January 29, 2025 • 38 mins
Authentic learning experiences help to create intrinsic motivation for students. In this episode, Julia Koeppe, Bonnie Hall, Paul Craig, and Rebecca Roberts join us to discuss BASIL, a course-based undergraduate research experience in Chemistry that has been implemented in many institutions.
Julia is an Associate Professor and Chair of the Chemistry Department here at SUNY-Oswego. Bonnie is an Associate Professor and Chair of the Chemistry & Physics Department at Grand View University. Paul is a Professor in the School of Chemistry and Material Science at the Rochester Institute of Technology. Rebecca is a Professor in the Biochemistry and Molecular Biology Program in the Department of Biology at Ursinus College.
A transcript of this episode and show notes may be found at http://teaforteaching.com.
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Episode Transcript
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(00:00):
Authentic learning experiences help to create intrinsic motivation for students. In this
episode, we discuss a course-based undergraduate research experience in Chemistry that has
been implemented in many institutions.Thanks for joining us for Tea for Teaching,
(00:23):
an informal discussion of innovative and effective practices in teaching and learning.
This podcast series is hosted by John Kane, an economist...
...and Rebecca Mushtare, a graphic designer......and features guests doing important research
and advocacy work to make higher education more inclusive and supportive of all learners.
(00:50):
Our guests today are Julia Koeppe, Bonnie Hall, Paul Craig, and Rebecca Roberts. Julia is an
Associate Professor and Chair of the Chemistry Department here at SUNY-Oswego. Bonnie is an
Associate Professor and Chair of the Chemistry & Physics Department at Grand View University.
Paul is a Professor in the School of Chemistry and Material Science at the Rochester Institute
(01:12):
of Technology. Rebecca is a Professor in the Biochemistry and Molecular Biology Program in
the Department of Biology at Ursinus College. Welcome Julia, Bonnie, Paul, and Rebecca.
Today's teas are:... Julia, what tea are you drinking today?
I have a wild raspberry hibiscus tea.Very nice. Bonnie?
(01:35):
I have an iced tea because it is quite warm at our location today.
And where is your location today? We are currently in San Juan, Puerto Rico,
doing some workshops to support universities here in Puerto Rico.
Excellent. And Paul?I like Irish Breakfast tea.
That's a favorite of mine, too.And Rebecca?
(01:57):
Like Bonnie, I'm doing the sweetened iced tea because it is warm here in
San Juan. And the other Rebecca? John, I have a new tea that I got this
holiday season. It's called Blue Lady. It's a flavored black tea, and it's very tasty.
Very good. Maybe I'll share with you next time I see you.
Usually that only happens when you don't like the tea.
(02:20):
I know, I know. Usually I give them the stuff I don't like.
And I'm drinking some of the last of the Christmas tea left over from our celebrations several weeks
back. It's not quite as warm here in Oswego right now. I think it's about 15 degrees, 20 degrees,
something like that. Not if you figure in the wind
(02:40):
chill. John, really… Yeah, it's cool.
Well, we wish we were in Puerto Rico with you, but we're not. So we've invited you here today,
virtually, to discuss the biochemistry authentic scientific inquiry laboratory community. Can
you give us an overview of this project?Sure, the BASIL project is a CURE. CURE stands for
(03:00):
course-based undergraduate research experience. And what that means is that we teach a course,
a laboratory course, but instead of being a traditional cookbook lab where the instructor
knows exactly what's happening, the students are given directions, and they follow directions and
complete their assignments, in a CURE, students actually engage in research. Neither the students
(03:23):
nor the instructors know the answer to the question, and so this is open-ended basic
research, where discovery actually happens in the course. Our course is focused on predicting
protein function. We begin with computational methods, computational tools, these are all
web based resources that our students use, and then once they have a prediction, they go in the
(03:44):
wet lab, they express and purify the protein, and then they run some assays to see if their
prediction is correct. And our overwhelming goal in this process is that we want to transform our
students from thinking of themselves as students to thinking of themselves as scientists.
So if someone were to visit one of your classrooms, what would it look
(04:05):
like? What would be taking place? What would students be doing? What would you be doing?
This is Rebecca. It would basically look like a research lab, but going on with a lot of people
in the room doing their experiments, and they might be doing different things at different
times. So it's quite an exciting place to be. We have designed BASIL with a flexible structure. So
(04:29):
it's modular, it's adaptable, and faculty can choose to take as much or as little as they'd
like. So we have some faculty that might do only the computational modules with our students, and
others might do all of it, or some might choose one module and do it more as a one time in the
(04:50):
lab, let's introduce you to a computational tool. And because of that, it's been really nice to see
how different faculty have chosen to run BASIL at their institutions, depending on their own
learning outcomes and student population and the resources that they have at their disposal. We've
mostly designed it to be at the undergraduate level, but it's been used at the master's level.
(05:15):
Julia uses it at Oswego in one of her master's level computational chemistry courses. And we've
even had some high school faculty take it on for their advanced students at the high
school level. The great thing about this flexible structure is it's what we started with, and it's
what we've really maintained a passion for, so that people can use it how they want to use it,
(05:41):
and it's accessible, so it's open access; all of the computational modules are free web based
modules to, again, improve student accessibility and flexibility for the instructors.
Can you talk a little bit about how this differs from traditional
instructional approaches in biochemistry?So in a traditional biochemistry lab, you might do
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what's called, often, a cookbook experiment, where we give you some specific instructions to follow,
to learn a particular technique with and then to achieve a known outcome. And in our approach,
again, we're looking at doing some more authentic inquiry and research-based experiments. And so one
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of the things that is very different from some of the traditional biochemistry lab is the inclusion
of the computational tools that we use, and these tools help us to study protein structures in order
to predict a function, and then students can use more traditional wet lab techniques in
biochemistry to then create their protein sample and do some analysis of that protein to confirm
(06:51):
the function. And so there's a lot more research going on, as far as the instructor doesn't know
the outcome, the student doesn't know the outcome. The students are really having to come up with a
hypothesis based on their initial computational analysis and then design the experiments to test
that hypothesis, and when I say they have to do that, it's because the instructor also doesn't
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know what we're looking for, except that you have the same data the students have already
collected computationally. And one of the reasons that I do this in the biochemistry lab is because
I like the idea that the students are driving the project, and I also see the students are engaged
with the project because they have connections. So they're learning the different biochemistry lab
(07:38):
techniques because they need to use them to solve a problem, rather than just learning techniques,
because techniques are important. And I'll throw it over to Bonnie and Rebecca to talk some about
why they're using the project as well.So I chose to adopt BASIL because we have a
pretty diverse group of students on our campus. We have traditional students coming in at 18.
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We have a lot of retired military that come in, so veterans, international students, immigrants,
and they really go into a wide variety of career options after: some go into industry, some go into
sales, some go to graduate programs. And the one thing that we know that every employer, graduate
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program, whatever it's going to be, wants them to be able to do, is to learn how to get stuck,
which happens in real research, and how to figure out how to get unstuck, how to move forward when
you run into a barrier. How do you problem solve and how do you motivate yourself to get through
that? And so that's an experience we really want every student in our program to have.
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And at Ursinus, I was really interested in the idea of interdisciplinary learning. And again,
that's a tool that students need to be able to have the ability to talk to others in a
different field when they're out of school. And so at Ursinus we leverage the flexible nature
of BASIL in that I teach a course in structural biology, and we do all the computational work,
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the students there, and they then go to the students enrolled in biochemistry and tell them
about what they've learned, what their hypothesis is, and then the biochemistry students kind of
roll with it from there, and they have to talk to each other, and that's engineered into the
syllabus. And they end their semester with a joint poster presentation to the entire campus
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community during our celebration of student achievement day. And it really forces them to
learn from each other and talk to each other, and that is a model that I've used to try to engage
students in this idea of interdisciplinary conversations across disciplines.
I imagine that the prep for faculty is a little bit different in a course like this
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than maybe a traditional bio chem class. Can you talk a little bit about that?
Yeah. And so as far as the computational analysis, that is going to be something
that many instructors are not familiar with. And so one of the things that we have done as the
developers of the curriculum is try to make these things accessible to the instructors as well as
(10:16):
to the students. So one of the reasons we're here in Puerto Rico right now is that we regularly run
workshops to help train instructors how to take on this project and to help them to design their
curriculum, to use our modules. And then we also regularly run instructor support workshops, where
we will go through how the computational modules work and what you can learn from them, and the
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kind of data you get, how to analyze those data. As far as the wet lab goes, many of the wet lab
experiments, students do follow more traditional labs. It's just that the focus for each one is a
little bit different. Rather than give students a mixture of proteins and ask them to separate
that mixture, just to teach them a separation technique, the students have a mixture of proteins
(11:03):
because they express a sample using E. coli, and now they need to separate that mixture to
isolate their protein of interest, the same as you would do in a research lab. And so many of the wet
lab techniques are the same as we would see in a traditional biochemistry lab. And so planning for
that is very similar to what you would do for the traditional lab. It's just your starting material
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might change from semester to semester, and then the end point for how you test your predictions
can change from semester to semester, so that can require the instructor to do a little extra work
ahead of the semester, to pick the targets, so that you know what you might need to test, and be
able to order your supplies ahead of time, so that you're not running into difficulties mid semester:
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I need this thing because these students predicted this function, but the wait time from the company
is going to be six weeks, and then graduation will have already happened. So there is some prep time
that you have to do ahead. And then there's also some training. We provide support for that.
For my course, I have my students do a lot of the prep work. When they graduate, if they go
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into a work or industry environment or an academic environment, there's going to be the expectation
that they can run experiments without a prep person making all their solutions and finding all
the reagents. So mine get protocols that look like they do when they come out of the literature. This
is what you need to make. How do you take what you took from literature and translate that into
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steps that you take in the lab? So in fact, we don't do a lot of prep in advance for my course.
I need to know what they're doing so I can guide them in the right direction, but they're doing the
actual prep work, because that's experience that's really valuable for them after they graduate.
You've all addressed this to some extent, but could you talk a little bit more about
the student reaction to this type of instruction? Because it seems like this is providing a really
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authentic learning experience, which is different from the type of cookbook type
approach that was mentioned earlier. And I would think that would make students much more excited,
much more curious about the outcomes and so forth, than just doing something
that everyone else has done in the past.Students do get excited and when the beginning
of the semester starts and I’m explaining what we'll be doing for the semester,
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you can see some of the students’ eyes just light up at the thought of doing some real research.
One of the powers of course-based undergraduate research experience in general is that you can
engage more students in the research better than in the one-on-one mentoring model. And the data in
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the literature shows that experiences with CUREs do improve the student's identity as a scientist.
They feel more project ownership. They begin to appreciate the reasons behind why they're doing a
certain method. And so that is all really exciting for the students. What was mentioned earlier is
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this idea of failing, and that happens a lot, and that could be really scary for an instructor who's
taking on a CURE for the first time, because you do lose a little bit of the power and control,
because you don't really know what might happen that day in the lab. And I've really embraced that
idea of having the students do research, take the risk, experience the failure that was mentioned
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before, and pushing through it, not taking it personally, learning how to troubleshoot,
learning how to celebrate the little things in research that actually do go well and not to get
so frustrated by failure. So I like doing a CURE in my courses, because it allows for a safe space,
(14:56):
a slightly more controlled space, for students to engage with failure and
get the experience of overcoming that. Yeah, and I can add that there are students
who get very excited that I don't know what the answer is, so I cannot gauge their ability to do
this based on whether they got the correct answer. However, there are also students that are very
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frustrated by the fact that we don't know what the answer is, so I cannot tell you if you are
correct. And so you see both sides of that, but there's some students that are very happy to hear:
“I don't know.” I can't grade them on whether they are correct or not. I can grade them on
what they learned, and I really emphasize that as well. I'm gonna see that you can tell me something
that you learned and show me that you have some understanding of what you're doing, but even I
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don't know if you've got the right answer. Can you talk a bit about how this project got
started? I mean, there's four of you. You're together, you're in Puerto Rico,
and you're doing a lot of work together. How did this all get started in the first place?
Well, I'm a computational biochemist, and if I never go into another wet lab again or touch
a test tube or make up a buffer or grow E coli again, I'll be perfectly happy when that will be
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fulfilled. If I'll never run another gel, I'll be very happy. And so I had students working with me,
and they designed some stuff. And the software they designed, you could feed it protein structure
and would give you information back about what that protein probably did. So we developed this
original software, and that was fun. But what I found as they developed the software is they
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would predict the function. Well, they wanted to go in the wet lab and find out what it was. So
they started doing that. And then what we found… this is in my research lab, and I was sharing this
with a couple other professors… and what we found was that they started acting like scientists.
They started looking in the literature to find a better assay to use, they started walking up
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to me and saying, “You know, Dr. Craig, we think that's not a good idea. We should do it this way
instead.” They came up with alternative proposals. So we talk about experimental design as scientists
and part of that is making something that's going to be statistically robust, but part of
the experimental design is choosing the tools. And they started choosing the tools. One of my
students actually called up the Head of Research at Argonne National Lab and talked to him for an
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hour about our project, and never even asked my permission. And that was fantastic. So they're
collaborating with each other. They're going to people in other departments and saying, “We tried
to do this, it didn't work. Can you help?” As I said, I’m a computational biochemist, if something
wasn't working in the lab, I wasn't the guy to ask. They asked the other faculty in chemistry
and biology and biomedical sciences. And so we saw that, we started to incorporate additional tools.
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About the time this project really started working well in our research lab, we had funding from the
National Institute of Health, and they decided to go a different direction. And so our funding dried
up, but our program officer was very supportive at NIH, and she recommended we contact NSF and
go through their program. They have a program called Improving Undergraduate STEM Education.
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STEM stands for science, technology, engineering, and math. So we approach them, and at the same
time, our first round of money was unsuccessful, but I presented a poster at a conference, and at
that conference, I met Rebecca, and I also met Mike Pikaart, who's another member of the team,
a couple other folks who had joined the BASIL project at that point, and we've had new people
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join us since then. And so Rebecca went back to Ursinus all excited, and presented it to Julia,
who was at Ursinus at the time, and Julia signed up. Rebecca was attending a conference hosted by
another organization that they’re all affiliated with, called BioMolViz, which is focused on
molecular visualization of biological molecules. And that's where she met Bonnie. And so Bonnie
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joined the team, and she was, as far as I could tell, it was like the day she joined the team,
she'd been there all along. It was a very smooth fit. One of our other team members was doing some
stuff very much like what we do, and one of his colleagues at the University of Richmond said,
“You know, there's some people doing this called BASIL. You should contact them.” And so now he’s
part of our team. And so we have other people that attend our presentations at conferences,
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and they join the team. And so there have been ongoing themes. We want to have continuing use
computational tools and building for the future, I think. We also want to have authentic science,
they really do get into the wet lab and do stuff. We want to share what we're doing at
conferences. We want to write papers about it, and we want to act like scientists. We want to
model that behavior for our students. So I'll just add that Paul gave a very good
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introduction to how we got started, and we've been working together on this for 10 years now. So we
started in 2015, so this is a very timely podcast, in that we are in our 10th anniversary.
Yay, BASIL. So, how many faculty are involved in
the project overall? You've mentioned a few people in addition to those of you on the podcast.
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So we have a core team, and that varies between about 8 and 10 instructors. So there were the
original folks who were involved with the project. People retire. They find other projects they're
even more passionate about, new people come into BASIL, but that core team sits at about
8 to 10 people, and there's a variety of roles. As the number of users of the material has grown,
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we've had to be more organized about how we keep ourselves, as the community, organized. So we
are all members of the steering committee, so we're responsible for high-level things,
thinking about funding, thinking about how the different subcommittees work together,
about how to let folks know we exist. We also have an instructor recruitment onboarding committee,
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so they help show new users what our curriculum looks like and support them as they start to
adopt that curriculum, or think about how they can adopt it on their campus. We also have an
instructor support committee that's focused less on new folks and more on supporting the folks
that are already using BASIL. So as we update modules or find new tools, offering workshops
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about how to use those and supporting people when they look at things like promotion and tenure:
how can faculty use this as part of their natural professional development cycle? We also have an
assessment committee that looks at both how do you as an individual instructor assess the work your
students are doing in class, but also on a larger scale, how do we assess that this curriculum is
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achieving educational goals that we want it to achieve. There's also just some housekeeping.
We have a website and data management team. We do collect data. We run a website. So there's just
some logistical support that has to be provided. Another key part is keeping modules updated. So
some of that happens organically as we use them in our courses and we say, “Oh, in our instructions,
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we say, you will see a button to click on the right hand side. It's no longer a button. Here's
a different workflow.” Sometimes the online tools that we use get a complete redo, and we
don't know when that's coming. We don't run those tools. That happened with our SwissDock module,
we had great instructions, and then they did an update, and none of our instructions were relevant
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really anymore. So we had to go in and do a major update on that one so that it would be usable for
faculty again. There's also tools that get retired and new tools that we discover. Probably the last
thing that drives those changes in modules are pedagogical demands. Certainly, the pandemic is
a good example of how you need to adapt suddenly. And so the computational modules were really great
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for transitioning to a remote model. But we also had a faculty member develop some Choose Your Own
Adventure wet lab substitutes, where the students come into a Google form, they have to choose
what they want to use, and if they make the wrong choice, it routes them back and says, “That's not
right. Try again.” And so they couldn't be in the wet lab at that point, but they were able online
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to kind of step through that whole lab experience and really get a sense of what it would have been
like had they been able to be in person.If I can jump in… And a great thing that
happened during that time is, because we are a community, we were sharing data so if one of us
had gotten far enough with our students that we had some data, we were sharing that with
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other BASIL users so that their students could, even if they couldn't be in the lab anymore to
actually collect their own data, they had data to analyze. So that's how some of the
community can help each other out too.I think the original question was,
how many people are involved? And so we have our core team, but then we have a lot of other
people who have attended our workshops, or are using our stuff. Whenever I go to a conference,
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I search the abstracts in the conference to see if anybody mentions BASIL, and inevitably
we find one or two folks who are doing stuff that we've never met before, and we talk to
them and we offer them our support, and we have a Slack channel for BASIL participants,
and we have, what, 200 people there?Yeah. So we just checked the numbers yesterday,
where over 200 people are connected to us on Slack, and we estimate that we have at least
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50 campuses using the curriculum in some form or another. And as Paul said, part of that estimate
comes from conferences, the people who reach out to us, the people who are attending any of
our workshops. So most of our instructor support workshops are virtual, and we have gotten much
better at record keeping to keep track of who is attending, where the people are that are using
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things. And like I said, we're in Puerto Rico right now, running a couple workshops. We did a
workshop here in San Juan yesterday, and we'll be traveling to Mayagüez this afternoon and running
a workshop tomorrow. We've done a number of other in-person workshops, new adopters, the community,
and we've done even more virtual workshops to recruit new adopters to the community. We also
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give presentations at conferences, so there are lots of people. There is broad interest.
And so I think Paul mentioned that we regularly attend ASBMB meetings, the American Society for
Biochemistry and Molecular Biology, but members of our core team have also been invited to speak at
American Chemical Society meetings, at biophysical society meetings. Schrodinger is a software
(25:18):
developer, and they've invited us to speak during their educators’ week. And this is driven by
the community often, that people hear about us, they talk about us, and more people come in.
With a project that seems to be scaling up and up and up, I'm sure sustainability and scale
and thinking at scale is always at the front of your mind; these things aren't free. So
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what's on your mind related to these issues?I do think about costs. So it costs about as
much to run BASIL on a campus as it costs to run a traditional biochemistry lab. So the expenses for
BASIL are not unusual. The equipment that we use is not unusual. All of our software is free, and
it all works through a browser, and so that's very sustainable. Now, the people are really, really
(26:04):
important, and we really like working together. It's a very cohesive group, good friends. We work
together, we socialize, we stay in each other's houses, whatever, when we're in town, and that
kind of stuff. And so there's a very positive reaction and relationships that exist here. I
think that's an important part of sustainability. Also talk about making it work on our campuses,
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and there's something called the four frames model and thinking about the factors and effective
process. And there's also, what are the barriers to getting people involved and try to identify
and overcome those barriers? Our current grant funding is actually focused on those barriers.
So, as Paul said, our current grant funding is focusing on identifying barriers, and especially
(26:47):
barriers that might exist at different institution types. So one of the things we're doing in our
current workshop, is really exploring that with the attendees and the barriers that we identify,
of course, are time and expertise, and those seem to be common across all institution types, because
this sounds like it could require some different things than a traditional biochemistry lab and so
(27:13):
for sustainability and trying to overcome that barrier of time, that's where our core team has
really tried to do a lot of the work to make the curriculum something that you can adopt without
having to put in as much of the time to create all the materials that you might have to do if you're
just creating a new curriculum on your own. As for cost, we've really tried to keep the cost down as
(27:36):
well, so we use web-based computational tools, so that there's no requirement to purchase software
or software licenses. It makes it more accessible to students who have different devices. So you
need a device that connects to the internet, but you don't need to have a laptop with a certain
processor speed or capacity to install software. You can work from a tablet or a Chromebook,
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and we've also tried to use the traditional wet lab method, so that the cost of the wet lab is
not any more than a traditional biochemistry lab might cost, and we may even be able to have it
cost less than a traditional lab, because half of the work is using free computational tools,
which means, if you used to purchase materials for 14 weeks worth of experiments,
(28:23):
now you're purchasing materials for maybe seven weeks worth of wet lab experiments.
I'd like to talk just a little bit about this idea of CURES as a pedagogy, and part of the
excitement that I find being involved in BASIL is introducing this pedagogy to faculty around
the country, and whether they're choosing to use BASIL or not, just opening their eyes to
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a different way of teaching biochemistry, or whatever it is that they happen to be teaching,
and that is really an exciting place for me, personally, to be. I've seen the power of it. I've
seen how scary it can be from an instructor point of view. And I get scared every time I'm about to
start my BASIL curriculum, which will start in a week for me, but I know that I have the support,
(29:09):
and I know that it's so important and so effective for these students that spreading
the word is really satisfying to me. I think another issue that comes up in terms
of sustainability is that at one point, BASIL was small enough that everybody doing it could
hop into a online meeting room together and have sort of a town hall meeting format and just talk
(29:32):
about things. Now with over 50 campuses and all the different time zones that are represented,
that's not a supportable model, and so we've had to do things like create the Slack channel,
where we can have asynchronous communication and figure out ways to offer virtual workshops. And
we also now have begun offering something called BASIL week, where it's a full week of workshops
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about all of the different kinds of topics that users of the material might be interested in. So
some walkthroughs of computational material, some conversations about how to assess student work,
conversations about how to leverage the work that you do with BASIL in terms of required
professional activities that your institution asks you to do. So really thinking about how to build
(30:22):
that community and how to sustain that community as it grows is another part that we focused on.
Is this something you'd recommend to other STEM disciplines, because we do know that we lose a
lot of students in the STEM pipeline, and maybe this way, we'd get more people intrigued by the
process of being a scientist or becoming more engaged with science as a potential career.
(30:46):
Yes. So we ran a workshop about a year and a half ago at Prairie View A&M University,
outside Houston, Texas, and they had encouraged people from across the sciences to attend our
workshop. So we had attendees from biology and chemistry, as we expected, but also from physics,
math, computer science, the School of Education, and they even had a couple Deans come to our
(31:09):
workshop to see what was going on, and that was a very useful space for them, but also for us
to see how others might be inspired by what we've put together and looking for ways that
they could use our curriculum as a model for creating some additional kinds of experiments,
some more research-based things in the labs across the disciplines. And the person who was there from
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education was also very interested to know that this was happening, because there seems to be a
disconnect in those who are training in education departments and schools and those who are in STEM
departments and divisions. We don't necessarily talk to each other, so we don't know that we're
all thinking about student assessment to some extent and student learning and what the outcomes
(31:59):
are. And so I think that this is a good way to engage students in science. And I use it in my
courses for Chemistry and Biochemistry majors, but also in my courses for non majors.
I think additionally, one of the drivers for other STEM disciplines using a CURE is that the research
shows that it engages students. Students are retained, they're excited. It's increasing the
(32:24):
number of students who have a research experience under their belt, especially underrepresented
students in that field, and that, in and of itself, is going to increase the pipeline of
students moving through to the next level of their careers. Anecdotally, I have students
always coming back to tell me that when they were on a job interview or a graduate program interview
(32:49):
or a medical school interview, that they talked about their experience with BASIL and that it
made a difference, and people were really excited to talk with them about their experiences.
Yes, and I think we did mention earlier, but I'll just re-emphasize that the CURE model,
the course-based undergraduate research experience model, is a way to engage more students in
(33:13):
authentic research experiences. So many of us are familiar, especially in the STEM fields,
with the one-on-one research model, where a student approaches me to join my research lab.
But there's a limit to how many individual students I can take into my research lab,
due to the space in the lab, my capacity to mentor each one of them and the resources to support
(33:38):
their projects. But if we can do some level of research in the courses that we teach, then that
really gives every student some amount of research experience. And the studies show that whether it's
a week or a couple weeks, even, you are going to gain some more identity as a scientist and
project ownership than in a traditional lab setting or a traditional lab course
(34:01):
and a semester-long project can be even better for that science identity and project ownership.
It also is a way of reducing the barrier for those students who might not know or
be brave enough to approach a faculty member to ask about individual research. If they're
enrolled in your course, they're doing it, and so it lowers that barrier, and may be attracting
(34:23):
and retaining some students who otherwise might drop out of the pipeline of STEM.
So, we always wrap up by asking: “what's next?”
So part of what we'll keep doing as we go into the future is to maintain the baseline that we have:
keep updating modules, keep looking at new tools, replacing tools that get deprecated. We'll
(34:44):
also focus on maintaining community. We're also beginning to have collaborations with other active
learning communities, so communities that are not the BASIL CURE, but that provide active learning
opportunities to embed in your campus curriculum and trying to see how we can all work together
(35:04):
to improve biochemistry and STEM education collectively. So to put all our efforts together
and pull in one direction together. We're also especially interested in BASIL in computational
tools. So a big breakthrough in biochemistry in the last couple of years is the ability to
use computation to fold proteins and determine their structures. So when we started with BASIL,
(35:31):
there were about somewhere between a hundred and 150,000 protein structures. That had been solved
with the computational tools available now, there are over 700 million folded protein structures,
structures that anyone could use to do research. So we're interested in adapting BASIL to,
(35:51):
first of all, work with data sets that are so large now, every year they've grown tenfold.
So how do we keep up with data sets that are so large, and there's also a lot of new tools coming
out? So how do we keep our curriculum up to date and relevant? But a really great thing about this
is that before there were 150,000 proteins, many of which already had assigned function,
(36:17):
now, with 700 million protein structures, most of those don't have a verified function. So
there's an opportunity for anybody who wants to do BASIL to keep as many undergraduates as
they have engaged in research for as long as they wish. So it's a very exciting time for
(36:38):
BASIL and for the biochemistry community.Yeah, we're hoping that the outcome of our
research on barriers will lead us to be able to aid in overcoming those barriers to CURE adoption
and implementation. Are there ways that we can address that? So I think that's another future
goal for us is really looking at not just adoption of BASIL but adoption of CUREs in
(37:00):
general. And as Bonnie said, we're working with some active learning communities, and if we can
identify what the barriers are, that's the first step in being able to overcome those barriers.
We've been using these computational methods and emphasizing them and it's really about that, which
is probably the most of any CURE that’s out there, and we're hoping to have an impact,
(37:20):
at least to help other people who are doing course-based undergraduate research
experience with their students, to identify and implement those tools. And based on our experience
with getting these tools into our teaching labs, I think we could probably provide helpful
insight to any group that wants to introduce computational tools into their teaching lab,
even if it's botany or nutrition, whatever is out there. I think we could do that.
(37:45):
Well, thank you so much for joining us. There's lots of great information that you've shared. I
think no matter what discipline one might be in, there's lots of rich information. Thank
you so much.Thank you.
You’re welcome.Thank you.
Thank you.Yeah, thanks a lot. This was great.
If you've enjoyed this podcast, please subscribe and leave a review on iTunes
(38:07):
or your favorite podcast service. To continue the conversation, join us on
our Tea for Teaching Facebook page. You can find show notes, transcripts and
other materials on teaforteaching.com. Music by Michael Gary Brewer.
Authentic learning experiences help to create intrinsic motivation for students. In this
episode, we discuss a course-based undergraduate research experience in Chemistry that has
been implemented in many institutions.Thanks for joining us for Tea for Teaching,
(00:23):
an informal discussion of innovative and effective practices in teaching and learning.
This podcast series is hosted by John Kane, an economist...
...and Rebecca Mushtare, a graphic designer......and features guests doing important research
and advocacy work to make higher education more inclusive and supportive of all learners.
(00:50):
Our guests today are Julia Koeppe, Bonnie Hall, Paul Craig, and Rebecca Roberts. Julia is an
Associate Professor and Chair of the Chemistry Department here at SUNY-Oswego. Bonnie is an
Associate Professor and Chair of the Chemistry & Physics Department at Grand View University.
Paul is a Professor in the School of Chemistry and Material Science at the Rochester Institute
(01:12):
of Technology. Rebecca is a Professor in the Biochemistry and Molecular Biology Program in
the Department of Biology at Ursinus College. Welcome Julia, Bonnie, Paul, and Rebecca.
Today's teas are:... Julia, what tea are you drinking today?
I have a wild raspberry hibiscus tea.Very nice. Bonnie?
(01:35):
I have an iced tea because it is quite warm at our location today.
And where is your location today? We are currently in San Juan, Puerto Rico,
doing some workshops to support universities here in Puerto Rico.
Excellent. And Paul?I like Irish Breakfast tea.
That's a favorite of mine, too.And Rebecca?
(01:57):
Like Bonnie, I'm doing the sweetened iced tea because it is warm here in
San Juan. And the other Rebecca? John, I have a new tea that I got this
holiday season. It's called Blue Lady. It's a flavored black tea, and it's very tasty.
Very good. Maybe I'll share with you next time I see you.
Usually that only happens when you don't like the tea.
(02:20):
I know, I know. Usually I give them the stuff I don't like.
And I'm drinking some of the last of the Christmas tea left over from our celebrations several weeks
back. It's not quite as warm here in Oswego right now. I think it's about 15 degrees, 20 degrees,
something like that. Not if you figure in the wind
(02:40):
chill. John, really… Yeah, it's cool.
Well, we wish we were in Puerto Rico with you, but we're not. So we've invited you here today,
virtually, to discuss the biochemistry authentic scientific inquiry laboratory community. Can
you give us an overview of this project?Sure, the BASIL project is a CURE. CURE stands for
(03:00):
course-based undergraduate research experience. And what that means is that we teach a course,
a laboratory course, but instead of being a traditional cookbook lab where the instructor
knows exactly what's happening, the students are given directions, and they follow directions and
complete their assignments, in a CURE, students actually engage in research. Neither the students
(03:23):
nor the instructors know the answer to the question, and so this is open-ended basic
research, where discovery actually happens in the course. Our course is focused on predicting
protein function. We begin with computational methods, computational tools, these are all
web based resources that our students use, and then once they have a prediction, they go in the
(03:44):
wet lab, they express and purify the protein, and then they run some assays to see if their
prediction is correct. And our overwhelming goal in this process is that we want to transform our
students from thinking of themselves as students to thinking of themselves as scientists.
So if someone were to visit one of your classrooms, what would it look
(04:05):
like? What would be taking place? What would students be doing? What would you be doing?
This is Rebecca. It would basically look like a research lab, but going on with a lot of people
in the room doing their experiments, and they might be doing different things at different
times. So it's quite an exciting place to be. We have designed BASIL with a flexible structure. So
(04:29):
it's modular, it's adaptable, and faculty can choose to take as much or as little as they'd
like. So we have some faculty that might do only the computational modules with our students, and
others might do all of it, or some might choose one module and do it more as a one time in the
(04:50):
lab, let's introduce you to a computational tool. And because of that, it's been really nice to see
how different faculty have chosen to run BASIL at their institutions, depending on their own
learning outcomes and student population and the resources that they have at their disposal. We've
mostly designed it to be at the undergraduate level, but it's been used at the master's level.
(05:15):
Julia uses it at Oswego in one of her master's level computational chemistry courses. And we've
even had some high school faculty take it on for their advanced students at the high
school level. The great thing about this flexible structure is it's what we started with, and it's
what we've really maintained a passion for, so that people can use it how they want to use it,
(05:41):
and it's accessible, so it's open access; all of the computational modules are free web based
modules to, again, improve student accessibility and flexibility for the instructors.
Can you talk a little bit about how this differs from traditional
instructional approaches in biochemistry?So in a traditional biochemistry lab, you might do
(06:06):
what's called, often, a cookbook experiment, where we give you some specific instructions to follow,
to learn a particular technique with and then to achieve a known outcome. And in our approach,
again, we're looking at doing some more authentic inquiry and research-based experiments. And so one
(06:27):
of the things that is very different from some of the traditional biochemistry lab is the inclusion
of the computational tools that we use, and these tools help us to study protein structures in order
to predict a function, and then students can use more traditional wet lab techniques in
biochemistry to then create their protein sample and do some analysis of that protein to confirm
(06:51):
the function. And so there's a lot more research going on, as far as the instructor doesn't know
the outcome, the student doesn't know the outcome. The students are really having to come up with a
hypothesis based on their initial computational analysis and then design the experiments to test
that hypothesis, and when I say they have to do that, it's because the instructor also doesn't
(07:14):
know what we're looking for, except that you have the same data the students have already
collected computationally. And one of the reasons that I do this in the biochemistry lab is because
I like the idea that the students are driving the project, and I also see the students are engaged
with the project because they have connections. So they're learning the different biochemistry lab
(07:38):
techniques because they need to use them to solve a problem, rather than just learning techniques,
because techniques are important. And I'll throw it over to Bonnie and Rebecca to talk some about
why they're using the project as well.So I chose to adopt BASIL because we have a
pretty diverse group of students on our campus. We have traditional students coming in at 18.
(08:03):
We have a lot of retired military that come in, so veterans, international students, immigrants,
and they really go into a wide variety of career options after: some go into industry, some go into
sales, some go to graduate programs. And the one thing that we know that every employer, graduate
(08:23):
program, whatever it's going to be, wants them to be able to do, is to learn how to get stuck,
which happens in real research, and how to figure out how to get unstuck, how to move forward when
you run into a barrier. How do you problem solve and how do you motivate yourself to get through
that? And so that's an experience we really want every student in our program to have.
(08:45):
And at Ursinus, I was really interested in the idea of interdisciplinary learning. And again,
that's a tool that students need to be able to have the ability to talk to others in a
different field when they're out of school. And so at Ursinus we leverage the flexible nature
of BASIL in that I teach a course in structural biology, and we do all the computational work,
(09:11):
the students there, and they then go to the students enrolled in biochemistry and tell them
about what they've learned, what their hypothesis is, and then the biochemistry students kind of
roll with it from there, and they have to talk to each other, and that's engineered into the
syllabus. And they end their semester with a joint poster presentation to the entire campus
(09:35):
community during our celebration of student achievement day. And it really forces them to
learn from each other and talk to each other, and that is a model that I've used to try to engage
students in this idea of interdisciplinary conversations across disciplines.
I imagine that the prep for faculty is a little bit different in a course like this
(09:55):
than maybe a traditional bio chem class. Can you talk a little bit about that?
Yeah. And so as far as the computational analysis, that is going to be something
that many instructors are not familiar with. And so one of the things that we have done as the
developers of the curriculum is try to make these things accessible to the instructors as well as
(10:16):
to the students. So one of the reasons we're here in Puerto Rico right now is that we regularly run
workshops to help train instructors how to take on this project and to help them to design their
curriculum, to use our modules. And then we also regularly run instructor support workshops, where
we will go through how the computational modules work and what you can learn from them, and the
(10:39):
kind of data you get, how to analyze those data. As far as the wet lab goes, many of the wet lab
experiments, students do follow more traditional labs. It's just that the focus for each one is a
little bit different. Rather than give students a mixture of proteins and ask them to separate
that mixture, just to teach them a separation technique, the students have a mixture of proteins
(11:03):
because they express a sample using E. coli, and now they need to separate that mixture to
isolate their protein of interest, the same as you would do in a research lab. And so many of the wet
lab techniques are the same as we would see in a traditional biochemistry lab. And so planning for
that is very similar to what you would do for the traditional lab. It's just your starting material
(11:26):
might change from semester to semester, and then the end point for how you test your predictions
can change from semester to semester, so that can require the instructor to do a little extra work
ahead of the semester, to pick the targets, so that you know what you might need to test, and be
able to order your supplies ahead of time, so that you're not running into difficulties mid semester:
(11:50):
I need this thing because these students predicted this function, but the wait time from the company
is going to be six weeks, and then graduation will have already happened. So there is some prep time
that you have to do ahead. And then there's also some training. We provide support for that.
For my course, I have my students do a lot of the prep work. When they graduate, if they go
(12:12):
into a work or industry environment or an academic environment, there's going to be the expectation
that they can run experiments without a prep person making all their solutions and finding all
the reagents. So mine get protocols that look like they do when they come out of the literature. This
is what you need to make. How do you take what you took from literature and translate that into
(12:37):
steps that you take in the lab? So in fact, we don't do a lot of prep in advance for my course.
I need to know what they're doing so I can guide them in the right direction, but they're doing the
actual prep work, because that's experience that's really valuable for them after they graduate.
You've all addressed this to some extent, but could you talk a little bit more about
the student reaction to this type of instruction? Because it seems like this is providing a really
(13:01):
authentic learning experience, which is different from the type of cookbook type
approach that was mentioned earlier. And I would think that would make students much more excited,
much more curious about the outcomes and so forth, than just doing something
that everyone else has done in the past.Students do get excited and when the beginning
of the semester starts and I’m explaining what we'll be doing for the semester,
(13:25):
you can see some of the students’ eyes just light up at the thought of doing some real research.
One of the powers of course-based undergraduate research experience in general is that you can
engage more students in the research better than in the one-on-one mentoring model. And the data in
(13:47):
the literature shows that experiences with CUREs do improve the student's identity as a scientist.
They feel more project ownership. They begin to appreciate the reasons behind why they're doing a
certain method. And so that is all really exciting for the students. What was mentioned earlier is
(14:09):
this idea of failing, and that happens a lot, and that could be really scary for an instructor who's
taking on a CURE for the first time, because you do lose a little bit of the power and control,
because you don't really know what might happen that day in the lab. And I've really embraced that
idea of having the students do research, take the risk, experience the failure that was mentioned
(14:35):
before, and pushing through it, not taking it personally, learning how to troubleshoot,
learning how to celebrate the little things in research that actually do go well and not to get
so frustrated by failure. So I like doing a CURE in my courses, because it allows for a safe space,
(14:56):
a slightly more controlled space, for students to engage with failure and
get the experience of overcoming that. Yeah, and I can add that there are students
who get very excited that I don't know what the answer is, so I cannot gauge their ability to do
this based on whether they got the correct answer. However, there are also students that are very
(15:20):
frustrated by the fact that we don't know what the answer is, so I cannot tell you if you are
correct. And so you see both sides of that, but there's some students that are very happy to hear:
“I don't know.” I can't grade them on whether they are correct or not. I can grade them on
what they learned, and I really emphasize that as well. I'm gonna see that you can tell me something
that you learned and show me that you have some understanding of what you're doing, but even I
(15:44):
don't know if you've got the right answer. Can you talk a bit about how this project got
started? I mean, there's four of you. You're together, you're in Puerto Rico,
and you're doing a lot of work together. How did this all get started in the first place?
Well, I'm a computational biochemist, and if I never go into another wet lab again or touch
a test tube or make up a buffer or grow E coli again, I'll be perfectly happy when that will be
(16:07):
fulfilled. If I'll never run another gel, I'll be very happy. And so I had students working with me,
and they designed some stuff. And the software they designed, you could feed it protein structure
and would give you information back about what that protein probably did. So we developed this
original software, and that was fun. But what I found as they developed the software is they
(16:28):
would predict the function. Well, they wanted to go in the wet lab and find out what it was. So
they started doing that. And then what we found… this is in my research lab, and I was sharing this
with a couple other professors… and what we found was that they started acting like scientists.
They started looking in the literature to find a better assay to use, they started walking up
(16:48):
to me and saying, “You know, Dr. Craig, we think that's not a good idea. We should do it this way
instead.” They came up with alternative proposals. So we talk about experimental design as scientists
and part of that is making something that's going to be statistically robust, but part of
the experimental design is choosing the tools. And they started choosing the tools. One of my
students actually called up the Head of Research at Argonne National Lab and talked to him for an
(17:12):
hour about our project, and never even asked my permission. And that was fantastic. So they're
collaborating with each other. They're going to people in other departments and saying, “We tried
to do this, it didn't work. Can you help?” As I said, I’m a computational biochemist, if something
wasn't working in the lab, I wasn't the guy to ask. They asked the other faculty in chemistry
and biology and biomedical sciences. And so we saw that, we started to incorporate additional tools.
(17:37):
About the time this project really started working well in our research lab, we had funding from the
National Institute of Health, and they decided to go a different direction. And so our funding dried
up, but our program officer was very supportive at NIH, and she recommended we contact NSF and
go through their program. They have a program called Improving Undergraduate STEM Education.
(17:58):
STEM stands for science, technology, engineering, and math. So we approach them, and at the same
time, our first round of money was unsuccessful, but I presented a poster at a conference, and at
that conference, I met Rebecca, and I also met Mike Pikaart, who's another member of the team,
a couple other folks who had joined the BASIL project at that point, and we've had new people
(18:19):
join us since then. And so Rebecca went back to Ursinus all excited, and presented it to Julia,
who was at Ursinus at the time, and Julia signed up. Rebecca was attending a conference hosted by
another organization that they’re all affiliated with, called BioMolViz, which is focused on
molecular visualization of biological molecules. And that's where she met Bonnie. And so Bonnie
(18:40):
joined the team, and she was, as far as I could tell, it was like the day she joined the team,
she'd been there all along. It was a very smooth fit. One of our other team members was doing some
stuff very much like what we do, and one of his colleagues at the University of Richmond said,
“You know, there's some people doing this called BASIL. You should contact them.” And so now he’s
part of our team. And so we have other people that attend our presentations at conferences,
(19:03):
and they join the team. And so there have been ongoing themes. We want to have continuing use
computational tools and building for the future, I think. We also want to have authentic science,
they really do get into the wet lab and do stuff. We want to share what we're doing at
conferences. We want to write papers about it, and we want to act like scientists. We want to
model that behavior for our students. So I'll just add that Paul gave a very good
(19:27):
introduction to how we got started, and we've been working together on this for 10 years now. So we
started in 2015, so this is a very timely podcast, in that we are in our 10th anniversary.
Yay, BASIL. So, how many faculty are involved in
the project overall? You've mentioned a few people in addition to those of you on the podcast.
(19:49):
So we have a core team, and that varies between about 8 and 10 instructors. So there were the
original folks who were involved with the project. People retire. They find other projects they're
even more passionate about, new people come into BASIL, but that core team sits at about
8 to 10 people, and there's a variety of roles. As the number of users of the material has grown,
(20:13):
we've had to be more organized about how we keep ourselves, as the community, organized. So we
are all members of the steering committee, so we're responsible for high-level things,
thinking about funding, thinking about how the different subcommittees work together,
about how to let folks know we exist. We also have an instructor recruitment onboarding committee,
(20:36):
so they help show new users what our curriculum looks like and support them as they start to
adopt that curriculum, or think about how they can adopt it on their campus. We also have an
instructor support committee that's focused less on new folks and more on supporting the folks
that are already using BASIL. So as we update modules or find new tools, offering workshops
(20:59):
about how to use those and supporting people when they look at things like promotion and tenure:
how can faculty use this as part of their natural professional development cycle? We also have an
assessment committee that looks at both how do you as an individual instructor assess the work your
students are doing in class, but also on a larger scale, how do we assess that this curriculum is
(21:24):
achieving educational goals that we want it to achieve. There's also just some housekeeping.
We have a website and data management team. We do collect data. We run a website. So there's just
some logistical support that has to be provided. Another key part is keeping modules updated. So
some of that happens organically as we use them in our courses and we say, “Oh, in our instructions,
(21:47):
we say, you will see a button to click on the right hand side. It's no longer a button. Here's
a different workflow.” Sometimes the online tools that we use get a complete redo, and we
don't know when that's coming. We don't run those tools. That happened with our SwissDock module,
we had great instructions, and then they did an update, and none of our instructions were relevant
(22:09):
really anymore. So we had to go in and do a major update on that one so that it would be usable for
faculty again. There's also tools that get retired and new tools that we discover. Probably the last
thing that drives those changes in modules are pedagogical demands. Certainly, the pandemic is
a good example of how you need to adapt suddenly. And so the computational modules were really great
(22:35):
for transitioning to a remote model. But we also had a faculty member develop some Choose Your Own
Adventure wet lab substitutes, where the students come into a Google form, they have to choose
what they want to use, and if they make the wrong choice, it routes them back and says, “That's not
right. Try again.” And so they couldn't be in the wet lab at that point, but they were able online
(22:59):
to kind of step through that whole lab experience and really get a sense of what it would have been
like had they been able to be in person.If I can jump in… And a great thing that
happened during that time is, because we are a community, we were sharing data so if one of us
had gotten far enough with our students that we had some data, we were sharing that with
(23:20):
other BASIL users so that their students could, even if they couldn't be in the lab anymore to
actually collect their own data, they had data to analyze. So that's how some of the
community can help each other out too.I think the original question was,
how many people are involved? And so we have our core team, but then we have a lot of other
people who have attended our workshops, or are using our stuff. Whenever I go to a conference,
(23:45):
I search the abstracts in the conference to see if anybody mentions BASIL, and inevitably
we find one or two folks who are doing stuff that we've never met before, and we talk to
them and we offer them our support, and we have a Slack channel for BASIL participants,
and we have, what, 200 people there?Yeah. So we just checked the numbers yesterday,
where over 200 people are connected to us on Slack, and we estimate that we have at least
(24:09):
50 campuses using the curriculum in some form or another. And as Paul said, part of that estimate
comes from conferences, the people who reach out to us, the people who are attending any of
our workshops. So most of our instructor support workshops are virtual, and we have gotten much
better at record keeping to keep track of who is attending, where the people are that are using
(24:30):
things. And like I said, we're in Puerto Rico right now, running a couple workshops. We did a
workshop here in San Juan yesterday, and we'll be traveling to Mayagüez this afternoon and running
a workshop tomorrow. We've done a number of other in-person workshops, new adopters, the community,
and we've done even more virtual workshops to recruit new adopters to the community. We also
(24:53):
give presentations at conferences, so there are lots of people. There is broad interest.
And so I think Paul mentioned that we regularly attend ASBMB meetings, the American Society for
Biochemistry and Molecular Biology, but members of our core team have also been invited to speak at
American Chemical Society meetings, at biophysical society meetings. Schrodinger is a software
(25:18):
developer, and they've invited us to speak during their educators’ week. And this is driven by
the community often, that people hear about us, they talk about us, and more people come in.
With a project that seems to be scaling up and up and up, I'm sure sustainability and scale
and thinking at scale is always at the front of your mind; these things aren't free. So
(25:41):
what's on your mind related to these issues?I do think about costs. So it costs about as
much to run BASIL on a campus as it costs to run a traditional biochemistry lab. So the expenses for
BASIL are not unusual. The equipment that we use is not unusual. All of our software is free, and
it all works through a browser, and so that's very sustainable. Now, the people are really, really
(26:04):
important, and we really like working together. It's a very cohesive group, good friends. We work
together, we socialize, we stay in each other's houses, whatever, when we're in town, and that
kind of stuff. And so there's a very positive reaction and relationships that exist here. I
think that's an important part of sustainability. Also talk about making it work on our campuses,
(26:25):
and there's something called the four frames model and thinking about the factors and effective
process. And there's also, what are the barriers to getting people involved and try to identify
and overcome those barriers? Our current grant funding is actually focused on those barriers.
So, as Paul said, our current grant funding is focusing on identifying barriers, and especially
(26:47):
barriers that might exist at different institution types. So one of the things we're doing in our
current workshop, is really exploring that with the attendees and the barriers that we identify,
of course, are time and expertise, and those seem to be common across all institution types, because
this sounds like it could require some different things than a traditional biochemistry lab and so
(27:13):
for sustainability and trying to overcome that barrier of time, that's where our core team has
really tried to do a lot of the work to make the curriculum something that you can adopt without
having to put in as much of the time to create all the materials that you might have to do if you're
just creating a new curriculum on your own. As for cost, we've really tried to keep the cost down as
(27:36):
well, so we use web-based computational tools, so that there's no requirement to purchase software
or software licenses. It makes it more accessible to students who have different devices. So you
need a device that connects to the internet, but you don't need to have a laptop with a certain
processor speed or capacity to install software. You can work from a tablet or a Chromebook,
(28:00):
and we've also tried to use the traditional wet lab method, so that the cost of the wet lab is
not any more than a traditional biochemistry lab might cost, and we may even be able to have it
cost less than a traditional lab, because half of the work is using free computational tools,
which means, if you used to purchase materials for 14 weeks worth of experiments,
(28:23):
now you're purchasing materials for maybe seven weeks worth of wet lab experiments.
I'd like to talk just a little bit about this idea of CURES as a pedagogy, and part of the
excitement that I find being involved in BASIL is introducing this pedagogy to faculty around
the country, and whether they're choosing to use BASIL or not, just opening their eyes to
(28:45):
a different way of teaching biochemistry, or whatever it is that they happen to be teaching,
and that is really an exciting place for me, personally, to be. I've seen the power of it. I've
seen how scary it can be from an instructor point of view. And I get scared every time I'm about to
start my BASIL curriculum, which will start in a week for me, but I know that I have the support,
(29:09):
and I know that it's so important and so effective for these students that spreading
the word is really satisfying to me. I think another issue that comes up in terms
of sustainability is that at one point, BASIL was small enough that everybody doing it could
hop into a online meeting room together and have sort of a town hall meeting format and just talk
(29:32):
about things. Now with over 50 campuses and all the different time zones that are represented,
that's not a supportable model, and so we've had to do things like create the Slack channel,
where we can have asynchronous communication and figure out ways to offer virtual workshops. And
we also now have begun offering something called BASIL week, where it's a full week of workshops
(29:58):
about all of the different kinds of topics that users of the material might be interested in. So
some walkthroughs of computational material, some conversations about how to assess student work,
conversations about how to leverage the work that you do with BASIL in terms of required
professional activities that your institution asks you to do. So really thinking about how to build
(30:22):
that community and how to sustain that community as it grows is another part that we focused on.
Is this something you'd recommend to other STEM disciplines, because we do know that we lose a
lot of students in the STEM pipeline, and maybe this way, we'd get more people intrigued by the
process of being a scientist or becoming more engaged with science as a potential career.
(30:46):
Yes. So we ran a workshop about a year and a half ago at Prairie View A&M University,
outside Houston, Texas, and they had encouraged people from across the sciences to attend our
workshop. So we had attendees from biology and chemistry, as we expected, but also from physics,
math, computer science, the School of Education, and they even had a couple Deans come to our
(31:09):
workshop to see what was going on, and that was a very useful space for them, but also for us
to see how others might be inspired by what we've put together and looking for ways that
they could use our curriculum as a model for creating some additional kinds of experiments,
some more research-based things in the labs across the disciplines. And the person who was there from
(31:35):
education was also very interested to know that this was happening, because there seems to be a
disconnect in those who are training in education departments and schools and those who are in STEM
departments and divisions. We don't necessarily talk to each other, so we don't know that we're
all thinking about student assessment to some extent and student learning and what the outcomes
(31:59):
are. And so I think that this is a good way to engage students in science. And I use it in my
courses for Chemistry and Biochemistry majors, but also in my courses for non majors.
I think additionally, one of the drivers for other STEM disciplines using a CURE is that the research
shows that it engages students. Students are retained, they're excited. It's increasing the
(32:24):
number of students who have a research experience under their belt, especially underrepresented
students in that field, and that, in and of itself, is going to increase the pipeline of
students moving through to the next level of their careers. Anecdotally, I have students
always coming back to tell me that when they were on a job interview or a graduate program interview
(32:49):
or a medical school interview, that they talked about their experience with BASIL and that it
made a difference, and people were really excited to talk with them about their experiences.
Yes, and I think we did mention earlier, but I'll just re-emphasize that the CURE model,
the course-based undergraduate research experience model, is a way to engage more students in
(33:13):
authentic research experiences. So many of us are familiar, especially in the STEM fields,
with the one-on-one research model, where a student approaches me to join my research lab.
But there's a limit to how many individual students I can take into my research lab,
due to the space in the lab, my capacity to mentor each one of them and the resources to support
(33:38):
their projects. But if we can do some level of research in the courses that we teach, then that
really gives every student some amount of research experience. And the studies show that whether it's
a week or a couple weeks, even, you are going to gain some more identity as a scientist and
project ownership than in a traditional lab setting or a traditional lab course
(34:01):
and a semester-long project can be even better for that science identity and project ownership.
It also is a way of reducing the barrier for those students who might not know or
be brave enough to approach a faculty member to ask about individual research. If they're
enrolled in your course, they're doing it, and so it lowers that barrier, and may be attracting
(34:23):
and retaining some students who otherwise might drop out of the pipeline of STEM.
So, we always wrap up by asking: “what's next?”
So part of what we'll keep doing as we go into the future is to maintain the baseline that we have:
keep updating modules, keep looking at new tools, replacing tools that get deprecated. We'll
(34:44):
also focus on maintaining community. We're also beginning to have collaborations with other active
learning communities, so communities that are not the BASIL CURE, but that provide active learning
opportunities to embed in your campus curriculum and trying to see how we can all work together
(35:04):
to improve biochemistry and STEM education collectively. So to put all our efforts together
and pull in one direction together. We're also especially interested in BASIL in computational
tools. So a big breakthrough in biochemistry in the last couple of years is the ability to
use computation to fold proteins and determine their structures. So when we started with BASIL,
(35:31):
there were about somewhere between a hundred and 150,000 protein structures. That had been solved
with the computational tools available now, there are over 700 million folded protein structures,
structures that anyone could use to do research. So we're interested in adapting BASIL to,
(35:51):
first of all, work with data sets that are so large now, every year they've grown tenfold.
So how do we keep up with data sets that are so large, and there's also a lot of new tools coming
out? So how do we keep our curriculum up to date and relevant? But a really great thing about this
is that before there were 150,000 proteins, many of which already had assigned function,
(36:17):
now, with 700 million protein structures, most of those don't have a verified function. So
there's an opportunity for anybody who wants to do BASIL to keep as many undergraduates as
they have engaged in research for as long as they wish. So it's a very exciting time for
(36:38):
BASIL and for the biochemistry community.Yeah, we're hoping that the outcome of our
research on barriers will lead us to be able to aid in overcoming those barriers to CURE adoption
and implementation. Are there ways that we can address that? So I think that's another future
goal for us is really looking at not just adoption of BASIL but adoption of CUREs in
(37:00):
general. And as Bonnie said, we're working with some active learning communities, and if we can
identify what the barriers are, that's the first step in being able to overcome those barriers.
We've been using these computational methods and emphasizing them and it's really about that, which
is probably the most of any CURE that’s out there, and we're hoping to have an impact,
(37:20):
at least to help other people who are doing course-based undergraduate research
experience with their students, to identify and implement those tools. And based on our experience
with getting these tools into our teaching labs, I think we could probably provide helpful
insight to any group that wants to introduce computational tools into their teaching lab,
even if it's botany or nutrition, whatever is out there. I think we could do that.
(37:45):
Well, thank you so much for joining us. There's lots of great information that you've shared. I
think no matter what discipline one might be in, there's lots of rich information. Thank
you so much.Thank you.
You’re welcome.Thank you.
Thank you.Yeah, thanks a lot. This was great.
If you've enjoyed this podcast, please subscribe and leave a review on iTunes
(38:07):
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our Tea for Teaching Facebook page. You can find show notes, transcripts and
other materials on teaforteaching.com. Music by Michael Gary Brewer.
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