Category: Chad Raymond

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Meta-Cogitating Motivation, Part 2

More about making students aware of whether they are motivated to learn.

You never told me that I would have to read.

Every assignment that I’ve created on Perusall now includes these two questions:

  1. What most interested you in Reading X?
  2. What most confused you in Reading X?

I’m hoping that this will help students who consistently answer “nothing” to (1) and “everything” to (2) will help them realize that they’re in the course for the wrong reasons.

I’m also devoting eight class sessions throughout the term to wicked problem exercises, but I’m going to hold students accountable for participating. For each exercise, every student will get a worksheet consisting of the following:

  1. What is my solution to the problem?
  2. What are the main points of the other team’s presentation?
  3. What is one strength and one weakness of the presentation?
  4. What is a question for the presenter?

I’ll collect these worksheets and mark them as satisfactory (full marks) or not (zero). Each worksheet is worth only 2 percent of the course grade, but students will need to pay attention to what’s happening around them to earn credit, which requires a minimal amount of cognitive effort. It should be plain to students that a worksheet grade of zero indicates that they weren’t willing to exert this effort.

Finally, I’m instituting a device-free classroom policy. Laptops, tablets, and phones will need to be out of sight and out of reach when students are in the room.* If students prefer staring at the wall to engaging with what’s happening around them, that’s their choice. They will see the effects of that choice in their grades.

I’ll be at the UACES conference in Prague. Dr. Susherwood, international man of mystery, will be leading a panel discussion — “I’m Sorry, Dave, I Can’t Do That”: How To Address The Use Of AI In The Classroom — on the morning of 9 September. Hope to see you there.

*Yes, I will make exceptions for students with documented disability accommodations, but I expect this to be a rare occurrence.

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Meta-Cogitating Motivation, Part 1

As the readers of this blog know, but many of our students don’t, learning requires effort. People who want to learn how to play the guitar, bake croissants, or speak Tibetan are willing to expend considerable effort to learn these skills. Those who don’t aren’t.

Teaching in the age of AI?

At this stage of my career, I expect students to have the minimal level of motivation needed to learn something about the subject of the course I’m teaching. If they prefer outsourcing all cognitive effort to AI because they have no curiosity about, say, how the election of populist authoritarians has led to higher energy prices, they should go somewhere else. My enthusiasm in the classroom won’t by itself magically convert them to my religion.

For my autumn term courses, I will be introducing some exercises that should make the connection between motivation, effort, and learning more apparent to students. One such exercise is a graded* self-evaluation with the following questions:

  1. On a 1-5 scale, with 1 being lowest and 5 being highest, how much of a priority is this course compared to my other courses, work, personal life, etc.?
  2. Given my answer to Question 1, how much effort did I exert last week to learn in this course?
  3. What have I learned in this course?
  4. What specific actions did I take last week to learn more effectively in this course?
  5. How does my answer to Question 4 reflect advice from the “How to Maximize Learning” page in the course’s “Start Here” Canvas module?
  6. How have I connected my experience in this course to one of my interests?

I’ve scheduled this self-evaluation to occur four times across the term. I’m hoping that it will help students realize that learning is like taking one’s mind to the gym. They can either lift the mental weights themselves, or they can waste their time watching other people — or machines — do it. I’m happy to coach them on how to do the former. The latter isn’t my problem.

*Automatic full marks for completing the survey’s questions, courtesy of the LMS/VLE.

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Taking Stock

As is my habit, I’m already evaluating what worked and what didn’t in my spring term teaching, especially with undergraduates who I’ve discussed previously.

Banning smartphone use was a big success. Before class began, students struck up conversations with each other instead of silently staring at screens. They were also far more attentive during class.

Perusall didn’t work as well as it did in the past.1 I began using Perusall in September 2022 because (1) my prior experiments at collaborative student note-taking had failed, (2) several colleagues had raved about it, and (3) it enabled asynchronous online interaction between students, an essential element of course design given the lingering effects of the Covid pandemic. Two months later, it proved useful for a fourth reason: the release of ChatGPT. Toggling Perusall’s various background settings made the outsourcing of reading and writing to AI much less convenient, which definitely wasn’t possible with reading responses.

This term, the quality of students’ conversations on Perusall and in the classroom seemed lower than before. Since I haven’t dug deeply into any quantitative data, I might be wrong about this, but I don’t think so. In a last-minute attempt to get students to think more deeply and communicate more effectively, I resurrected my wicked problem activity, but with a few tweaks.

First, I tied each problem to the Perusall assignment students had just completed. For example, in relation to a reading assignment on industrial policy, I threw out this scenario:

  • You are Senior Vice President of Operations at Apple.
  • Apple needs a new iPhone manufacturing center.
  • Options: Zhengzhou, China; Jundiaí, Brazil; and Dayton, USA.
  • Which location should Apple choose and why?

Second, before putting students into groups to collaborate on solving the problem, I had them individually complete worksheets that contained these questions:

  • What is the problem I am trying to solve?
  • What solution am I choosing?
  • Why is this the best solution to the problem?
  • What assumptions am I making by choosing this solution?
  • How do I learn whether my assumptions are reasonable?
  • How does my problem-solving process apply to other problems I might encounter?

Although my questions need revision, I’m thinking of doing these wicked problem exercises on a weekly basis in my autumn term courses to see what happens. They are AI resistant, encourage social interaction, and maybe even help students learn how to apply knowledge learned in one context to new ones.

1My series of posts on Perusall is here. Scroll to the bottom for all the links.

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Best Coffee in Town: Update 2

Another progress report on my Best Coffee in Town research project . . .

Whereas seven students chose not to complete the first StoryMap assignment, all seventeen did so for the second. Yay, scaffolding works for formative assessment! Well, no, not in this case. The URLs that seven students uploaded to the LMS didn’t work, which meant I saw the message below instead of their StoryMaps.

The instructions on the LMS for all of the StoryMap assignments contain the following statement:

You will need to submit the link to your BCiT StoryMap here. Your StoryMap must be set to share/public when published for this link to work. Review the Digital Literacy section of the syllabus. Work that I am unable to access will earn a grade of zero.

So, seven zeros, because these students didn’t bother test whether the links they uploaded actually worked.

The remaining ten students earned an average score of 12.8 out of a possible 25 points; i.e., they all failed.

The final StoryMap assignment is worth 100 points, the equivalent of a full letter grade. Will it be an exercise in learned helplessness? I hope not.

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Best Coffee in Town: Update 1

 
 Creative Commons CC0 1.0 Universal

Taking a break from preparing for our next blizzard . . .

A quick progress report on my Best Coffee in Town research project, which I designed to counter AI and combat grade inflation: students, or at least some of them, submitted the first of a series of scaffolded StoryMap assignments. While this assignment constitutes only 2.5 percent of the final course grade, it forms an integral part of the project as a whole. In other words, it lets students know where they stand and where they are headed.

Of the class’s 17 students, 10 chose to complete the assignment, with an average score of 9.5 out of 25 points. In letter grade terms, everyone in the class earned an F.

Will this change as the semester progresses? I don’t know.

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AI-Induced Course Improvements

I’ll be teaching a course on globalization again in the spring term. As usual, I’ve built the course around a StoryMap ethnography project, but I’m incorporating field research in response to how I’m seeing students (mis)use AI. The field research component comes from Amanda’s Best Breakfast in Town, which I used once before in 2019. This time each team of students will need to identify a Best Coffee in Town by evaluating the effects of coffee production in some other country and how and why the coffee is consumed locally. At minimum students might learn a little about the off-campus community. Best case scenario is that they talk with the people who produce the coffee and learn something about them.

Project outcomes thus now depend much more on engaging with the physical world than they used to, and I’ve changed my assessment metrics accordingly by down-weighting text-driven activities that students can offload to AI, such as the location and synthesis of published source material. Students will now need to do a better job with the harder elements of the project, which now comprise a far greater share of assignment grades — which should have the added benefit of helping me continue to combat grade inflation. To illustrate this, here is the rubric I’ll be using to score the final, complete versions of students’ StoryMaps:

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A Degrading Experiment – Preliminary Results

In my last post, I pondered whether eliminating coercive elements of assessment changed student behavior. Would students complete assignments that had no direct effect on their grades?

So far, it looks like the answer is “no.” I’m teaching my usual autumn term course on economic development and environmental change. In previous years, I assigned a graded evaluation for each of the five games that students played in class. This time I assigned three evaluations. The first two were ungraded, but I provided students with feedback that they could apply to the third, graded evaluation, which is due next week.

Failure is not an option

In a class of twenty-five students, five completed the first evaluation. No students completed the second evaluation.

These numbers lead me to suspect that many of my students do what they do to avoid punishment, not because of intrinsic motivation or interest.

Perhaps I should investigate assessment optionality . . .

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The Power of Grades

N now = 2. I tried Terry Hathaway’s exercise in a class of eleven students. All of them complied without question, and when I asked why, the first student to speak said, “Because you have power over our grades.”

I’m a superstar!

I then asked, per Max Weber, whether my authority was based on personal charisma, tradition, or some rational-legal principle. Students hesitated to respond to this question. Since they had been reading about China’s Cultural Revolution, I drew a table on the board with columns titled “Chairman Mao” and “Chairman Chad” to illustrate the human tendency to do stupid things like stand on one foot with one hand on our heads, or denounce/beat/kill our neighbors, only because an authority figure has told us to.

Educational institutions, at least in the USA, function as authority figures in which grades reflect the ability to conform rather than learning. Students perceive education as a performative exercise in which intrinsic interest is irrelevant, and they behave accordingly, a phenomenon that the anthropologist and ungrading advocate Susan D. Blum calls “schoolishness.”

In an attempt to encourage student curiosity and independence, I’ve been, like Terry, experimenting with ungrading, or, as I like to put it, degrading. It’s still a work in progress. This semester, for example, I have a barbell-shaped grade distribution in one of my undergraduate courses/modules, with a quarter of the students failing and half at an A level. The students who do the work, whether it contributes to their final grade or not, are doing fine. Those who don’t are not. Maybe removing the usual mechanisms of coercion also removes the incentive to engage in schoolishness.

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Teaching Trump While You Still Can

Unfortunately, another installment in the Teaching Trump series.

Some books of continuing relevance:

  • Barbara F. Walter, How Civil Wars Start and How to Stop Them.
  • Ece Temelkuran, How to Lose a Country: The Seven Steps from Democracy to Dictatorship.
  • Cass R. Sunstein, ed., Can It Happen Here? Authoritarianism in America.
  • Sarah Kendzior, Hiding In Plain Sight: The Invention of Donald Trump and the Erosion of America.
  • Timothy Snyder,  On Tyranny: Twenty Lessons from the Twentieth Century.

Comparative studies of democratic erosion like these work well as foundations for a forecasting project in which students assign probabilities to the next potential steps in America’s devolution into authoritarianism. For example:

  • Will the U.S. Supreme Court overturn New York Times Co. v. Sullivan?
  • Will martial law be declared in Democratic-majority U.S. cities prior to the midterm elections?
  • Will the U.S. Department of Justice announce criminal investigations of university professors?

The list of previous Teaching Trump posts:

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End of Term Thoughts: Tank Your Student Evaluations

Another brief reflection on the previous semester:

I resumed teaching an intro course on globalization after a five-year hiatus. I decided to compare student evaluations of my teaching in this course to those from 2019.* The table below** shows that my teaching evaluation scores fell off a cliff.

The sample sizes were far too small to have any statistical reliability — 7 respondents out of 24 students in 2019, 10 out of 16 students in 2025 — but the drop still caught my attention. I hadn’t changed much this time around. I again devoted most of classroom time to discussion rather than lecture, and had teams of students create StoryMaps. My use of Perusall was new, but that simply replaced a different type of graded writing assignment. Per student feedback, the course’s level of difficulty was about the same.

My assessment regime, however, was very different, for reasons that I’ve previously discussed. The 2019 class had an average numerical final course grade of 94, equivalent to an A-, while for 2025, it was 80, just a whisker above a C+. My attempt to combat grade inflation worked, and I assume that this tanked my evaluation scores — an assumption that aligns with the published research.

As a tenured professor who has worked in academia for longer than my undergraduate students have been alive, I don’t face any direct career consequences from this. But a lot of faculty aren’t shielded as well as I am.

*I did teach this course in Spring 2020, but campus closed halfway into the semester due to Covid, so I used Spring 2019 instead.

**The evaluation instrument contains many other questions, but they had changed between 2019 and 2025.