts of stuff.
[00:46:07.028]The lifesaving thing on that is, and I can't remember
[00:46:09.717]the size there, is we're talking hundreds and hundreds
[00:46:13.651]of students, so you do have some ability
[00:46:16.432]to see some signal there.
[00:46:19.897]But interestingly, we looked year over year.
[00:46:25.648]So one fall semester they were doing the explorer,
[00:46:33.530]and the next fall semester it was the discover.
[00:46:35.956]So it was essentially the same teachers,
[00:46:38.082]so we were able to control, not control,
[00:46:41.108]but the instructors are the same from one year to the next.
[00:46:45.906]So usually the questions are actually about the same,
[00:46:50.070]more or less, but it was taking what we had from the
[00:46:54.816]instructors, not actually providing
[00:46:56.919]any questions of our own.
[00:46:58.837]We have done that, but we haven't done a lot of those
[00:47:01.045]questions because usually the questions that we provide
[00:47:04.568]to instructors are more about the system dynamics
[00:47:08.737]that they haven't talked about in lectures.
[00:47:10.920]So people are a lot more resistant to putting those
[00:47:13.150]on their exams, because their students may or may not do
[00:47:16.668]very well on it, because they haven't taught it.
[00:47:20.005]So there is that resistance to doing that for sure.
[00:47:34.420]Touching again on what
[00:47:35.253]you were saying about the revision process.
[00:47:40.031]I was trying to think of a way you could, like you were
[00:47:43.434]saying, find a way to add a step where they can be
[00:47:48.103]learning about why they were wrong.
[00:47:50.810]And I was trying to think about how I would approach
[00:47:52.479]one of these problems, and if I was doing it,
[00:47:55.643]and I didn't necessarily want to be learning it.
[00:47:59.901]I would randomly be Which is true for a lot
[00:48:01.080]of students.
[00:48:02.251][Audience Member ] Yeah, I would be randomly changing
[00:48:04.311]variables, knowing that eventually I would hit a combination
[00:48:07.657]that would give me the correct answer.
[00:48:09.698]But that's a (laughs)
[00:48:11.678]So what I Maybe.
[00:48:14.503]I mean there's like 10 components in that model.
[00:48:20.035]So the number of interactions that you can
[00:48:21.717]put on there I guess yeah, you would
[00:48:23.120]have to be pretty determined.
[00:48:25.165]You would have to be pretty determined.
[00:48:27.111]Could you maybe implement a step
[00:48:29.518]where if they got it wrong, they then also have to
[00:48:33.138]answer a question as to why they think they were wrong?
[00:48:35.297]Like you were saying, you didn't have it where
[00:48:37.915]they had to be right to progress.
[00:48:41.022]But maybe they have to answer a question as to why
[00:48:43.328]they don't think it was right before they move on
[00:48:45.408]to the possibility of changing another variable.
[00:48:48.756]Yeah, I think that would be the possibility there.
[00:48:53.201]Is to kind of move into, I do think that the more you
[00:48:57.033]have them explore why they're correct or incorrect,
[00:48:59.241]the deeper, the more involved it gets.
[00:49:01.861]But it also becomes harder to provide the feedback
[00:49:05.298]because now you're dealing with an open response thing,
[00:49:07.938]and you have to say, is that good or,
[00:49:10.953]something has to grade that response also.
[00:49:14.999]I think you're completely right.
[00:49:20.169]I think that's why we value those questions,
[00:49:21.967]to explain why this works or it doesn't work,
[00:49:24.710]and that is a very hard process for us to do.
[00:49:28.113]Why didn't it work?
[00:49:30.911]And I think that for many of us, explaining why
[00:49:34.326]it doesn't work, I mean it only works one way.
[00:49:39.674]Well, there are actually multiple ways you can
[00:49:41.920]get a functioning model, but there are very few ways
[00:49:44.539]that it works and a lot of ways it doesn't work.
[00:49:47.114]Okay, 'cause yeah I would think
[00:49:49.346]eventually you could maybe create some sort of system
[00:49:51.970]where each component can spit out a multiple choice
[00:49:55.641]question where they have to choose between four answers
[00:49:58.310]as to why they think they're wrong.
[00:50:00.352]But the reason I think this might be important is,
[00:50:02.364]obviously in the classroom you can never avoid that
[00:50:06.254]there's going to be some students that simply aren't
[00:50:08.255]gonna be engaged, they're just not gonna apply themselves.
[00:50:10.749]Yeah, that's true.
[00:50:12.050]And so, the more steps you add,
[00:50:14.572]the more likely you are to get something to stick,
[00:50:19.054]even if they're not trying to learn.
[00:50:21.814]I agree.
[00:50:23.703]The counterpoint to that is the more steps you add,
[00:50:26.076]the more time it takes, and one of the biggest issues
[00:50:31.311]that we have with that building is it takes 30 minutes extra
[00:50:34.696]but students are really upset about that.
[00:50:38.741]Because they have a three-hour class period.
[00:50:41.613]Every time they come to class, it only takes an hour.
[00:50:45.565]This one takes an hour and a half,
[00:50:47.343]and all of a sudden the world ends.
[00:50:49.558]I mean honestly, we have all these audio files where they
[00:50:52.233]were supposed to do this other concept inventory,
[00:50:54.930]and students are like, "I don't even care, just put B."
[00:50:58.101]Well, that just ruined my research, right?
[00:51:00.642](laughs)
[00:51:03.297]It was 30 minutes, you weren't asking you to stay
[00:51:07.659]the rest of the class period.
[00:51:10.153]But they'll be like, "I only allocated an hour today,
[00:51:13.541]"and I can't stay another half hour."
[00:51:16.088]So that is really hard to overcome.
[00:51:18.866]That mentality is just embedded in these students.
[00:51:23.204]So every time you add more stuff, it makes more time,
[00:51:26.750]which is great, we think they should be excited about this.
[00:51:29.246]But you also have the people who don't want
[00:51:31.181]to be excited about it for sure.
[00:51:37.570]Any other questions?
[00:51:41.115]All right.
[00:51:41.948]Nope, I think you're done.
[00:51:43.118]Great, thank you very much.
[00:51:44.170]Let's thank Joe one more time.
[00:51:45.448](applause)

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Joe Dauer - Modeling to Learn Biology

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