Who's in the Room, and Are They Engaged

The second and fourth KTCP workshops sit next to each other for a reason: you can design the most beautifully backward-designed course in the world, and it will still fail a student who doesn't believe it's meant for them, or who never gets pulled out of passive listening. This post covers both — belonging, and the active-learning research on what actually gets students to participate.

Belonging isn't a vibe, it's four measurable things

The workshop leaned on a study by Rainey et al. (2018), which interviewed 201 STEM seniors and "leavers" and found that a sense of belonging tracks four concrete, teachable factors:

Factor	The question it answers	Instructor lever
Interpersonal relationships (strongest predictor)	"Do I have real connections in this community?"	Learn names, small-group work, office hours that feel welcoming
Competence	"Can I actually do this work?"	Normalize struggle, scaffold difficulty, give process-focused feedback
Personal interest	"Do I want to do this?"	Connect content to real-world questions students already care about
Science identity	"Is this my community — do people like me do this?"	Diverse examples, role models, avoid tokenizing students

It's also unevenly distributed — white men reported the highest belonging, women of color the lowest, and belonging tracked how well-represented your demographic was within a given sub-field (women fared noticeably better in biology, close to gender parity, than in physics).

Asai (2020) sharpens the point with a number that stopped me: PEER students (Persons Excluded due to Ethnicity or Race) enter STEM at high and rising rates, but still leave at the same high rate as they did thirty years ago. That single fact rules out the usual "pipeline" or "not enough interest" explanations — the interest is there, and growing. What isn't fixed is the culture they land in. Asai's argument is pointed: stop trying to "fix the student," and start questioning institutional defaults — grading on a curve, weed-out courses, prerequisite gatekeeping — that quietly filter people out regardless of ability.

That reframing matters because it turns "diversity" from a vague aspiration into an actual design problem, with the same kind of levers as the rest of course design: who gets called on, how struggle is normalized, whether examples and role models look like the whole class rather than a slice of it.

The exercise: mapping your own identity pie

The workshop's central exercise had us draw a literal pie chart of the identities that make us up, then think about which slices are sources of privilege in our field and which are sources of friction. Mine came out roughly like this:

pie showData
    title My Identity Pie
    "Interdisciplinary background (ME + math + atmospheric science)" : 35
    "Hong Konger" : 25
    "English as a second language" : 20
    "Early-career researcher" : 20

The interdisciplinary slice, it turns out, is a real asset for teaching — students in an environmental or Earth-systems course come from wildly different backgrounds, and having sat in several of those disciplines myself made it easier to meet them wherever they started. The ESL slice pushed me to lean harder on visuals and animated illustrations for anything conceptually dense, rather than trusting my phrasing to carry the idea on the first pass. For the class-facing strategy, I picked competence — the idea that struggle should be normalized rather than treated as a sign a student doesn't belong — and built a concrete plan around it: collect the mistakes students actually make on assignments, have them explain their own reasoning out loud, and coach them toward finding their own blind spots rather than just handing them the correction.

Active learning works — but only if it's structured

The fourth workshop makes the case, with numbers, for getting students out of passive listening.

The headline number

A meta-analysis across 642 STEM studies found active learning drops failure rates from roughly 34% → 22% and raises test scores by about half a standard deviation, consistently across course levels and disciplines. That's about as strong as evidence gets in education research.

The more interesting finding, though, is a paradox from Deslauriers et al. (2019): in a controlled comparison, students in active-learning sections learned more on objective tests but felt like they'd learned less than students in a traditional lecture. Passive listening feels smoother — you're not confused, because you're not doing the cognitively harder work of retrieval and application. That fluency is misleading, and it means an instructor introducing active learning needs to say so out loud, early, or risk students reading the discomfort as evidence the class isn't working.

Tanner's (2013) contribution is the caveat that keeps this connected to the belonging discussion: active learning by itself isn't automatically equitable. Open discussion, without structure, tends to amplify the same confident voices that were already comfortable speaking up. The fix is deliberate structure so "active" doesn't quietly become "active for the students who were already engaged":

[ ] Set explicit participation norms at the start of the term
[ ] Vary how students are called on (not just volunteers)
[ ] Track who has and hasn't spoken across sessions
[ ] Use think-pair-share (or similar) before opening the floor, so everyone has an answer ready
[ ] Mix individual, pair, and group work rather than defaulting to whole-class discussion
[ ] Learn names — it's the cheapest, highest-leverage belonging move available

Here's the technique catalog from the workshop, with the practical trade-off (prep time vs. class size) that decides which one fits:

Technique	What happens	Prep effort	Best class size
Peer Instruction	Poll → discuss with neighbors → re-poll	Low	Large lecture
Think-Pair-Share	Solo thought → pair discussion → share out	Low	Any
Empty Outlines	Blanks for key terms/equations, filled in live	Low–moderate	Any
Real-World Framing	Open/close topics with real-world tie-ins	Low	Any
Debate	Assigned sides, prepared arguments, a "decider" judges	Moderate	Small–medium
Pro/Con Grid	Research both sides of a live disciplinary debate	High	Small–medium
Collaborative Problem Solving	Coached group problem-solving, whole-class ↔ pairs	Moderate	Any
Gallery Walk	Small groups rotate through stations, then debrief	Moderate	Medium (10–20)
Jigsaw	Expert groups study one piece deeply, then teach it to a mixed group	High	Medium–large

What I picked

For my own course, I chose three techniques: empty outlines for equation-heavy material (so students self-check meaning rather than passively copying), real-world framing at the open/close of each topic, and a pro/con grid where students research and argue both sides of a live scientific debate — nuclear energy, or solar geoengineering — before comparing notes with a neighbor.

Next in this series: once students are in the room and engaged, the next question is what they walked in already knowing — and how you build on it without either boring the ones who know too much or losing the ones who don't know enough.