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| Edition 39 · October 2, 2026 |
Trapped by Content |
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Dear Colleagues, Welcome to the 39th edition of Weekend Reads from CEPI. In 2011, three researchers at the University of British Columbia set up a contest between two ways of teaching. They took two sections of a large first-year physics course, about 270 students each, both meeting in a big theater-style lecture hall. Both sections spent the twelfth week of term on the same topic, with the same learning goals, the same class time, and the same test at the end. One section kept its usual instructor, an experienced lecturer with high student evaluations. The other went to a postdoctoral researcher and a graduate student with little teaching experience. They gave students a reading and a short quiz before each class. In class, students answered clicker questions and argued over them with the people beside them, worked problems in small groups, and got quick feedback from the instructors walking the rows. |
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| The same room, the same topic, the same week, the same test. |
| Image credit: Concept by Anzar Khaliq | Center for Empowerment and Pedagogical Innovation (CEPI), San Francisco Bay University | Visuals created with ChatGPT |
Before reading on, decide which section you would bet on. |
Lecture Experienced, highly rated lecturer 41% | Active learning Two instructors new to teaching 74% |
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The lecture section averaged 41 percent on the test, given in the first class after the unit. The other section averaged 74. The lecture section also covered more. In the time available, it reached the material for all twelve test questions, and the active section reached eleven. So the section that covered less of the test scored far higher on it. A test taken a few days after one unit cannot tell us what students keep by the end of a term, but the gap is hard to explain as a difference in content. One experiment could still be a fluke. In 2014, Scott Freeman and colleagues pooled 225 studies of undergraduate science, engineering and math courses, using many kinds of active learning, and found results pointing the same way. Students in traditional lecture courses were about one and a half times as likely to fail. |
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The Coverage Question |
So why do so many of us keep lecturing? Usually it comes down to a sensible worry. There is a lot of content, and class time feels like the only time to cover it. The worry is fair. A Python course has syntax and techniques students must learn. A data structures course builds idea on idea. Engineering has foundations no activity can skip. Students can strengthen the worry without meaning to. In 2019, Louis Deslauriers and colleagues at Harvard taught introductory physics students some topics by lecture and others by active learning. Students learned more in the active sessions and said they had learned less. The authors traced this partly to effort. Working through a hard problem felt to students like not understanding it, and a smooth lecture felt like progress. Look at the worry closely, though, and it turns out to be two claims stuck together. |
Students need to learn this content.
I need to deliver all of it during class. |
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The first is a responsibility. The second is one way of meeting it, and the UBC experiment suggests it is a weak one. Pull the two apart and the trap is easy to see. If delivering is the only way to cover content, then every minute spent on anything else feels like content lost. So we explain, advance the slide, and explain again, and participation waits for time that never comes. Grant Wiggins and Jay McTighe named coverage one of the “twin sins” of course design in Understanding by Design. The other is activity with no clear purpose, hands-on work that keeps students busy and leads nowhere in particular. Both lose sight of what students should understand at the end. Wiggins and McTighe start there and design backward. A recorded lecture on almost any topic in an introductory course is a search away, much of it free. When students can already reach the world’s lectures, what is the class hour for? The UBC section suggests an answer. It is the time when students try an idea while someone who knows it well watches, sees where their reasoning breaks, and helps them repair it. |
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Three Changes to Try This Term |
| 1 | Deliver the content before class, and use class for building. Give students the material beforehand, a reading, a short video, or a recorded lecture. Before class, check what they understood with a few questions or a short conversation with an AI tool. Then give the class hour to teams working on real problems in front of you. A programming class traces and fixes code. An engineering class tests a design’s assumptions. A business class defends a decision on a messy case. If many students arrive unprepared, open with a short explanation of the key idea and then start the problem. What the research says The UBC section in the opening worked this way. Students met the content in a reading and a short quiz before class, which left the class hour for clicker questions and group problems. In 2019, a team at Utrecht University analyzed 114 studies of flipped classrooms and found that what happens to the freed time matters. Results on learning were stronger when courses kept the full class hour, and weaker when flipping became a reason to cut it. Short quizzes on the preparation also helped. That pre-class check is the core of Just-in-Time Teaching, developed by Gregor Novak and colleagues. At SFBU CEPI takes that check one step further. In How Can We Thrive? Scientific Inquiry and the Future of Sustainability, students talk through the reading with Bayley, an AI conversation partner that follows up when an answer is thin. |
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| 2 | Update the examples, even when the content still holds. Open the oldest deck in your course and look at its examples, images and references. Some may be the publisher’s own slides, with the textbook’s watermark still in the corner. Keep the principle and replace what dates it. For any resource, ask when it was excellent, and whether it still is. What the research says Chris Hulleman and Judith Harackiewicz asked high school science students to write about how the material connected to their own lives. Interest and grades rose, most of all for students who expected to do poorly. The study tested that writing task and did not compare old examples with new ones. Our reading, which goes beyond the study, is that a current example is one of the easiest ways to show students where an idea meets their world. Imagine you were preparing for a career change yourself. How long would you stay with a course whose examples stopped years ago? |
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| 3 | Teach the tools the field uses today. List the professional tools your course uses, such as project management software, a programming language, or a data visualization package. For each one, ask what students are learning through it. If the tool only carries a lasting principle, an older version may serve well. If students are learning the workflow itself, check it against what the field uses now and teach that version. What the research says Jan Herrington and Ron Oliver’s framework for authentic learning asks for tasks that reflect how knowledge is used in practice. It is a design framework and does not test particular tools, so the check against current practice is one each of us makes for our own field. If you were retraining for a new field, how much would you want to learn a tool employers no longer ask for? |
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Weekend Prompt
Pick one of the three changes and one week of one course. Change only that part of the week and keep the rest as steady as you can, so the change is easier to see.
Before the week starts, write down what you expect students to produce. Afterward, compare. What did they make, what could they explain, and what would you change next time? Treat what you see as a first observation, and keep your notes for the next round.
Write and tell us what you found.
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References| • | Deslauriers, L., McCarty, L. S., Miller, K., Callaghan, K., & Kestin, G. (2019). Measuring actual learning versus feeling of learning in response to being actively engaged in the classroom. Proceedings of the National Academy of Sciences, 116(39), 19251–19257. | | • | Deslauriers, L., Schelew, E., & Wieman, C. (2011). Improved learning in a large-enrollment physics class. Science, 332(6031), 862–864. | | • | Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410–8415. | | • | Herrington, J., & Oliver, R. (2000). An instructional design framework for authentic learning environments. Educational Technology Research and Development, 48(3), 23–48. | | • | Hulleman, C. S., & Harackiewicz, J. M. (2009). Promoting interest and performance in high school science classes. Science, 326(5958), 1410–1412. | | • | Novak, G. M., Patterson, E. T., Gavrin, A. D., & Christian, W. (1999). Just-in-Time Teaching: Blending Active Learning with Web Technology. Prentice Hall. | | • | van Alten, D. C. D., Phielix, C., Janssen, J., & Kester, L. (2019). Effects of flipping the classroom on learning outcomes and satisfaction: A meta-analysis. Educational Research Review, 28, 100281. | | • | Wiggins, G., & McTighe, J. (2005). Understanding by Design (2nd ed.). ASCD. |
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Wishing you a reflective and restorative weekend.
Warmly,
Center for Empowerment and Pedagogical Innovation (CEPI)
© 2026 CEPI. All rights reserved.
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