[00:00:00] Bonni Stachowiak: Today on episode number 459 of the Teaching In Higher Ed Podcast, Engaging Students through Collaborative Research Projects with Rebecca Glazier and Matthew Pietryka.

[music]

Welcome to this episode of Teaching In Higher Ed. I’m Bonni Stachowiak, and this is the space where we explore the art and science of being more effective at facilitating learning. We also share ways to improve our productivity approaches so we can have more peace in our lives and be even more present for our students.

[music]

Today on the podcast, I’m joined by Matthew T. Pietryka and Rebecca A. Glazier. Matthew is an associate professor of political science at Florida State University. His research examines how people’s political choices are influenced by their friends, family, coworkers, and other acquaintances. He also studies methods for improving the validity and reliability of survey measures.

He teaches courses on political behavior, political psychology, media and politics, social network analysis, and research methods. Rebecca Glazier is a political science professor in the School of Public Affairs at the University of Arkansas at Little Rock. She studies the scholarship of teaching and learning and is passionate about improving the quality of online education.

Dr. Glazier is the author of Connecting in the Online Classroom: Building Rapport Between Teachers and Students. She’s also the director of the Little Rock Congregations Study, a long-term community-based research project on religion and community engagement. Rebecca and Matt, welcome to Teaching In Higher Ed.

[00:02:04] Rebecca Glazier: So happy to be here.

[00:02:05] Matthew Pietryka: Thanks for having us.

[00:02:07] Bonni: Matt, I’d love you to take us back to the beginning. How did you all get started in collective data projects?

[00:02:15] Matthew: What Rebecca and I are trying to share with as many people as possible is this basic structure for assignments that started to really take form as a model for building and creating new assignments right around the time of March 2020 when I was thrust into online teaching, and I always worry about how I can make sure that my students are taking the abstract concepts from the classes that I’m teaching and finding concrete ways to understand them and connect with them.

It felt like moving online, for me, it was my first time teaching online, everything that was abstract became even less tangible and so I needed some mechanism that I could use to help students make sense of the concepts that are so important for the class. At that point in time, I was reading some of the work that Rebecca had written about building rapport between instructors and students and I was realizing that the assignments that I was using in my in-person classes could do a lot more if I just expanded them to provide more individualized feedback to each student.

[00:03:35] Bonni: Matt, you were talking about the individualized feedback and just this morning we had a panel with students at my university talking about artificial intelligence. They were so amazing, by the way, [laughs] I just have to say that. At the end, there was an opportunity for questions and so I asked them how they felt because they were asked to predict or to observe how faculty, how their professors might feel about artificial intelligence and them using it or not.

Then I asked them how they felt about faculty, I didn’t say it quite exactly this way, but I was trying to ask a neutral question. [laughs] I’m not doing a good job of it now, but how do you feel if your professor were to rely on some of the for-profit textbook companies that provide AI feedback because the first time I ever heard about it, to me it’s like, oh, like a horror movie but I’m trying to keep my face neutral and everything.

I wasn’t sure what was happening in terms of, because I didn’t know a lot of these students very well of like, are they trying to behave themselves, but I’m thinking like feedback is so important, Matt, and it’s so often not scalable and not personalized so that is one of the reasons I get so excited about your project. Before I keep talking, Rebecca, [laughs] why don’t you tell us why you’re so excited about these projects that you’ve been working on?

[00:04:52] Rebecca: I think that this is really needed at this moment in time because so much of higher education is turning to big tech for solutions and thinking that we need these major companies and huge investments of money, but for me, in the research that I do, it has always come down to personal relationships.

When we make those connections with students in our classroom, when we create a community, when students feel like we care about them and their success because we’re doing things like calling them by name and giving them individualized feedback, that’s where you really see the magic in terms of their success and their learning.

Matt was trying to do that with big classes and I think all of us, and we think giving individualized feedback, even on a small scale, but especially on a large scale, it can sound really overwhelming. Matt, with these projects, was able to create these individualized reports using R code that he wrote so that students could contribute to big-picture projects and then get individualized feedback.

This is really flipping the model on its head of what the EdTech industry is telling us right now that we have to pay big amounts of money and get these outside companies to come into our classrooms and do things for us. This is totally professor-created from the ground up with free access code in a publicly available article that is peer-reviewed, but open access so anyone can check out the code and anyone can implement this in their classrooms.

[00:06:27] Bonni: I can remember talking to faculty who teach in a STEM program who were talking about the power that it was for some members of their class communities to be part of that bigger picture, that what they asserted was this is going to allow us to bring more women, more people of color into these fields because they’re not as motivated by the individual drivers.

I love that aspect of it as well. You’re getting the feedback in there, you’re getting that sense of community, and also this sense of, I’m part of a bigger thing. Matt, I’m realizing that we could be speaking more hypothetical right now and it might be helpful to get us a tangible example. Tell us about just one of these collaborative projects that you do in just one of your classes, even though I know you’ve been doing multiple, but just give us a slice of an example of what this looks like in practice.

[00:07:20] Matthew: The very first version of this that I ever used was really taking from my mentor, Walt Stone, who’s now a retired professor at UC Davis, and back when I was a TA for him, he had this assignment. John Geer wrote a book called In Defence of Negativity, and he’s arguing that negative ads in presidential campaigns are actually more useful for citizens than positive ads.

Most people really tend to hate negative ads, but Geer’s argument is that negative ads tend to be more informative and help voters make better comparison of the candidates. He assigned the book and along with the book, he had students use John Geer’s codebook to analyze presidential ads themselves so they could get a sense not just of what Geer is arguing, but really see it firsthand and make a decision for themselves, do they agree with Geer?

Essentially, the assignment then is taking this code book, watching these presidential ads, and classifying them in a variety of ways in terms of how informative they are, whether they’re focusing on policy issues versus character traits, et cetera and so each student is now gathering data. For a long time, I was using some version of that where students would submit the data and I would aggregate it up and we would spend a class time talking about the results. Students could share their results and I could talk about the class averages and we could compare it to what John Geer found back he was analyzing presidential elections in the 1980s, ’90s, et cetera.

We can have a really interesting classroom conversation and what I realized moving online was students had generated all this data with just a little bit more work. I could provide them a personalized report showing them what they found, comparing it to averages from the class, comparing it to what John Geer found in his data.

Suddenly, with not a huge amount of additional effort, I could be meeting each student where they are and showing them what they’re finding and helping them see that A, they’re a part of a whole, so that their work was contributing to the class averages, but also, seeing the places where their perceptions were differing from their class so that we could target either it’s possible that they have unique insight into the ad or it’s possible that they’re missing things that other people are seeing and so you can really help everyone level up in a way that they couldn’t on their own.

[00:09:52] Bonni: Is this one of those things that would, so if I participate in this and I love the– There’s this body of work by this researcher. He’s got what he’s done and then you’re bringing the slice in of the class and also the slice that’s me as an individual. To what extent is it my forming of my own perceptions and my own ideas? To what extent is there a right answer when I get done?

At what point do I veer off into that is just factually not accurate? To what extent is that, “Oh, you brought a new insight,” because you mentioned that the idea of them bringing new insights that are fresh and unique versus I suspect there are probably some of this that you are attempting to teach them a method of analysis that could be applied correctly or not?

[00:10:40] Matthew: There’s no single answer to that question.

[00:10:45] Bonni: [laughs] Turn it.

[00:10:47] Matthew: I think that’s one of the things that I would hope to share with other people who are considering implementing something like this is you can build in both, and triangulate. What I mean by that is, you might have some questions that are very clearly objectively correct or not. Those kinds of questions can serve as a flag where you will notice students who might need extra help on those things. Then you can also use that to break down the data for the questions that are more subjective and see if there’s differences in subjective perceptions based on whether they are understanding the objective things correctly.

There is also where you can build in that automated feedback that can be so useful. In my statistics class, I can show you got this one incorrect. Here’s what you entered. Here’s where it looks like you went off track so that they can target the specific spots where they need to go back and work if they want to understand that particular concept.

[00:11:44] Bonni: Rebecca, we’ve talked a little bit about the value that is coming about with these projects, would you share a little bit more about some of the benefits that we might not be seeing from these initial first impressions?

[00:11:56] Rebecca: Yes. I think what’s so cool about this is that it’s not just for political science classes. If you’re teaching a poetry class, you can have students go out and look for symbolism and a bunch of different poems. If you’re teaching a biology class, you can have students go out and count birds in their neighborhood. In each case, the individual student is coming and contributing to a broader project, and then getting the individualized feedback that makes them feel like they’re part of a classroom community.

That’s what Matt and I found when we looked at the data across a couple of 100 students that Matt taught over a few years. We found statistically significant results that students enjoy these classes more, they are learning more in these classes. They felt like the professor cared about them and cared about their learning. I know that all of us probably as professors if we have been teaching for any amount of time, we’ve probably had experiences where we have connected with students and where we’ve really felt like we’ve made a difference for them.

Those individual experiences, I think, can be really powerful. Those anecdotal experiences. I think what is really valuable about a project like this is that we can bring statistical evidence and say, “Making these connections, making this effort to bring students into a collaborative project to show them their contribution, and to connect with them on a personal level, giving them these individualized reports, that really makes a statistically significant difference for them.” It’s not just an anecdote, it’s not just a feel-good story. It’s real data.

[00:13:33] Bonni: I’d like to ask a clarifying question then I want to bridge back to something you just said, Rebecca. The clarifying question is we’ve been talking about it in terms of collecting data that that is maybe from a body of research externally. Rebecca, you mentioned being able to count birds. I know that, that, at least, in the United States, we have, I’m not sure I’m going to get the name right, but the national bird counting, I forgot what it’s called. I’m really failing society right now. I’m going to look it up and I’m going to put it in the show notes.

Anyway, there might be a body of some national or international research body that we can compare our slice of our class to. Do these collaborative learning projects go class over class so that I can see how my unique learning community this term, or this semester might compare to last year or the year before or before that?

[00:14:28] Matthew: Yes. That to me is one of the great values in using these. It can take quite a bit of work to design it initially and to refine it. Once you’ve got it running each new semester, it’s quite straightforward to run it again making some modifications based on feedback you got the last time, and over time you develop this beautiful time series, that can be very engaging for students to compare themselves to previous semesters. It’s rarely going to be entirely clear when differences are occurring because of something that has fundamentally changed and what you’re examining versus the students are different in their perceiving things differently, or you talked about the assignments slightly differently.

You can have those conversations which are quite valuable too. Just for example, I update the Presidential ads assignment every time there’s a new presidential campaign. We can compare how the 2020 ads stacked up to the 2016 ads, but multiple things are changing. We can talk about how much is a difference in the ads that were being run, how much is the differences in the types of students and the things that students are seeing. It’s a really useful conversation for talking about the evidence that John Geer is bringing in his argument relating it back to the course material.

[00:15:52] Bonni: All right. I want to go back, Rebecca, to something that you talked about just the value of students enjoying more what they’re doing which I suspect a correlation between enjoyment and feeling like I got to participate. You mentioned participating in this bigger thing. Then the second element that you talked about, feeling like professors cared about them more and cared about their learning.

Would love to hear a little bit more about your thoughts and you’re wrestling with the idea of automating some things so that it’s more doable so that we can reduce the workload that is automatable. Then which parts cannot, should not be automated, and just terms of your thinking and wrestling with the human element of feedback, and the parts of feedback that really we should be considering automating.

[00:16:47] Rebecca: Yes, that’s a great question. Something that I think that we should be wrestling with. This idea about connecting with students and building relationships, and helping students feel valued, and their professor cares about them. To help them enjoy the class is really something that I have cared a lot about and have studied and I wrote a book about in 2021, called Connecting In The Online Classroom. This is something that I have put a lot of thought and energy to.

I think we should use technology to our advantage as much as we can. I am in favor of automating but I think that what we really need to do is make sure that it’s authentic to us at every step of the way. That’s why I love what Matt has created here because if you know Matt you know that he loves data and that he is amazingly good at statistics. He created a way to connect with students that involves data and involves telling them statistically what’s going on with their contribution to this broader collaborative data project.

That’s so authentic to who he is as a person and as a professor. I automate communication with my students using mailmerge all the time, but I will write very sincere messages in my own voice that gives specific feedback depending on what their discussion grade is in the class. How they did on the last exam, how their score changed from exam one to exam two, telling them, “Hey, I’m really seeing a significant drop here between exam one and exam two, what happened there? Is there anything I can do to help you?”

Everyone who dropped seven points or more gets that message. It’s a very sincere message, and it’s in my voice. Yes, I automate it for everyone who had that particular drop. I’m using technology to my advantage, but I still really care about those students who have that drop. It’s going out to all of them. I get a really good response from them because they’re hearing my voice say to them that I care about them. I think we should use technology as much as we can because as faculty members, we have a lot on our plates.

[00:19:12] Bonni: That’s such a helpful distinction that you just made for me. I have felt guilty at times and Canvas that’s the learning management system we use. There’s a feature in the grade book that says message students who so I’ll try to be super explicit. You are receiving this email [laughs] because of this reason? Rebecca, I don’t think it matters any which way that I word it.

I don’t seem to be able to prevent the phenomenon from happening that they think I just emailed them. I have explicitly tried to not claim personalized authorship and yet there is. I think you just helped me make that distinction, the care when it can scale versus I suspect there are times when the care cannot scale and I’m just going to make a quick reference. I’m going to be careful how I say this because it’s too jarring of a change in our conversation.

I know also Matt has something to add here. There was recently a devastating national event in the United States that a university decided to write about it to the public using artificial intelligence. That probably was not the greatest choice in judgment to use. Artificial intelligence for what it’s best at. I’ve read a lot of subsequent articles about people talking about norms and that we haven’t really figured out as a society yet what our norms are around that. That was definitely an extreme example of a mismatch of where automation and care can come in. Matt, I know you have something to share about this topic as well.

[00:20:46] Matthew: I just wanted to follow up on one thing that Rebecca said. For me, the use of automation is so important because it’s systematic, and therefore, it ensures that I don’t miss individual students who need attention. Now, I can automate specific checks. Rebecca had mentioned looking for people who are seven points down or something like that. That’s something that we can be systematic about. Then, obviously, we have to be thoughtful about what other ways should we be looking for students who need additional help.

One thing that I was able to do because of automation that I wouldn’t have had the time to do is not just target students who maybe did worse than others, but also, automatically send students who did particularly well in a way that I just, in my limited amount of time I have for teaching, often that would get put aside. That’s something that I can rootnize using something like an R Script. Suddenly now I can help the students who are performing well also feel connected.

[00:21:54] Bonni: Talk more about, Matt, how feedback might ebb and flow in a particular class. Give me an example of maybe one or two assignments I might be working on in that class, and then what feedback I might expect to receive.

[00:22:07] Matthew: Yes. There’s two stages or two sets of feedback. The first is coming directly from these assignments. In my research methods class, it’s a quantitative class that focused a lot on statistics like regression analysis. In that case, what I need to ensure is that students are not just memorizing definitions, but they understand what’s going into the regression. I have them gather data from their own lives, go out and collect things that you see, and then I can provide them feedback and show them how their results compare to other students, and I can show them where they’re making mistakes.

That’s one version where it’s individualized, going to them and saying, “You use this particular example, and this was your bag of M&Ms, and this was the average or the proportion that were orange in your bag. This is how it compares to all the other students in the class.” I can teach them something like the Central limit theorem that way. Now, the other way, the second way that I can provide feedback, once I created those R Scripts, I realized I can do that for many other things, too. I can provide biweekly updates to every single student.

I’ve got 150 students, and I can say, “Here’s where you’re doing really well. Here’s where I’m noticing you’re struggling a little bit. Here are resources for people who are struggling on this particular thing.” I can send that through email, and they get this direct feedback that, again, I can’t do when I’m also teaching two other classes in nearly as systematic a way. Again, that can’t be the only form of feedback, but it’s a really important source of support for everybody.

[00:23:48] Bonni: Rebecca, anything that you wanted to share in terms of feedback? I’m thinking specifically about this series of projects and this work that you’re doing being offering something different to society than the ones who might be doing the for-profit textbooks, the add-ons, the labs, the simulations, and all of that. Something unique about this type of research and this type of feedback to students.

[00:24:14] Rebecca: Yes, I think this feedback is really valuable because it’s helping students see how they’re contributing to a larger project that everyone in their class is a part of. It’s really helping build a classroom community. They don’t feel they’re off in a textbook from a third-party provider getting automated messages. They feel they’re part of a classroom community, and everyone’s doing these same tasks, and they can see how they fit into that broader project.

I just love Matt’s point about also getting feedback to students who are doing well, because I think, so often we forget them. We’re busy doing triage to help the students who are struggling, and we forget that sometimes those students who are doing really well, or even the students who are on the cusp of an A or B, think a little bit of cheerleading, and that can help them be more successful or help them power through to the end.

[00:25:14] Bonni: One challenge that can sometimes come up for me when I’m attempting to experiment. First of all, let’s be clear. I think what you’re doing is so cool. I don’t even think I’ve remotely experimented with something like this, but when I’m being more experimental with the types of projects, and especially if they involve other students and wanting to have that experience that you’re talking about is timing of things. I’m wanting to be flexible with deadlines, and yet if you are overly flexible with– Okay, here’s my simple example. It’s months after my class started, and I’m teaching business ethics, and I had asked for them to contribute to a class playlist.

I hadn’t heard from all the students, so I still haven’t sent it to them. [laughs] This is the extent to which I’m trying to compare this magnificent thing you’re doing to my small teaching life over here. I’m thinking like, “That was not a great plan. Why did I not just send it out?” Then [laughs] the next couple of students came in, you could add to it. I don’t know any thoughts that you have around how you’re navigating deadlines in that– I don’t think we want to leave students behind. I think COVID has taught us so much, at least taught me so much about wanting to be flexible, but there is absolutely such a thing as too much flexibility. What are your thoughts around how we can navigate that?

[00:26:32] Matthew: I definitely agree that timeliness is important for these to be successful. If someone doesn’t have their data entered by the time that I’m aggregating it, either for the class discussion or for sharing the individualized reports, then they’re not going to make it into the report. The most important thing for me is giving students enough lead time so that they can get it done, and also, introducing it in a way so that they see how, not necessarily easy, but so that they recognize that it’s not too overwhelming just to get started.

Ideally, it’s built in a way that they can do a little bit each day, and show they can get it done in a relatively short amount of time without becoming overwhelmed because it’s a too larger project. The times when things have gone wrong is when it was the first time I’ve created the assignment, and maybe they only had two weeks before it was due, then there’s not enough room for mistakes either on my end in creating the assignment. Sometimes you don’t think of the different ways that people might enter to the data or on their end, deadline slipping by, et cetera.

[00:27:47] Bonni: Is there any advice that you have in terms of when inevitably someone just can’t? Is that any ways to make adjustments that you have found to be some way of not having people have to have such significant downsides to missing deadlines?

[00:28:03] Matthew: Yes. I think one big advantage of the individualized feedback is that it’s easy to update and send them a report whenever they submit their data. The other students won’t benefit from the data that they gather because it can’t make it into their reports. Likewise, the student who’s submitting late, it won’t be nearly as engaging during the class where we talk about everyone else’s experience. On the other hand, because things happen, it’s quite easy to just run the R Script again with the new data, and then they still get the individualized feedback that they would have missed if I was just presenting it to the class on that one day.

[00:28:47] Bonni: Rebecca, you were talking earlier about some other examples where you could see this, and I’m so fascinated by just how we can collectively expand each other’s imagination. That was really a fun part of the conversation. I’d love to have each of you share because we’re saying what could be done, but I’d love to have each of you share just a few more examples of what has been done. What are the specific assignments, specific times of collaborative research projects that are really tangible, that just have been fun for you to experiment with?

[00:29:17] Rebecca: Yes. Matt and I are both political scientists, so we definitely lean towards the politics angle. News articles are another one to have students look at news articles and have them look at the content in them or look at the tone of coverage or look at how partisan the coverage might be. Having students look at articles and what exactly is being covered over a period of a couple of weeks, you can start to see that change across different sources as well.

What sources are covering what stories? That can be something really interesting for students to engage with and lead to really great class discussions also. It also can lead to interesting insights, as you were talking about earlier, Bonni, about what is the right answer and what are students seeing through the lens of their own biases in coding those new stories as well.

[00:30:14] Bonni: Matt, I want to hear your answer to other projects and stuff, if something’s coming to mind around political science.

[00:30:21] Matthew: Just two more ideas. One is I teach a class about social influence in politics, and so the assignment I use would work well in something like a sociology class as well, where what I do is I have students enter data about their own social networks. Then in the class, I’m using a lot of jargon from network science concepts like centrality and modularity and community detection. What I can do is as I introduce a new concept, I can show them using their own networks. Again, it makes these really intangible things much more tangible, these little nodes, these are people that you personally know and so we can talk about these concepts in a more tractable way.

Then a second one related to this idea of information literacy is in my research methods class, I teach this idea of confounding variables, and so I have students find examples of journalism, reporting science and talk about the extent to which they think that the relationship that’s being described might be spurious and propose potential confounding variables. What you get then is this crowdsourced list of many, many, many, many different articles that students can then pluck through and find, “Oh, here’s the independent variable, here’s the dependent variable, here’s a potential confounding variable.”

It’s really creative and the more students you have, the more valuable it is for everybody else in the class, which is the opposite of so many things, where the more students you have, the harder it is to provide valuable feedback for everybody.

[00:31:56] Bonni: Oh, my gosh, both of these sound amazing. Linkedin used to have something like what you’re talking about, I think, what you’re talking about with the nodes and the network, they don’t have it anymore. It would be fascinating to me and it felt like a glimpse at a history of my career and the relationships and then how they overlap and went away and I was so sad and so now I’m fascinated by this. All right. We have to get ourselves disciplined here because I think I could talk to you for the next ten years and just be getting started. If we want to get started, how would we dip our toes into something like that? What would be your assignment to us if we want to experiment and learn more?

[00:32:34] Matthew: Well, so I would start simply by thinking about the kinds of assignments you give that aggregation is going to be beneficial so sharing with other students the collective what everybody has found. Then from there, you don’t need to learn a programming language like Python or R, Rebecca mentioned, a lot of this you can do with mail merge. If you have access to some license with like Microsoft, for example, you can start to provide these personalized reports that way instead.

Obviously, it still takes work just getting whatever data students enter, say, on the learning management system into Excel. If you use something like Canvas, you can download that and it’s relatively straightforward. Now if you can learn a programming language, it becomes much easier and so that would be a different conversation about how to get started with a programming language.

[00:33:29] Bonni: Rebecca, what’s your answer to how people might just start dabbling?

[00:33:33] Rebecca: I think Matt has some good suggestions, thinking carefully about your assignments, jumping into R because Matt’s done a bunch of hard work to make it super easy for you. If you want to use his freely available code, give R open Access article a read that might give you some good ideas. Then I would say you could do it simply with like a Google form and an add on to Google sheets and you could have students enter codes or enter data that way, and then you could send out information in a mail merge through Google sheets. It doesn’t have to be complicated. You can make it happen really easily.

[00:34:14] Bonni: Thank you both so much. This is our time in the show where we each get to share our recommendations. Since so many conversations on the podcast and so many at my university have been around artificial intelligence, I think sometimes we ended a session with a student panel today and someone ended, I haven’t watched it yet, but with a Saturday Night Live clip because I think we all needed to lighten it up a little bit. If you need to lighten it up a little bit With regard to AI, I was captivated by this artificial intelligence Instagram account, and it is called Captain Creep.

This individual, this is their bio. Welcome to Creep Mart. Open 24/7 Nothing is real. Nothing is for sale A.I. generated stuff and things. The images on the Captain creep Instagram are priceless. There are all these imaginatory, I can’t even use words today. There are all these toys that were created in this person’s imagination using the help of artificial intelligence. The one I’m looking at today is a very old-fashioned-looking television with a knob that looks like an old-fashioned microwave, and the toy is named shabby. Then inside of the old-fashioned-looking television set is what looks like, I don’t know, a green creature with very, very large ears and bright wide-eyed.

Then another one that I’m seeing right now is called Crawl and it looks like a dinosaur. I can’t even read the bottom of the thing progressive glasses on computer monitors sometimes we get it, sometimes we don’t. Then there’s blurred out in the background these other creatures. It is a hoot. They look like toys from maybe the 1950s and again, they’re all fictional you can’t buy these. Nothing is for sale.

All artificial intelligence inspired are created suime spelled S-U-I-M-E soupy and it looks like slime. Just this green series of slime creatures inside of a jar. Oh, it’s so much fun. If you need a little bit light hardness around your artificial intelligence AI created art and you want to laugh a little and just marvel at the imagination here. That’s my recommendation and I am going to pass it over to Matt for his recommendations.

[00:36:44] Matthew: I was thinking in a very similar vein, but much older than that. The thing I wanted to recommend was the album that I was listening to when I was first creating these assignments back in March 2020. This is an album called The Expanding Universe by Laurie Spiegel. The reason this is similar to your recommendation Bonni, is this is algorithmically generated music. It’s not AI, this is actually– Laurie Spiegel created this music back in the I think it was like early 1970s when she was working at Bell Labs. These giant, giant computers, she was creating these algorithms to generate. It’s essentially ambient music so there’s no words.

It’s something that I find really, really useful for working. A lot of ambient music is minor key negative this is super optimistic and exciting music. It’s something that I find really useful to put on at the start of the day to shift into work mode. I strongly recommend it for anyone who likes weird ambient music.

[00:37:54] Bonni: It sounds marvelous. Thank you so much. Rebecca, what would you like to recommend today?

[00:38:00] Rebecca: My recommendation is a book, but it’s not directly related to teaching, but it’s a book that has made me a much better person. I feel like it is related to teaching. It’s a book by Cheryl Strayed called Tiny Beautiful Things, and I have gifted it so often I feel like I should buy it in bulk. There’s a chapter in there that is called The Future Has an Ancient Heart, and it is a commencement address or a letter to students who are going to graduate. I think that’s a that’s a good one to start with if you’re thinking about reading that book. It’s a book that is close to my heart that I really love so I would recommend it for sure.

[00:38:44] Bonni: Matt and Rebecca, thank you so much for this generous and invigorating conversation. I’m so glad to be connected with you and thanks for coming on the show.

[00:38:52] Matthew: Thanks so much for having us.

[00:38:53] Rebecca: Thank you so much.

[music]

[00:38:56] Bonni: Rebecca Glazier and Matthew Pietryka, thank you once again for coming on today’s episode of Teaching in Higher Ed. Today’s episode was produced by me, Bonni Stachowiak. It was edited by the ever talented Andrew Kroeger. Podcast Production support was provided by the amazing Sierra Smith. I just want to thank you so much for listening to the episode, for being a part of the Teaching in Higher Ed community. If you’ve yet to sign up for the weekly email, head over to teachinginhighered.com/subscribe and you’ll get all kinds of goodness that doesn’t show up in the regular episode Show notes. Thanks for listening and I’ll see you next time on Teaching in Higher Ed.

[music]

[00:39:55] [END OF AUDIO]

The transcript of this episode has been made possible through a financial contribution by the Association of College and University Educators (ACUE). ACUE is on a mission to ensure student success through quality instruction. In partnership with institutions of higher education nationwide, ACUE supports and credentials faculty members in the use of evidence-based teaching practices that drive student engagement, retention, and learning.

Teaching in Higher Ed transcripts are created using a combination of an automated transcription service and human beings. This text likely will not represent the precise, word-for-word conversation that was had. The accuracy of the transcripts will vary. The authoritative record of the Teaching in Higher Ed podcasts is contained in the audio file.