Transform Your Teaching

Does Backward Design look different now that AI has changed the world of education? How does “starting with the end in mind” produce courses that serve students well? Join Rob and Jared as they discuss how a backward-design approach enables teachers to create AI-resilient courses.
  
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What is Transform Your Teaching?

The Transform your Teaching podcast is a service of the Center for Teaching and Learning at Cedarville University in Cedarville, Ohio. Join Dr. Rob McDole and Dr. Jared Pyles as they seek to inspire higher education faculty to adopt innovative teaching and learning practices.

Narrator:

This is the Transform Your Teaching Podcast. The Transform Your Teaching Podcast is a service of the Center for Teaching and Learning at Cedarville University in Cedarville, Ohio.

Ryan:

Hello, and welcome to this episode of Transform Your Teaching.

Ryan:

In today's episode, Dr. Rob McDole and Dr. Jared Pyles continue our series on AI resilient course design by talking about backward design, one of our favorite things on the Transform Your Teaching podcast. Thanks for joining us.

Rob:

Well, Jared, we've been talking about AI resilience here at Cedarsville, and we're doing kind of a back to the basics

Jared:

Esque.

Rob:

Esque. Series. Yeah. We did some work last time talking about what is AI resilience. And we did that in terms of AI resilience versus AI resistance.

Rob:

Mhmm. And today, we're bringing it back to where we always begin, especially here at CTLP, and that's backward design. Mhmm. Start with the end in mind, be clear about what you're wanting to accomplish, and move backwards from there. Thus, backward design.

Jared:

Start with the end. In mind. What's your course? What do you want the students to end the course being able to do? That's also where you get your objectives.

Jared:

Right. Here's how I want them to end. Here's how I want them to show it.

Rob:

And just to tie it back into some of the shifts that we we talked about in the in the last episode, we did talk to some degree as one of those shifts being a holistic assessment of a program and not just a particular course.

Jared:

Right.

Rob:

So when we talk about backward design, yes, we are going to be talking about it from a course perspective. But at the same time, we're also talking about backward design in terms of a program because you want that alignment and we talked about alignment in the last episode. So let's talk about it. You are a instructional designer here.

Jared:

By trade. Yes.

Rob:

By trade. Uh-huh. And this is something that you live with By day. By day Mhmm. And by night.

Jared:

Hopefully not.

Rob:

Oh, okay.

Jared:

But you're thinking Although sometimes it it never goes away.

Rob:

It doesn't let you go.

Jared:

Not go away.

Rob:

Well, one of the things that, you know, I'm always curious is how has backward design helped you in terms of your usage of AI? And then how would you kinda like define it? You kinda defined it a little bit ago, but how would you define it now, you know, with AI coming and your usage of it?

Jared:

That's interesting. I didn't expect you to ask me that question.

Rob:

But Well, you started with backward design means beginning at the finish rather than at the

Jared:

Yeah. But I don't know how if it's changed in light of AI resilience. Only thing that really changes for me is the assessment process and more of an emphasis on what they're doing formatively and how I'm actively looking at their formative assessments along the way to make sure that when we get to the product, there's less emphasis on that end product and more on the process along the way.

Rob:

I I totally agree. I don't think it changes how you go about finding what you start with, which is the end in mind.

Jared:

Good. I'm glad that I didn't you weren't gonna go, you are totally off base.

Rob:

No. You just you just evidenced the reality that it hasn't changed for you. Right? And it hasn't changed for me either. And I think that's a good place to start for a lot of people is to say one of my previous bosses used to say, the more things change, the more they stay the same.

Rob:

And I think this is one of those things. In terms of education, this hasn't changed. Yeah. Going through the process of doing a need, it's analysis, which I don't think we've really ever done on this podcast because we've always assumed everybody can do needs analysis. Like, do you understand the difference between a real need and a felt need?

Rob:

Mhmm. Right?

Jared:

Mhmm. And that's important to do when you're looking at backward design.

Rob:

Well, yeah. I mean, you've got to

Jared:

Even though we haven't talked about it.

Rob:

No. We haven't talked about it, but maybe just evidencing there a little bit for people. Maybe they've never heard that. As my wife has said to me on more than one occasion, don't assume that everybody knows what you're talking about. And so I think I'm just trying to, you know, as you guys have reflected back to me, like you and some of our other colleagues, reflecting back to me things I've actually said, which is make the, you know, make the implicit explicit.

Rob:

So these are the things that go on in my mind when I'm doing design, when I'm thinking about what is the end goal is needs assessment. Well, what's a felt need? In other words, what are students actually expecting at point about this time? I should think about that, but it's probably not where I need to land. I need to somehow do a blend of at least this is the way I think.

Rob:

I'm curious as how you might think. I need to blend somehow felt need and real need, like, the thing that they actually need to be able to do. Think Right. Or feel. Right?

Rob:

But if I totally ignore their felt needs, I think I might miss an opportunity to connect.

Jared:

Well, I think it also deals with motivation too. Right. I would say that's where your the felt needs being in there are helping to stoke that intrinsic, extrinsic motivation to help them want to succeed because you make that connection between the felt need and the real need. Because some will come in saying, yeah, I totally am with you on these real needs. I need to know x, y, and z by the end of this class.

Jared:

And while the felt needs may be implicit and may not be part of the objectives, they're still important because I think within those felt needs are the motivation needed to spur them on to wanna take care of those and accomplish those real needs.

Rob:

I think it's worthwhile of just reminding our listeners if well, we're even putting that out there if they've never heard of these things before, never thought of it in that way. Yeah. But we do have some, like, useful questions when you start with the end in mind. One would be and you were just alluding to it here just a minute ago. What should students still understand or be able to do after this course?

Rob:

Yep. Or what kind of judgment or character or responsibility does this work require? And sometimes we can miss that too, like, that they understand why they're in the course.

Jared:

Right. And that's not necessarily this is where I think AI resilience steps in because that second question, what kind of judgment, character, responsibility does this work require, that's not really something you tease out in your objectives if you're doing just strict backward design. To me, that's where you're looking Those were the marks of AI resilience come in, idea of judgment, character, responsibility, that aren't necessarily part of the, quote, unquote, traditional backward design.

Rob:

Well, let's move on to the second part of this, because we basically have three parts that we're gonna work through today. One is we just discussed the backward design starting with the end in mind. That could be an outcome. It could be a program outcome. It could be a course outcome.

Rob:

We usually deal with it in terms of course outcomes. Mhmm. The second would be evidence of that. What is the evidence that we're going to say needs to be there in order for us to say the outcome has been fulfilled? Yep.

Rob:

And then we have the actual learning pathway or the pathways that we might construct and provide for students so that they can get from their starting point to successful outcome adherence. The big one, I think that where we're gonna see AI resilience and where things change is in this evidence part.

Jared:

Oh, yeah.

Rob:

And I think that's where people are finding the biggest crunch, is that the ways that they evidenced learning or evidenced outcomes before are things that now AI can just readily do. So the output is easy for them. So how do we, you know, decide what will count as trustworthy evidence?

Jared:

This one to me, I don't know. I've I've always felt this way, and I've never really I've articulated in different ways, but I think it just brings to the front again the importance of modernizing assessment strategies in education. We have been doing multiple choice, truefalse. I'm pretty sure that Aristotle did it. Like, it's been so long that I know that's probably not true.

Jared:

Galileo, at least, if if he adjuncted a course about the moon. Anyway, I I'm being hyperbolic, but the fact remains that we have these assessment strategies that with AI, like you said, are easily, easily faked. And they were easily faked before because and we've seen it on this campus, and I'm sure we've seen it all over The United States and the world, that tools like Kahoot exist, that where people just throw up multiple choice, truefalse, short answer answers, and students are not learning. They're just taking that answer, throwing it on there, taking the A, and just going on, moving on with the rest of their lives.

Rob:

And some would actually pay other people to take

Jared:

the stuff Some, for I mean, there's those essay farms even that exist. So, like, the idea that AI is ruining education because of its ability to fabricate students' work on these assessment tools is not wrong, but it's not like AI is breaking new ground in that. This idea of being able to take these ancient assessment strategies and replicate them is nothing new. So, I think it's really, really crucial that we take a look at how we assess and figure out new ways of changing it. And if it comes through AI resilience, that's great.

Jared:

If we spend more time dismantling how we've assessed for so long and actually look at it and boil it down and say, Is this actually beneficial to our students? Is it showing their learning and showing that they are retaining the knowledge? Then great. But if it's not, and if there's other ways of doing it that get to the real core of learning, self paced stuff? Is it through reflection?

Jared:

Is it through assessing, assessing in smaller portions informatively? And I don't know, but we have to take a serious look at it. We can't just be like, oh, I'll just do multiple choice and true false because number one, it's the way I learned. Number two, it's easy to grade. There has to be a point where we're just like, maybe I'm saying too much, but I just it's to a point where it's like, what else is going to happen before we realize that we need to adapt how we assess?

Rob:

Yeah. Because ultimately, I think probably what you're trying to get at is what evidence really shows that students can make the right judgment calls in this particular area? Yeah. Or that they can analyze a problem or analyze a work situation or analyze a business strategy or analyze a piece of fiction, and then synthesize that and evaluate it. Right?

Rob:

So those are some of the things, like, when you think about it, you have to think, does my assessment actually do the things that I want it to do? Does it actually tell me what I wanna know?

Jared:

And before that, like we mentioned before, you have to know what it is you want them to know.

Rob:

Yeah. Yeah.

Jared:

You can't just be like throwing stuff at the

Rob:

wall and see if something sticks. I have seen that, and I have actually learned from those kinds of learning opportunities where there haven't been clear objectives. So for me to say you can't learn anything without super clear objectives would be a statement too far.

Jared:

I agree.

Rob:

I think it's also, as we like to say here, best practice generally accepted. It's better to have a a clear definition of where you're aiming at.

Jared:

Yep. Draw the target.

Rob:

Yeah. You draw the target, and you're clear on what your target is. Otherwise, if you have no target, you'll hit it every time. That's a difficult thing is an off awful lot of times, well, our objectives and our assessments don't really actually one does not prove the other. Yep.

Rob:

And so that's like you said, this has been going on for some time. This isn't new. But now AI has brought it to a sharp focus.

Jared:

Let me ask you this. I teach gen ed.

Rob:

Mhmm.

Jared:

What would you say to those who teach gen eds and are like, they don't need to know to analyze for this 1,000 level gen ed course? I have an answer, but I wanna hear yours. Ready, set, go. Actually, I don't have an answer. I have an answer for my course, but not any of this.

Jared:

Well, I

Rob:

think that's one of the things we're we're trying to get at with this conversation is when we talk about finding trustworthy evidence. As an institution, we've already made a decision about liberal arts gen ed. Right? And that's part of it. Literature is one.

Rob:

Because we believe there's something that we can learn from that. Having this broad understanding of knowledge that gives you more to draw from as as a student. I know for myself personally, and I know understand this is anecdotal, but, you know, Herman Melville's short stories that I went through here as a student still affect me today. Billy Budd, that was one that really brought to sharp focus much like Les Mis, justice and mercy. You know, that's not the only one.

Rob:

Celestial Railroad by Nathaniel Hawthorne for me. That was a huge one. You know, this idea of shortcutting to heaven or to the celestial city. If you're familiar with Pilgrim's Progress, John Bunyan. Mhmm.

Rob:

And Nathaniel Hawthorne writes this short story, which is kinda funny. He writes a shorter story. Uh-huh. Says, hey. Get on this train and

Jared:

it'll take Hawthorne didn't know what a short story was. He just oh, it's short.

Rob:

You can read it

Jared:

in one sitting. No. You can't.

Rob:

And he ends up you know, at the end, you end up I'm sorry if I'm spoiling this. But at the end, you know, it takes this train, it's it's paralleling Pilgrim's Path the entire way and you see the celestial city and then all of a sudden it takes a 90 degree left and and lands you in hell. Mhmm. And that reminds me of the scripture that says, you know, a way may seem right to a man, but in the end, it leads to death. There is a way that seems right to someone, but in the end, it leads to death.

Rob:

So it's like taking the time to make sure. And I think this is the kind of education that we provide, Lord willing, here at Cedarville, and why you would want something like a literature course because you'd wanna stop and say, why did they write this? Yep. What was their purpose, and how does it fit with what you understand about scripture? Because having those types of of ability, you know, being able to contrast is key when you start talking about analysis.

Jared:

Yeah. Because that's so that's a

Rob:

Synthesis.

Jared:

That would be would be my answer is is that how the skills that you learn, potentially could learn in agenda and literature class, will transfer. Exactly. So going to the synthesis and analysis. No. You don't necessarily need to be able to analyze the old man in the sea and Nathaniel Hawthorne's young Goodman Brown and figure out the parallels between the characters, which I don't I'm just coming up with too.

Jared:

I don't think you can actually do that. I don't think it would work. But but anyway, you it's not necessarily you need to know those skills. It's more of the I mean, specifically to the literature, but the the skill of analysis and synthesis is a crucial skill that crosses subject areas. Yeah.

Jared:

So that's what we're talking about.

Rob:

Right. And I think but the more you get to do it in different areas, and that's kinda what, like, a liberal arts program does, It allows you to practice that in different areas to show you that it's all connected. Right. You don't live in a bifurcated

Jared:

Good word.

Rob:

World. Mhmm. You're not living in these segments where you jump into one bucket and you're a engineer, and you just don't worry about anything else but engineering stuff. Which then reminds me of a of a further cry from scientists, Nion. Oh, I don't know how long ago it was ten, fifteen years ago.

Rob:

When they when when the CRISPR technology came out. Mhmm. I don't know for those of our listeners who don't know what CRISPR is, it's basically the ability to splice genetic material. So you can, one, find a genomic sequence that they want to replace, and they can cut it out, and they can put something else in its place. Understood.

Rob:

So basically, they're messing with the building blocks of life in a very precise way. And they can make whatever they, you know and that's what one of the scientists said. They were like, literally, I think it was a TED talk and they were talking to the TED people and they were saying, please provide us with morality essentially. Please provide us with ethical boundaries. We don't, you know, we just made this tool.

Rob:

We don't know what to do with it. And that's kinda where I think we've ended up in AIs, like the tools are outpacing the character.

Jared:

As Jeff Goldblum said in Jurassic Park, your scientists were so preoccupied with whether or not they could, they didn't stop to think if they should.

Rob:

Yeah. Yeah. And I think we're now in a point where if you've been if we've been ignoring this, we've got to talk about what is trustworthy evidence.

Jared:

Yeah. It's gotta happen.

Rob:

Yeah. We can. Which then that leads us to the last part, which is designing the learning path.

Jared:

And this is the cyclical cycle, which is the most redundant. It's the pattern of learning where you go through the instruction, practice, feedback, revision, and that could repeat, repeat, repeat, repeat until you get to the application part of it. This is something we're seeing in STEM education, but it's where you really give your students a real field relevant opportunity to compare, question, create, improve, to learn from, and throughout that process, you're giving feedback and you're reinforcing, redirecting, doing what you can so that when it gets to the end product, as I've said so many times, you are not surprised by the outcome.

Rob:

Well, I was just gonna say, and that's where AI comes in. Right? AI can really be a benefit to us here. I and I can think of several ways. Early practice is one of those.

Rob:

Right? Yes. In that in that sequence and getting students the ability to practice in a particular area.

Jared:

In a safe Yeah. Low stakes environment.

Rob:

Right. And and and so very formative, like you're fond of saying, formative assessment. So what's one way you can do that? Well, you can you can create an AI bot or an AI prompt that will essentially provide simulations for students in different ways, but along the same lines. So if you're wanting to have them analyze literature, you might be able to have it actually create literature that was very similar to what you're studying

Jared:

Mhmm.

Rob:

And provide similar areas that you want them to go in, recognize, be able to identify. And then you can have them do that in AI and capture that entire interaction so that you have an actual example, an artifact, if you will, of them interacting with a simulation.

Jared:

I don't know if I agree with using AI to create literature. Because I maybe mainly because I haven't seen a good enough example of it where it generates something that isn't like we were, like Ryan and I have a course that we're developing right now, and we're trying to develop quiz questions from a book, and the first couple iterations were terrible.

Rob:

Yeah, that makes sense.

Jared:

Because it's creating these distractors in the multiple choice, and they're poor, it's so obvious. And so when it comes to creating literature, to use your example, where let's say we want them to be able to identify symbols and their usage, or like we're talking about vampirism right now, where there's vampiric relationships that aren't necessarily twilight or vampires, but they're actual, where one person is feeding off of another and the other person doesn't realize it, or it seems like it's a normal relationship, there's actually vampiric. I can't imagine AI developing an original enough of a story that isn't so painfully obvious that it's AI generated. Yeah. So, in my case, I don't necessarily use the AI for that, but I can use AI trained on the curriculum that we're using and say, and use it as a tool alongside to be like, you know, where are you seeing symbolism or vampirism in this text?

Jared:

Like in a text that already exists and having it reinforced that way of the, you know, because when we talk about synthesis and analysis and writing, the tool could then be used to train them to help them before it even gets on my doorstep. They're looking at it. They've gone through the process of using this tutor or whatever it is to strengthen their own stance using these tools, and then they present it to me. No.

Rob:

I'm I I don't disagree. I think just like you're refining those distractors, though, I think you can you can have AI also work off of a known piece of literature.

Jared:

Right.

Rob:

Having said that, some are gonna be like, well, that's not right. You know, that that could lead you down to intellectual property issues or copyright issues. Sure. That has yet to be decided by law fully. I personally think it's fine.

Rob:

You take it. Utilize it to create even more opportunity to understand that original work, understand the themes, and understand something that you want your students to learn from it or examples of it, you could do it through other ways. And I'm not saying you have to. I'm just saying that it's a for early practice in in a course like literature. And obviously, in the math and the sciences, I'm sure there are lots of faculty.

Rob:

Matter of fact, I'm I know this. There are several that have already figured out ways of doing it even to show students it purposely will give them incorrect things that they then have to

Jared:

Sure.

Rob:

Identify and say why it's wrong and what part of it's wrong. So it could be like a chemical formula. It could be something along those lines, mathematical formula, an engineering, I don't know, circuit. Like, if you're talking about electrical engineering, it could be a circuit of some sort, and it it produces this circuit and you have to just decide, is this circuit actually work? That has to come from someplace where you actually know what you're doing.

Rob:

Yeah. That's just one possibility of where you might use AI, like, in those learning paths, but you could identify them there when you should and should not use AI. And it's all gonna be based on where do you wanna end up.

Jared:

Mhmm. Name the outcome.

Rob:

And then what evidence are you wanting And to then how can AI either need to be left out

Jared:

Mhmm.

Rob:

Or brought in to help you bring that evidence forward.

Jared:

Yep. So, yeah, I mean, backward design to AI resilience is, like we've said, it's pretty much backward design at its core, but then some AI sprinkled in, especially there at the end there with the learning path and figuring out where should it or should it not be used, and that's up to the discretion of the instructor. It could also be a conversation you have with your students themselves and if they want to use it or if not to use it or they help design it as well and help you out with that aspect of it would be a bad idea.

Rob:

To to sum up, at least what it means to me is backward design isn't going away. If anything, it helps us put AI in its right position and where it belongs. And and also, if we've done our due diligence in the first two areas where we have been specific about our outcomes, and then we also have decided clearly what the evidence is that we're looking for, then AI can be used or not used. Yep.

Jared:

Yep. So in our next episode, we're going to go down the path of designing a course. We're gonna start with some lecture. We're calling them lecture, although direct instruction, I guess, should be what we should call it instead. Where we're talking about objectives, and then we're going to a lab where our special guest is going to come in and we're going to develop the objectives together.

Jared:

Then we're doing assessment and then content delivery and stuff like that.

Rob:

We're gonna dive deep into We're gonna

Jared:

dive, and if you wanna follow along at home, we'll introduce the course alignment grid, our next episode that we use here.

Rob:

Oh, I'm looking forward to it.

Ryan:

Thanks for listening to this episode of Transform Your Teaching. If have you any questions or comments about our episode on backward design, feel free to reach out to us at CTLPodcastcedarville dot edu. You can also follow us on LinkedIn. And finally, don't forget to check out our blog at cedarville.edu/focusblog. Thanks for listening.