Three weekly tidbits from the Center for Teaching & Learning plus upcoming events.
You may or may not be ready to use generative AI for teaching and learning, but there are so many other uses. These large language models are really good with language, so if you need something well-written in a short amount of itme, try out one of the AI tools we have access to, like Gemini, Copilot, or Perplexity.
This article in Change: The Magazine of Higher Education does an excellent job of outlining the effect that political pressures have had on faculty across the country and how some faculty in Florida have responded.
Spoiler alert: you can’t tell how well the students actually learned in your course. While feedback on your course evaluations will be helpful to understand the student experience, a recently published meta-analysis found no correlation between student evaluations of teaching (SETs) and later performance, and actually a negative correlation after grade controls. When schools connected contract renewal to SETs, there was evidence of grade inflation by those instructors.
One of the challenges of teaching is reaching every student. This can be especially difficult in distance courses where you don’t have a chance to grab a student after class to check in on them.
Are you curious about what it’s like for someone with a vision impairment to navigate a website? Gareth Fuller attended this training at the Association on Higher Education and Disability (AHEAD) conference last week and returned with the session materials. This website explains how screen readers work and what the experience is like for someone to use it. It also provides guidance on downloading and using software to try it yourself.
Deslauriers et al. (2019) compared traditional lecture with active learning in an introductory physics course. Although students in the active sections learned more—as shown by higher performance on objective tests—they felt like they learned less. The authors argue that active learning requires more cognitive effort, which students may interpret as poor learning, while smooth lectures create an illusion of learning. This mismatch suggests that student perceptions alone (e.g., course evaluations) can be misleading when judging teaching effectiveness.
Rubrics are handy tools for providing clear expectations and consistent feedback to learners, but students also welcome authentic feedback that sounds like it came from you. You can add your own “voice” through the commenting tool on the rubric in Brightspace or by adding multimedia feedback.
The University of Kansas has a fantastic team supporting their CBE program, including a psychometrician (an expert in the measurement of mental capacities and processes) who developed a taxonomy of assessment types. While it is still in development, you can find the verb used in your learning outcome in the list in this database (such as Apply) and see helpful related information. This includes:
Chew & Cerbin propose a research-based framework of nine interacting cognitive challenges that teachers must address in order to promote “optimal learning” rather than merely acceptable performance. They emphasize that teaching is not just delivering content but creating the conditions in which students learn. Each of the nine challenges represents a characteristic of how students think, learn, or struggle — the idea being that failure to address any one of these can undermine learning. The authors describe each challenge, provide examples, and suggest instructional strategies for mitigation.
I had the pleasure of attending a session on the Science of Meaning with Dr. Todd Kashan from the Well-Being Lab at George Mason University. According to his research, the three primary drivers of meaning in life are: (1) coherence, (2) significance, and (3) purpose. How can you use this in your teaching?