When I started co-teaching with Prof. Sylvia Sullivan in Spring 2026, I carried a set of beliefs about teaching that I had built mostly in reaction to my own student experience throughout the years. Through my BS and MSc in India, I had sat through long stretches of traditional, didactic lectures where the teacher's contribution felt small and most of the real work happened later, alone, in self-study. When I came to the US for my doctorate, I had the unexpected good fortune of taking a few courses that used active learning, and the contrast was sharp enough to stay with me. I joined this program (the CIRTL Postdoc Pathways) wanting to teach in a way that did not put students through what I had been through. I wanted them to walk out of the room with something. I want to plant seeds rather than emphasize mastery, a couple of ideas that can take root and would still be there in six months when the rest of the semester had faded.

I knew lectures took work. I did not know how much. I had assumed the gaps in my own understanding would surface while I was preparing the material. In practice, they often surfaced only in the classroom itself, in front of students, in the half-second between a question and an answer. That experience pushed me into a different relationship with prep time. It also made expert blindspot less of an abstract idea from the CIRTL training and more something I caught myself doing in real time, racing through a derivation that felt obvious to me while watching the room go quiet.

I had read about the predict-then-explain technique (predict-observe-explain) and arrived assuming it was the right default move. In practice, I found that asking students to predict a phenomenon they had no prior contact with often produced anxious silence rather than productive engagement, especially in an 8 AM class with chemical and environmental engineering majors who were not in this course out of pre-existing fascination with aerosols and its climate effects. What worked far better, and became my signature move, was to state a fact or a phenomenon first and then ask the students why. The why question turns the room from a transcription exercise into a reasoning one, but enough scaffolding is already in place that students have somewhere to start. Pairing this with Slido polls gave me a window into what they were actually thinking, not just whether they were following along. The egg-boiling Slido word cloud, which I used to introduce the components of a climate model, drew responses I genuinely did not expect, and the room visibly woke up when students saw their classmates' answers on the screen.

The pieces that did not work are worth mentioning here. My last session, on climate model representation of aerosols, was one I had built carefully and could not finish. Seven of seventeen students were in the room; the others were in their senior design capstone. I got through climate model basics but barely touched the actual aerosol representation, which was the point of the session. The geoengineering segment the week before had felt strong (the SRM360 surveys and the interactive SAI simulator from Reflective (see Samples) gave students something to react to that slides alone could not), but its slow pace had cost me almost a session I never got back. In addition, I am still actively working on slide density. I tend to over-prepare and over-pack, partly because I want students to have a reference they can return to, partly because I do not yet trust myself to leave things off the screen. The student feedback survey was generous on most fronts but named this directly, and the students were right. Perfectionism has helped me in research and cost me in teaching. Thus moderation, not eradication, is what I am after.

What I want to keep learning is mostly downstream of all this. I want to get better at the discipline of leaving things out. Takeaways and conceptual models matter more than information itself. I want more practice with flipped-classroom designs, which I read about in CIRTL but did not get to implement in this course. I want to think harder about how summative assessments can carry weight that I prefer to assign to formative exams. And I want to keep being a reflective practitioner, which I now understand as a habit rather than a credential. It is the willingness to keep noticing what is and is not working, and to let the next session change because of it.

Some of the most useful feedback on my teaching came from Prof. Sullivan, who observed four of my sessions and wrote detailed notes. I've read them closely and outline below the points I should work on.

The growth I'm still working toward falls into two horizons. The first is presence. For an 8 AM class my energy can read a bit flat, and I default my attention to whichever side of the room is answering. The fixes include standing center-front, moving between the computer and the projector, and using quiet participation methods (thumbs up/down voting) to pull answers from students who won't volunteer aloud. The one I care most about is learning to sit in silence after I ask a question. Following the fact-first inquiry approach, I realize I should give students more time before I lead them to the answer. The idea is to state a result and then question students backward through the mechanism that produces it, and that method collapses if I cannot tolerate sixty seconds of quiet without answering my own question.

The longer-term work is calibration of the material. Teaching a mixed undergraduate and graduate students means constantly judging how much to unpack, especially something that is interdisciplinary. Prof. Sullivan caught me oversimplifying some aspects of one lecture while overpacking information in others. Other feedback was about making concepts more relatable to students who do not yet know the jargon of the field. This is a familiar trap in academia where the deeper you go into your own discipline, the easier it is to lose sight of how its language sounds to someone encountering it for the first time. Closing that gap is the harder skill, and the one I most want to keep developing.

The program material and the discussion across two semesters of the CIRTL Program gave me a foundation in education pedagogy. I came in with instincts and reactions, much of it built from what I disliked about my own student experience; I left with a set of evidence-based principles to anchor those instincts. Early in the CIRTL course, I encountered a mathematician's note that effective teaching rests on four foundational principles, almost like Euclid's axioms:

• students' prior knowledge matters,

• how they organize that knowledge matters,

• they need practice with feedback, and

• the affective domain of emotions and motivation matters.

That framing stuck with me because it gave me four questions I could ask of any lecture I was preparing. My Slido quizzes, for example, stopped being a generic engagement tool and started being a deliberate probe of prior knowledge and surfaced misconceptions. Bloom's taxonomy became a tool for designing assessment questions rather than a poster on the wall, and the final term project I built around analysis of real climate model output was a direct attempt at the higher levels of that taxonomy. The distinction between deep and strategic learning helped me put into words something I had seen in the classroom and experienced myself, where even strong students drift toward strategic goals as the semester loads up on them, and my job is to keep some deep work alive inside that strategic frame rather than fight it. 

A broader view of diversity, one that includes prior knowledge and engagement levels alongside social background, shaped how I worked with my mixed class of undergraduate engineers and a small number of graduate students, and pushed me to draw analogies from their own majors so that abstract aerosol concepts could land inside frameworks they already trusted. The Teaching-as-Research framing, which positions the classroom as an iterative exercise rather than performance, is the meta-level stance underneath all of these techniques.

I also had the chance to critically review some of the concepts in the program once I had an overview of it. I realized that it is not necessary to hold active learning on a pedestal. Exploring where active learning genuinely costs more than it gives, or where a traditional lecture still has a place once a time for telling has been created, mattered more than absorbing these ideas wholesale. Following on from the program, I spent time reading on my own about where active learning might fail lectures are time-efficient, as students do bring well-adapted strategic skills, and active learning introduces real chaos in the classroom. Engaging with these issues, rather than dismissing them, is what I now mean when I say I want to be a reflective practitioner, an instructor who keeps evaluating evidence, keeps adjusting, and does not pretend the trade-offs are not real. The principle of less is actually more is one I read this semester and have not yet fully internalized, but it has become a useful challenge whenever I catch myself over-packing a slide.

The cohort spanned disciplines I would not otherwise have spent time with: physiology, plant genetics, astronomy data labs, food-justice and wellness seminars. Watching active learning play out across those topics, instead of reading about it, changed what I tried in my own sessions. Slido is the clearest example. After hearing peers describe think/pair/share and clicker re-polling, I stopped using Slido as a polling tool and started using it to make students commit to an answer before I responded. One peer mentioned in passing that her students had been lying on thumbs-up checks. I had been seeing the same thing in my own class without putting words to it. I shifted toward fact-first prompts: fill-in-the-blank, predict-then-analyze, things students could not nod their way through.

A harder conversation was about participation. I remember group discussions on the fact that under active learning, engaged students stay engaged, and quieter ones get quieter. I had assumed the format was inclusive on its own but it is not. I was watching the same thing happen during my class, which pushed me toward more deliberate cold-calling the entire class. 

Two other topics stayed with me. AI use came up a lot in our discussions. Peers said students were using AI tools without disclosing it, even when they had been told they could, which made clear that the disclosure problem is structural and permission alone does not fix it. A conversation about class prompts also went against my intuition. Easy prompts had students zooming through, while harder, more open ones kept them talking longer, and the time spent in the room tracked with how much they understood. I had been defaulting to simpler prompts to keep momentum in class, but I realized more complex ones would also do good, albeit hard to implement for an 8 AM class.

The prep-time check-in was, unexpectedly, reassuring. Prep time varied enormously among peers doing similar work, with no clear correlation to class size or experience. It made it easier to stop measuring my own hours against an imagined standard.

Bloom's taxonomy has been one of the more useful pieces of CIRTL training for me. The idea of bloomification, writing assessment questions with deliberate cognitive verbs, changed how I approach questions and in-class problems for my co-teaching course. It made my prompts crisper, gave students a clearer target, and made my grading more consistent. I have used it on the two homeworks I redesigned and for the final term project.

What I was less prepared for was finding the same tension inside the CIRTL materials themselves. When Prof. Knight in the videos introduced Bloom's, she presented it as a pyramid where the levels build on each other, so that working at the top means you have already mastered the skills lower down. Learning expert Bill Penuel pushed back on that universality. He pointed out that Bloom's was developed before we appreciated how domain-specific learning is, and that higher-level practices look drastically different in physics than in biology. I then found a very recent critique titled "Bloom's taxonomy is wrong, just ask Bloom." The argument was similar, that the strict hierarchy in Bloom's taxonomy does not match how students actually think. In our class for the term project, students often have to analyze model output, look at the shape of a size distribution or a forcing curve, before they understand what the simulation is showing. The Bloomian order would have them understand first and analyze later. In practice, the analysis is what produces the understanding. The other point that stuck with me was that "higher-order" verbs are not always harder. Identifying which aerosol process is dominating a visual plot of size distribution can be far more difficult than evaluating which physical mechanism matters most in a textbook comparison. This seems to fit Penuel's domain-specificity point in our setting. Reading a size distribution is a skill particular to this field, and it does not slot cleanly into a verb that a general pyramid would label lower or higher.

The same author talks about the The Cognitive Web model and the Golden Tetrad in particular (analyse, evaluate, justify, explain), which maps much more cleanly onto what I have been doing in class. The fact-first inquiry technique, where I state a result and then question students backward through the mechanism, is already a tetrad activity where students analyze the mechanism, justify why it dominates, and explain it back. I am not going to drop Bloom's verbs from my assessment design as they still help me bound a prompt. But I am going to stop treating the hierarchy as a ladder and start treating the four tetrad skills as the actual targets, especially for the term project where analysis, justification, and explanation are inseparable.