Author: Adam Hawkins

By Kate Cammack, CfT Co-Director

TLDR: Students often explain themselves through learning styles – “I’m an auditory processor,” “I’m a visual learner,” “I don’t really learn by reading.” Here’s what 15+ years of studies in psychology and education actually found about “learning styles”, and how to help students see a more nuanced relationship between their learning, their preferences, and even their identity.

“I’m a visual learner”

As a neuroscientist, I draw a lot of diagrams in my classes. They get ugly and messy, and if I post one here it’ll make your eyes bleed. Despite this, every semester, at least one of my students tells me something along the lines of, “I really like the diagrams, because I’m such a visual learner.”

In my own learning experience, I found that drawing diagrams helps solidify core concepts and visualize dynamic processes and interactions in ways that reading the material did not. In my teaching, asking students to generate drawings helps them to remain actively engaged in their learning. I’ve even built assessments where students can choose to explain concepts via diagrams on video – here’s a screenshot of one of my NEUR101 students’ TikTok explanations:

I might posit that visual-based approaches to learning can be particularly helpful for explaining certain concepts. But I balk at “visual learner,” and so does the data.

A story that we tell ourselves

I imagine that most of us have heard students reference their “learning style” at some point. and it got me thinking about the origins of this myth, and why it’s so persistent.

The word “myth” comes from the Greek mythos, meaning “story” – not necessarily a false one, just a shared narrative that explains something about ourselves to ourselves. 

The “learning styles theory” states that our learning styles are innate, unchanging, and wired into our brains – they are a part of who we are, as individuals. VARK is one model, but there are over 70 frameworks out there (an early indicator that the construct might be suspect). It is a myth that is widely told and deeply embedded in educator training and development.

(image source)

Everyone loves a good story – they can be memorable, easy, and simplify things. But telling ourselves stories that aren’t accurate, particularly about our capacity to learn, can pose problems.

We do have learning preferences

What is true: our students differ, and their preferences for how information is presented are real. 

It is valuable to use different approaches to teaching and assessment, in order to engage different students’ preferences and strengths – indeed, this is a central premise of UDL. 

It is also true that we might not know whether something “works” well for our learning until we engage with it in multiple ways and in different settings. I have preferences for how I work with material, and much of that was forged through trial/error and skill-building, and engagement.

Some of these things might be “learning preferences” but others might not; as one author quipped, “is it really a learning style to like to work in pairs, in the afternoon, when it is warm, and while snacking?”

Learning doesn’t improve when instruction is matched to learners’ preferences

Pashler, McDaniel, Rohrer & Bjork (2008) ran a definitive review, where they set a genuinely fair bar for what would count as compelling evidence: a crossover interaction, where they would sort learners by style, randomly assign them to different instructional conditions, and see whether Style-A learners do better under the Style-A method while Style-B learners do better under the Style-B method. Figure 1 diagrams “acceptable” vs “unacceptable” evidence, in true psychologist-nerd fashion.

They found that (a) very few studies met that bar and (b) the few studies that did found scant compelling evidence that learning style impacts academic performance.

As one example, Husmann & O’Loughlin (2019) found that anatomy students’ self-reported learning style did not predict the study strategies that they students used or how well they performed on assessments.

This TED Talk provides a nice overview of some of these data.

What might actually be going on

First, students aren’t always the best judge of their learning. In one study, students reported that they thought that they learned less in active- vs passive-learning STEM classrooms, but their quiz scores were actually higher in the active-learning classrooms (see below). Preference can easily get mistaken for ability.

Second, we tend to pay more attention to information that matches what we already believe. A student who “gets it” from my diagrams now has “proof” that they are a visual learner (even though we didn’t test whether other ways of understanding might have worked just as well). This is called confirmation bias, and we all have it.

Third, it can be a deferral of responsibility. Maybe they didn’t set aside enough time to study, or weren’t using study techniques that led to deeper understanding.

What’s at stake

There are deeper costs. When students pigeonhole themselves as a certain type of learner, they’re not just misdiagnosing how they learn, they are absorbing a fixed mindset about learning itself. A “learner identity” can turn a temporary preference into a fixed label, which runs counter to a growth mindset. 

If a student walks away believing they can’t learn well from a certain format because it doesn’t match their style, that’s not a neutral misunderstanding — it’s an excuse to stop trying. In other words, when the learning style myth is reinforced, it suggests that struggles are signs of a mismatch that is outside their control, not a normal and expected part of learning.

In these ways, fixed academic identities (e.g., “bad test-taker”) can have far-reaching implications for students’ persistence, self-efficacy, and even belonging.

Three things that we can do

We can help students think about their learning. Students don’t often know how to make sense of their learning experiences. Consider talking explicitly with them about what makes courses hard (or easy) or how to make sense of the feedback that you give them – it will be time well spent. 

We can also build metacognitive practices into our classes. It can be valuable to provide students dedicated time to articulate aspects of their learning, from exams to groupwork.

Finally, when we hear students cite their “learning style,” we can gently reframe their thinking to allow more nuance and welcome space to their academic journey.

Your “enjoys-a-visual-but-can-learn-other-ways-too” colleague,

Kate

By Kate Cammack, CfT Co-Director

TLDR: A new – and particularly valuable – report on AI from MIT provides concrete guiding principles and a compelling call to action.

We hear a lot about AI as a “crisis in higher education.” The word “crisis” comes from the ancient Greek root krinein, which means “to separate, decide, or judge”. A few years ago, I heard a “crisis” helpfully reframed as akin to the process of sifting sand at the beach – it’s a chance to let all the stuff that you don’t care about fall away, so that you’re left with only the treasures – the most important and valuable things.

For many of us, the AI crisis has invited deep discernment – what are we doing, why are we doing it, and how might we re-center and uplift the values and goals that we care most about?

This past Friday, cognitive psychologist (and MIT alumna) Karen Yu shared a new report on AI from MIT with our department, and it is one of the more lucid, organized, and well-supported pieces on AI in higher education that I’ve read thus far.

It presents guiding principles, including:

• Be humble. AI has evolved so rapidly, and changed our work so quickly and dramatically, that we need to acknowledge what we don’t know and that we must continue to navigate this landscape, as an inevitable aspect of our job as educators. As Brene Brown says, “we’re not looking to be right, we’re looking to get it right” – and, for me, getting it right requires adjustments, course corrections, and (ultimately) a growth mindset.
  
• Be bold. This is not a time for “patches and duct-tape” but for big thinking and reinvention. That takes cognitive-emotional space and some gas in the tank, so this principle goes hand-in-hand with sustainable teaching and self-care.
  
• Put humanity front and center. Teaching and learning are relational. We are here for our students, and they are here for us. A crucial goal of higher ed is to “nurture and protect our shared humanity.” As I told my students, if they’re submitting AI work and I’m using AI to grade, we have some real existential problems to grapple with (!)
  
• Lean into learning. As educators, we are here to show our students how to learn, not just convey content. The report calls us to clarify the process of learning (engaging in productive struggle; engaging in the “hard fun” of thinking) and the product of learning as themselves – their intellectual growth, habits of mind, and ways of being that will enable them to lead purposeful lives. This also might invite us to reconsider the products of learning that students often peg too much importance to – their grades.
  
• Teach with intentionality. We all tweak and adjust assignments and course materials from semester to semester. However, it can be rare for us to pull back and really ask ourselves what we’re trying to accomplish, and if we’re accomplishing it in the most meaningful ways. If we really consider what our learning objectives are and (re)design from there, we might find that a more expansive and exciting set of possibilities opens up.

There are many more valuable parts to the report, including recommendations for our teaching. Please consider giving it a read.

Looking for outlets to navigate the AI crisis together?

• Join our community of practice (CoP), a come-as-you-are way to think through teaching possibilities together.
• Get a sense of how your students’ learning via our FCR program
• Identify and build your teaching network, by inviting a colleague to coffee or for a walk.

In solidarity,

Kate

Kate Cammack, CfT Co-Director

“One of the best ways to be bad at something is to tell yourself that you are bad at it.”

The idea that some people are simple “bad test-takers” is ubiquitous in popular academic lexicon and in the media (Reddit threads provide one of many cringy examples).

But it’s tricky to define what a “bad test-taker” actually is.

Presumably, it’s someone that understands the academic material and has effective study skills, but who consistently does not “show what they know” on tests. But why might this be?

Holmes (2021) suspected that students who attributed their test performance to a trait-like factor somehow inherent to themselves, rather than their grasp of the material or effectiveness of their study habits, would be more likely to consider themselves a bad test-taker. In this way, he wrote, a bad test-taker identity is a form of low self-efficacy, or the belief that you have the ability to succeed; indeed, “self-efficacy for a task promotes avoidance, low perseverance, and negative emotionality in the face of that task (Bandura, 1997)”.  

This explanation related to explanatory style, or consistent ways that people explain negative events in their lives (Peterson & Barrett 1987). Briefly, Peterson and Barrett (1987) found that people who attribute negative events to internal factors that are unlikely to change over time (i.e., a personal “trait” or characteristic) are more passive and less effective problem solvers than people who attribute negative events to external (non-personal) factors. Further, college freshman who make such internal attributions for their academic struggles had lower GPAs (despite similar SAT scores), greater symptoms of depression (and this study was in 1987!), and were less likely to seek assistance from their academic advisor. Follow-up studies found that students making internal attributions believed that failures were out of their control and were more likely to give up (McKean 1994) – indicators of a fixed mindset.

Holmes’s goals were to assess the prevalence of bad test-taker identity among college students and identify factors associated with a bad test-taker identity.

What he did:

• Recruited 311 undergrad participants at a liberal arts college (83% women), who were relatively evenly distributed across freshman to senior years
• Gave them a series of established scales that measured:

1. test anxiety, or students’ various anxious reactions to tests & testing situations (Benson & El-Zahhar 1994)
2. academic self-efficacy, or students’ confidence in their academic abilities (Chemers et al., 2001)
3. academic entitlement, or the “expectation that one should receive certain academic outcomes (e.g., high grades)…often independent of performance” (Kopp et al., 2011)
4. need for cognition, or the “tendency to ‘engage in and enjoy effortful cognitive endeavors’ (Cacioppo et al., 1984)”
5. study strategies and motives, or the use of deeper or more superficial strategies and motives used to study (Biggs et al., 2001)
6. academic beliefs & attitudes, or responses to questions such as “Some people are bad test-takers even when they know course material well”

Findings regarding the prevalence of bad test-taker identity:

• Over 90% students agreed that some people are bad test-takers even when they knew the course material well; over half (55%) agreed “strongly” with this.
• Over half (56%) of the students reported that they see themselves as bad test-takers.
• Over one-third (35%) reported that someone else had told them that they are a bad test-taker. 
• Over two-thirds (65%) reported that they are “smart enough that they should not struggle in their classes. 
• Most students (70%) agreed that standardized tests primarily assessed test-taking ability, rather than content knowledge.

He also found that students who saw themselves as bad test-takers:

• had lower confidence in their academic abilities,
• expended less effort on cognitive activities,
• possessed more surface-level learning motives and utilized more superficial study strategies,
• felt more entitled to positive academic outcomes (e.g., grades) regardless of performance, and
• showed higher levels of test anxiety

compared to students who don’t see themselves as bad test-takers.

How this might inform your teaching:

• Try a simple intervention. In one study, asking students to write briefly about a positive experience prior to a quiz reduce test anxiety, improve quiz performance, and consider the quiz as a challenge rather than a threat (Nelson & Knight, 2010)
• Help students think about their learning. Students don’t often know how to make sense of their learning experiences. Consider talking explicitly with them about what makes courses hard (or easy) or how to make sense of the feedback that you give them – it will be time well spent. 
• Explore an expanded set of assessment approaches. There are many ways to assess students’ learning without relying exclusively on traditional quizzes or exams (e.g., here). For instance, are there alternate ways to assess students’ learning, or to revisit your balance of low- versus high-stakes quizzes/exams in your course?
• Take a colleague to coffee. A casual chat with another educator about things we’re trying, challenged by, and curious about? Sounds amazing! In fact, some deeply meaningful ideas and moments can arise when we co-create knowledge and engage in mutual care. Take a minute to email someone in another department, building, discipline and invite them to connect over something we all love to do – teach.

We hope to expand our conversations on assessment practices over the coming year – including different ways to grade (e.g., the unessay), whether grading is “broken,” how to get students to use feedback in helpful ways, how we might rethink grading in the context of GenAI, and how to shift our assessment practices to be kinder and even joyful (!) Stay tuned for more.

Kate Cammack, CfT Co-Director

The Faculty Fellows for Mentored Research and Creative Work recently hosted an informative session on Course-Embedded Research Experiences (CUREs). Three faculty from different divisions/disciplines – Kati Curts (Religious Studies), Jennifer Matthews (Theater), and Katie McGhee (Biology) – led an engaging conversation on CUREs; you can find their slide deck here.

A few questions that were considered at their CfT event:

What is undergraduate “research”? What might fall under that umbrella?

• It can be defined broad as a “mentored investigation or creative inquiry conducted by undergraduates that seeks to make a scholarly or artistic contribution to knowledge” (Council on Undergraduate Research)
• How do we define “knowledge”? Must it be unique? Is the process of research sufficient?

What is a CURE? How does it differ from related, high-impact practices?

• A CURE “integrates undergraduate teaching and research through authentic research opportunities in which students contribute to knowledge that is new to the discipline” (CUR Characteristics of Excellence in Undergraduate Research 2.0)

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What are the benefits? Challenges?

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What can I try, if I’m not quite ready to commit?

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What resources do you recommend, so that I can learn more?

• Your colleagues!
  • If you’d like help identifying examples of CUREs within your subdiscipline, please email any of the Faculty Fellows or me (CfT co-director)
  • If you’re potentially interested in exploring a faculty learning community (FLC) focused on CUREs, the CfT would be happy to help coordinate – email Kate at kmcammac@sewanee.edu
• Council on Undergraduate Research
  • here are a few of their resources & articles on CURE
• General or disciplinary search engine, using CURE as a search term
  • As a general example, I used Google (not Scholar) to find a helpful, orienting article specific to my discipline (neuroscience)
  • My favorite disciplinary search engine turned up 100s of results (and ideas!)

The Faculty Fellows program is coordinated by the Office of Undergraduate Research & Scholarship (co-directed by Elise Kikis and Ross Sowell) and supported by a McCrickard Faculty Development Grant.

Kate Cammack, CfT Co-Director

Language educators have been grappling with AI-related issues for far longer than educators in most other disciplines. Google Translate was launched nearly 20 years ago, in 2006; this is around the year that many of our current college students were born, so basic language-translation tools have been available to them for most (if not all) of their lives.

Recently, the CfT hosted facilitated small-group discussions between language faculty that focused primarily on pedagogical compatibility (e.g., alignment of GenAI with evidence-based language teaching practices) and preparation of students. The discussion focused on (a) how/when they were discussing language learning in the context of AI with their students, and (b) whether/how/when they were incorporating AI tools into students’ learning experiences. While I’ll do my best (as a non-language-faculty) to summarize some key points here, I encourage you to connect directly with our language colleagues for more (!)

Motivating students to put in time & effort

Challenge #1: Learning a language requires some rote memorization, which requires dedicated time to practice. As a basic example, students must be familiar with vocabulary in order to form sentences and express thoughts; in other words, some lower-level Bloom’s work (e.g., what is the word for “apple”) is required in order to do more high-level activities (e.g., ask a child if they would like an apple). Faculty reported that their general sense was that students perceived “language learning” to be important and valuable, but it was not always clear that students’ working definitions of “language learning” included this lower-level Bloom’s work. As it is very easy to have AI assist much of this lower-level Blooms work, it is additionally challenging to motivate students to invest in this type of work.

Ideas and/or “things they’ve tried”:

• Dedicate a few minutes of class time to define and think critically about the components involved in “language learning.”
• Remind students that “language learning” requires putting in the time to practice the basics, as you won’t be able to build fluency without them
• Help students envision what their language learning goal is (e.g., having a basic conversation with a neighbor) and provide opportunities for them to revisit their “whys,” which may help sustain their interest and persistence

Perfectionism & concerns over embarrassment

A challenge: We all make many mistakes when we’re learning a language, and mistakes can make people feel embarrassed or self-conscious. Students might perceive that AI will help improve their writing so that it contains fewer mistakes; it may also allow them to practice a language in private, without fear of others’ reactions. Some faculty wondered how/whether AI tools might help students practice certain aspects of language, without inadvertently shortchanging or shortcutting their learning experience.

Ideas and/or “things they’ve tried”:

• Explicitly reassure students that they can have meaningful interactions with incomplete sentences and without perfect grammar.
• Underscore the unique value and importance of Authentic Interaction (a different AI!) and cross-cultural understanding as critical components of language learning & communication; these interpersonal skills and face-to-face interactions may in fact help ameliorate some of the losses that students feel in the face of AI
• Be clear about what AI tools (from translators to chatbots to tutorbots) do well and don’t do well (yet), and provide examples specific to your course so that students know whether/when it’s OK to use those tools. Knowing when & why college students use AI can also be helpful in framing your conversations effectively.

To reduce overreliance on AI and/or empower students to begin work without AI assistance:

• Short-write at the beginning of class, to ensure students’ own words/thinking
• Start larger essays or projects together in class
  • Introduces the project as a collaborative learning activity
  • Reduces “blank document phobia” by allowing students to leave class with their project/paper already started
• Break up medium-sized projects into smaller, more manageable chunks (as students often turn to AI in the face of deadline crunches and/or large/daunting assignments)
• All Sewanee faculty have pro versions of Microsoft CoPilot, which is worth playing around with. If you’re interested in other platforms (e.g., ChatGPT), please contact Lauryl or Kate so that we can “check out” a paid platform/version to you.

Additional resources*:

• Generative AI @ Harvard: Resources for Faculty
• Lee et al (2025) Generative AI (GenAI) in the language classroom: A systematic review
• Du & Daniel (2024) Transforming language education: A systematic review of AI-powered chatbots for English as a foreign language speaking practice
• Here’s an online (mostly asynchronous) workshop hosted by Michigan State and an “advanced” mini-course hosted by UChicago

 * identified by your soundly non-language CfT co-director (!) you likely have others, please add to this list!

Kate Cammack, CfT Co-Director

We’ve made it to fall break! Hopefully you have given yourself permission to celebrate, relax, and do some things that you enjoy – or perhaps just take a few deep breaths.

I am busily employing all of my standard procrastination techniques to avoid finishing my midterm grades today. Anyone else?

A few teaching opportunities that mid-semester brings:

• Feedback for you. If you haven’t already done so, this is a terrific time to check in with your students about how things are going. I typically use an anonymous version of start-stop-continue — just make a quick, anonymous Google Form (here’s the one I used last week), generate a QR code to the form, and have your students snap the code and complete the questions in your last 5min of your next class. (FWIW response rates are typically much better if you have them do this in class, rather than asking them to do it on their own time). Short on prep time? Hand out index cards for them to write a quick minute paper on what’s helping (or hindering) their learning in your course.
• A metacognitive opportunity for them. You might ask students to reflect critically and constructively on their own learning experience thus far. How are they engaging in your class? How do they think they are doing, with respect to any guidelines set earlier in the semester (e.g., discussion norms, class community agreement)? What worked (and didn’t) in their preparation for your midterm exam (e.g., exam wrapper), and can they articulate what they might do differently next time (see this simple example from a chemistry course)?
• Hit the reset button. Students’ motivation and intentions sometimes need a bit of a reset around midterm. For instance:
  • if they seem to not be as prepared for class as you’d like (e.g., not doing all of the reading), you might ask them to complete these four quick questions on their reading prior to coming to class
  • if you’re growing concerned that students are getting more lax about AI use, you might send them a gentle nudge reminding them what your course policy is and why it is important to their learning. 
  • if some aspect of your course isn’t working as you’d hoped, you might adjust it – have a conversation with your students, explain what you’d like to try and why you think it’ll help, and get their feedback.

Look what I just did in writing this – productive procrastination at its best (!)

Kate Cammack, CfT Co-Director

As an educator, I have:

[ ] assumed students know how to work in groups, based on past courses/experiences

[ ] assumed that students are adults who can figure out how to work together

[ ] heard students say that they hate groupwork and/or prefer to work independently

[ ] had students complain about group conflicts/problems to me

[ ] found it challenging to grade/provide feedback on groupwork

If I could put 100 check marks in each of these boxes, I would.

As educators, we know that teamwork is an important skill for their college experience, career readiness (e.g., this, this), and post-college lives. We also know that groupwork can go sideways quickly. With experience, we find new ways to structure groupwork to avoid pitfalls and minimize conflicts – what can be thought of as teamwork administration.

But how many of us actually dedicate class time to helping students build the skills needed for effective teamwork – what can be thought of as teamwork pedagogy?

Goldsmith & colleagues (2024) remind us that asking students to work in teams isn’t the same as teaching the knowledge, skills and attitudes (KSAs) necessary for teamwork. But, because few of us received training in how to teach these skills, we often don’t do it (myself included).

Key take-aways:

• teamwork, groupwork & taskwork are defined differently, and the differences will shape your goals & expectations and your students’ experience (pg 2)
  
• teamwork requires different competencies, including an understanding of how individual identities might contribute to teamwork processes (pg 2-3)
  
• teamwork pedagogy can take multiple forms, including didactic instruction, active learning techniques (e.g., exercises, case studies, role-play), and reviews/check-ins incorporated into the teamwork itself; a meta-analysis found that teamwork is improved by active learning & reviews but not didactic instruction (pg 4)
  
• while different disciplines might require distinct KSAs, they are largely generalizable (pg 6)
  
• while teamwork administration can promote equity & belonging, teamwork pedagogy can help students to cultivate skills for equitable collaboration and positive interdependence both inside and outside of the classroom (pg 7)

How this might inform your teaching:

The authors suggest four evidence-based, pedagogical approaches, including (pg 4-5):

• Team charters – these “describe the behavioral norms that teams wish to establish at the outset of a project and can have bearing on knowledge (e.g., the need to manage meetings), skills (e.g., effective communication), and attitudes (e.g., importance of working together).” Here are two resources on group contracts & group agreements.
  
  • Dr. Lydia Reinig does a lot of work on our campus related to community standards and group dynamics — for instance, students might (a) think about the best (or worst) groups that they’ve worked in, (b) articulate what made those groups so effective/satisfying, and (c) what group expectations or goals could they establish to help their group operate in those ways. She is a terrific resource and we encourage you to reach out to her with questions, suggestions & resources.
    
• Case studies – these “provide short written descriptions of hypothetical, but likely, scenarios that could occur during teamwork and use the scenario as the basis for group discussions that identify the problems and consider potential solutions.” Here’s a video case study that Kristen Cecala used in her biology course.
  
• Role-play – these activities “provide the opportunity for students to practice and receive feedback on how they would respond to hypothetical, but likely, scenarios that could occur during teamwork.”
  
• Self- or team-reflection exercises – these “offer the opportunity for students to practice metacognition and grow in their attitudes towards teamwork.”

We know how complex groupwork can be, and plan to hold future sessions where we can discuss both teamwork administration and pedagogy, as both are important & valuable. Stay tuned for more CfT programming around these issues.

For more, please check out the full paper:

• Goldsmith et al (2024) Overcoming the Barriers to Teaching Teamwork to Undergraduates in STEM. CBE—Life Sciences Education 23:es2

Interested in contributing to SoTL Corner with a summary of a SoTL article? Please contact Kate at kmcammac@sewanee.edu

Kate Cammack, CfT Co-Director

Have you ever experienced any of the following, when you read through your course evaluations?

Me too. #solidarity

First, it’s worth naming that course evaluations are an imperfect measure of something deeply valuable and important to us – our teaching. There are many scholarly articles on bias in student evaluations of teaching; here are a few and a brief 2023 Chronicle opinion piece. In fact, I’ve had past students play with this tool that shows gender bias across 14 million RateMyProfessors posts. (At least they’ve gotten rid of the chili pepper)

However, student evaluations of teaching can offer some value when we think critically, ask thoughtful questions, filter appropriately, and consider them to be one part of our broader teaching portfolio and story.

In a 2019 Chronicle article, the author suggests helpful contextualization and reframing of comments such as:

• “This class was too hard (or easy)”
• “The grading was unfair”
• “The assignments were confusing or pointless”
• “The professor spent too much time (not enough time) on X”
• “The professor/course was disorganized”
• “This class was boring”
• [general logistics category, including time of class, speed of grading, etc]

It’s worth a read.

At our CfT session on course evaluations in early September, we also spent time:

• reading between the lines and looking for additional clues to help get at the student’s core points, as students often have difficulty articulating aspects of their learning experience
• remembering the role that context plays in how students think & feel about our course, with respect to the course itself (e.g., a non-preferred GenEd course versus an upper-level seminar in their major) and also what students might be experiencing (e.g., election-related stress)
• considering how to approach student comments that included personal details/ad hominem attacks that did not relate to our teaching
• looking for themes & threads across our course evaluations – both repeated offerings of the same course, and different courses that share certain features – that might point to ways that we’ve adjusted to our students’ feedback and/or evolved in our teaching practices

Remember that there are additional ways to get feedback about your teaching and about students’ learning & experiences in your classroom, that you can implement at any time. You might take 5min to try the “start-stop-continue” method, or use an anonymous minute paper to gather students’ response to an aspect of your course. Or, next semester, sign up for a CfT-sponsored Facilitated Course Reflection!

Solidarity in teaching,

Kate

Two quick reads:

Chaplin (2025) Teaching evaluations shook my confidence  – until I learned to filter critiques. Science

Whitaker (2019) How to make the best of bad course evaluations. The Chronicle of Higher Education

By Stephen Ray Miller, Professor & Chair, Department of Music

Six students and I got in a University van at 5 a.m., Saturday morning (9/13), and drove to Atlanta. This inconveniently early travel indicates our determination to participate in 120th session of the United Convention of Sacred Harp singers. We are beyond grateful that the Center for Teaching made this possible for us.

Although an annual event, the United Convention this year was unique in that it doubled as the “launch” for the new edition (2025) of the Sacred Harp. This song book was first published in 1844 and last (prior to this year) in 1991. Excitement among Sacred Harp singers worldwide ran high for the new edition, and the seven of us joined about 700 other singers at the Atlanta site (The Foundry at Puritan Mill, near downtown), including visitors from as far away as Australia and Germany.

Those of us from Sewanee are all involved to some extent or another in MUSC254, a class in the Music Department, and the Atlanta experience provided a once-in-a-lifetime opportunity to witness some of our composer heroes in action and to recognize the worldwide impact of this southern style of music making.

The photos below depict:

a) Judy Hauff, one of the first non-southern singer/composers in the Sacred Harp idiom (she’s from Chicago), leading a song of her own composition

 b) two of the Sewanee students showing off their new copies of the tunebook and fashionable masks,

c) another Sewanee student less concerned about aerosols, displaying both his new tunebook and his “singer” ribbon, and

d) most of the Sewanee contingent enjoying the “dinner on the grounds” that the organizers of the event had provided.

By Leslie Todd, Assistant Professor of Art History

Many of the discussions on AI in education that I hear across campus have focused on professors’ anxieties around cheating and how to build a course policy. This is a complex and necessary discussion, but not one I’m interested in here. Instead, I would like to shift our focus for a moment to how we can use AI to create engaging and effective materials and activities on our course content while communicating to our students the limitations of AI. 

In my Global Renaissance course, during our week 6 module on materials and techniques we address Medici porcelain, Benin bronzes, and Nahua featherworks. Because I teach using a flipped model, the students come to class having completed the readings (usually Smarthistory articles for the first day of class and a scholarly article for the second day) so they are already familiar with the material when they enter the classroom. (I know what you’re thinking. Yes, they really do actually complete the readings. How I do that might come in a different blog post.) 

Last year, I experimented with what generative AI might offer students as I assessed their understanding of the different objects. In preparation for the week’s activity, I used the free version of ChatGPT. I prompted it to generate images of “Medici Porcelain,” a “Benin Bronze,” and a “Nahua featherwork”. This version only allowed me to enter three prompts before cutting me off so I had to wait until the next day to enter more prompts before arriving at a set of images that were worth working with. As I expected (and desired), the images were inaccurate to varying degrees. I asked students to examine the inaccurate images using the art history skills of stylistic and iconographic analysis they were building in the course. 

Students were able to assess that the AI Medici porcelain example did not have an accurate shape and displayed an improper use of gold among other details. This then gave us the opportunity to address what we should expect to see and how those styles developed and related to Renaissance forms and values, or potentially challenged existing forms and values of the period to expand our understanding of the Renaissance in a global context.

While the activity is about digging into how students recognize the objects stylistically and iconographically to give me the opportunity to assess what knowledge they may be lacking as it pertains to the week’s theme and individual objects, it also implicitly pushes students to consider the limitations of AI. A consistent warning about AI is its potential inaccuracies. At the faculty gathering in August, some panelists noted that employers are beginning to want to see that future employees have skills connected to AI. But a key part of those skills is discernment. As experts in our fields, we must know what to expect to come out of an AI generator. This activity demonstrates to students what an expert is and does in my field. Art historians must strengthen and maintain their visual literacy to know what to expect to see and how to confront and ask questions of the unexpected. I therefore also frame this exercise as an opportunity for them to recognize the growing strength of their own visual literacy in the face of a world in which, for better or worse, “fake” images can be generated with a few clicks on the keyboard.

Note from the CfT:

We’re interested in how our faculty are thinking about and/or using AI tools to support our students’ learning.

As these tools continue to evolve, so will our thinking about them. It’s valuable for us all — as a community of educators — to share our evolving ideas, thinking, questions, and approaches, so that we can learn from and support each other as we navigate this new landscape. No question or approach is too basic — there’s value in simply exploring, and in trying things out to see what works.

You can give us a sense of what you’re thinking about, reading or trying, at any time and as your thinking/work evolves — this form is set to continuously accept responses, so please help us build this new community of practice!