What College Instructors Are Really Doing to Keep Students Engaged
Two years of candid faculty conversations from Reddit show that the most popular engagement strategy has nothing to do with apps, games, or technology. Professors are reaching for the gradebook long before they reach for a tool.
The edtech world tends to frame student engagement as a design problem. Build something interactive enough, and students will pay attention. What college instructors actually say to each other tells a different story. In 19.2% of engagement-relevant comments, more than for any named tool, game, or technique, instructors describe grading students for structure: attendance, participation, frequent short checks that make showing up and trying the path of least resistance. They lose sleep over student attitude far more than over student technology access. The few who are genuinely enthusiastic about a product are almost always describing something that ties directly to learning rather than something that makes class feel more fun. This report explains what that pattern means for companies building products for this audience.
What Instructors Are Doing
When instructors advise each other on engagement, the most common strategy is structure: making attendance or participation count toward a grade, running short reading checks before class, designing deadlines that discourage avoidance. Roughly one in five engagement-relevant comments touches on this. That is more than any named technology, game, or discussion technique.
Attendance grading, participation grading, and low-stakes quizzes together. This is what instructors most often recommend to each other.
Kahoot, iClicker, Top Hat, Poll Everywhere, Mentimeter, Slido, and Wooclap mentioned as engagement strategies. Less than a tenth of the structural approach.
Strategy breakdown
Hover over any percentage to see its 95% confidence interval. Comments column shows advice-giving; posts column shows what instructors ask about when describing a problem to solve.
Short, frequent quizzes with feedback are one of the most reliably replicated effects in learning science. Students who are tested regularly on material remember significantly more of it weeks later, at effect sizes of roughly 0.8 to 0.95 in delayed-retention studies.1 This held in authentic college classrooms, with gains of 8 to 13 percentage points on real course exams and feedback roughly doubling the benefit.3 Graded attendance is a weaker lever: attendance does predict grades more strongly than SAT scores (r=0.44),2 but policies that compel attendance show only small causal effects (d=0.21, k=3) because motivated students simply attend more on their own.2
Three patterns worth paying attention to
Discussion is something instructors manage deliberately, not something that just happens
Instructors describe running discussion with careful structure: warning students they may be called on if the room goes quiet, then rewarding every contribution equally so students feel safe being wrong. One professor described letting an entire Socratic seminar sit in silence for ten minutes, writing in a journal, until students eventually filled the gap themselves.
“The stick is that I warn them if there are crickets, I will call on people. The carrot is that I don’t judge whatever they say. By treating every comment the same, students are less embarrassed about being wrong.”
AI-resistance has become its own form of engagement pedagogy
In-class writing, oral exams, and handwritten work spread because instructors wanted to stop AI cheating. But the pattern in these conversations is that instructors keep describing these formats as their most engaging sessions. What began as a defense against a tool became a philosophy about what makes learning stick. One instructor found a productive use for AI directly: making students critique an AI-written essay, which they described as more eye-opening than any lecture they could have given.
“I have my students make ChatGPT write an essay, then their actual assignment is to critique it. That’s done more for my students seeing how truly lacking it is than any lecture I could give them.”
A small but energetic group is using AI to understand their own students better
At 2.4% of comments, AI as an instructor tool is the smallest strategy in this table. But the instructors describing it are the most enthusiastic voices in the corpus. One professor built an agent that read his lecture transcripts alongside student survey responses and produced a same-day report on what to address next. No commercial product currently fills this space.
“It was like having X-ray vision into the learning journeys of the students. Every report was a joy to read, even the more critical ones.”
What Instructors Care About
One value runs through almost every engagement-related conversation in this corpus: real engagement is only worthwhile when it leads to genuine learning. In a quarter of relevant comments and 40% of problem posts, instructors draw a clear line between engagement that produces understanding and engagement that just fills time.
“Everyone is going to have a different definition of fun and fun does not mean flashing lights and music in a college class.”
“Meaningful engagement is a desire students need to develop and not something you control. You can’t make somebody fall in love with you by giving them flowers and chocolate on Valentine’s Day.”
These values have direct implications for how edtech products are received. Messaging built around fun, points, or game mechanics runs into skepticism with this audience. Messaging built around learning evidence and time saved fits the values they actually hold. The time and eval-pressure values also explain why products get abandoned: anything that adds setup work or creates grading complexity faces resistance from people who are already stretched.
Faculty burnout in higher education is measurable and significant. A 2023 survey reported that 64% of faculty feel burned out and 55% are less satisfied with their jobs than five years ago, compared to 46% of staff and 38% of administrators.10 Research on faculty disengagement describes a pattern where burned-out instructors reduce effort to only what is required, withdrawing from informal mentoring and advising work that most directly affects student outcomes.11 Products that claim instructor time compete with this backdrop. Products that return it address something real.
What Instructors Are Struggling With
The complaint that appears most often has nothing to do with technology. Instructors are describing students who treat the course as a box to check, who do the minimum required and nothing more, and who grow frustrated when the course asks them to think. That pattern shows up in one in five comments and one in three problem posts, and it is the root under most of the specific complaints that follow.
“I genuinely want my students to succeed. I update my course materials regularly, incorporate more engaging and relevant content than I did a decade ago. But somehow, it still feels like a losing battle.”
“We can’t force them to come in for exams, and when ChatGPT took off, every student got 100% on the multiple choice section. It’s gone from surprisingly rewarding to soul destroying.”
Reddit functions as a peer coaching system for this profession: one instructor describes a struggle, and dozens respond with what has actually worked. The most upvoted coping response in our entire sample captures the emotional work instructors are doing.
“Some bands would just destroy, playing to 6 people and the doorman as if they were playing to a packed stadium. I can grumble about everyone who’s not there, or I can just lean in and teach the hell out of my class for however many of them are there. I play for them.”
The instructors’ diagnosis maps closely to published findings. A controlled experiment (n=117) found that students using ChatGPT produced the most improved essays but showed no greater knowledge gain or transfer, spending less time reviewing sources and evaluating their own work.4 Researchers called this “metacognitive laziness.” An Anthropic analysis of 574,740 student AI conversations found roughly half sought direct answers with minimal back-and-forth.5 The transactional attitude complaint has a documented mechanism: a study of 608 UK undergraduates found that students with a stronger consumer orientation toward education had lower academic performance.6 Post-COVID disengagement is real and lasting: a 2025 report covering 1.1 million survey responses across 22 research universities found most campus-involvement indicators still below pre-pandemic levels as of 2023,7 a finding echoed in national survey data from 2021.8 Phone distraction has causal support: a meta-analysis of 27 randomized experiments found a near-large negative effect on lecture recall specifically, Hedges’ g = -0.70.9
The Tools They Mention
This is a full-corpus count across all 65,035 posts and comments, not a sample. It shows how many individual units mentioned each tool and how many distinct threads the conversation appeared in. AI dominates, primarily as a threat. Canvas is the most-named edtech product by far, but complaints focus on workflow friction. Among engagement-specific tools, iClicker still outpaces every newer polling brand. And the tool-shopping threads frequently end with someone asking for “something like Kahoot but better.”
Top tools by number of mentions
AI and ChatGPT bars scaled to corpus max (3,857); all other tools scaled relative to ChatGPT for readability. Red bars indicate primarily threat-framing context.
Techniques mentioned (full corpus count)
A 2025 meta-analysis of 43 Kahoot experiments found a moderate positive effect on achievement (g=0.77) with larger effects on motivation (g=0.96) and retention (g=1.49).13 But a 2025 PRISMA review of audience-response systems specifically in higher education found no consensus on learning outcomes, with 6 of 11 rigorous studies showing no significant difference from control groups.14 These tools produce real learning gains when used as formative assessment, and produce noise when used as entertainment. On gamification broadly, a 2020 meta-analysis found that cognitive effects are small but stable (g=0.49) in rigorous studies, while motivational (g=0.36) and behavioral (g=0.25) effects collapse to non-significance when study quality is controlled.15 Short-term enthusiasm fades, and competition-oriented elements like leaderboards can actively demotivate students who rank low.
What This Means for EdTech
Six patterns emerge from combining what instructors say on Reddit with what peer-reviewed research confirms. Each represents a place where demand is real, evidence is present, and the commercial category either does not yet exist or has significant room to improve.
Make accountability effortless
When instructors reach for an engagement solution, they reach for structure before a tool. Grading for attendance, grading for participation, running short quizzes before class. The strategy is sound, the demand is the highest in this conversation, and the bottleneck is almost always time. Any tool that packages these accountability structures into a single flow with minimal grading overhead sits directly on the biggest unmet need in this corpus.
Build for the post-AI classroom
In-class writing, oral components, and process-visible assignments are spreading fast. Instructors adopted them to stop AI cheating and kept them because they work. Any product that helps instructors design, deliver, or assess these formats addresses a growing institutional priority. Institutions are already moving toward assessment redesign as their main AI response (26%), rather than detection (20%) or bans (18%). Bans do not work because students continue using the tools regardless.
Give the polling category a brain
The most-mentioned polling tool is still iClicker, invented decades ago. Instructors asking for something “like Kahoot but better” are describing a gap in substance, not style. They want a polling and quiz tool that feeds back into learning rather than entertainment, with better reliability at scale and participant caps that hold. Six different brands appear in this corpus with no clear dominant preference beyond the legacy clicker.
Sell learning outcomes, not excitement
One in four engagement-relevant comments includes the idea that real engagement is about learning, not entertainment, and instructors actively police this line. Messaging built around fun, game mechanics, points, or streaks will trigger skepticism. Messaging built around learning evidence and time saved fits the values they actually hold. Any product that adds setup or grading time also fights a 64% burnout headwind, and anything that might produce mixed student evaluations creates professional risk for untenured instructors.
Build the instructor’s AI copilot
The most enthusiastic AI voices in this corpus are professors who built tools to understand what their own students are confused about. One professor’s homemade agent read his lecture transcripts alongside student survey responses and produced a same-day report on what to address. No commercial product currently owns this conversation. The category is real, the demand is expressed clearly, and the audience for it is already motivated.
Return time to the instructor
Almost two-thirds of faculty report feeling burned out, and 55% are less satisfied with their jobs than five years ago. When burnout takes hold, instructors withdraw from the informal mentoring, advising, and relational work that most directly affects student success. Products that reduce setup or grading time address something real. Products that add configuration requirements or ongoing workflows compete with exhaustion.
Methods
How this research was conducted
We collected two years of posts and comments from eight Reddit higher-education communities, covering June 2024 through June 2026. We used a two-pass scraper to find threads discussing student engagement, collected all content from those threads, and removed bot accounts and irrelevant material before analysis. The final corpus was 65,035 posts and comments across 1,566 threads.
Corpus composition by community
Honest limits
This research describes what is being said in a specific online community at a specific time. It is not a random sample of every college instructor in the country.
- Reddit is not the professoriate. r/Professors skews toward instructors who are dealing with frustration, US-based faculty, and those who carry heavier teaching loads. Satisfied instructors or those with lighter loads are underrepresented. Percentages describe what is being said in this conversation, not what every instructor believes.
- One community dominates. r/Professors makes up 77% of all rows. Weighting corrects for sampling within each community, but the discourse norms of one particularly active community color the whole dataset.
- We searched for engagement-related threads. Themes that rarely appear alongside engagement-specific terms will be underrepresented. The full listings sweep was skipped when the discovery cap was hit, so coverage favors explicit engagement talk over peripheral mentions.
- Coders were slightly more inclusive than the lead. Inter-rater reliability on the relevance gate was kappa 0.70. Coders admitted slightly more content than the lead coder would have, which means theme rates in this report are slightly conservative.
- r/OnlineEducation was effectively empty. This community contributed nothing to the corpus. Online-modality voices appear through other communities but may be underweighted relative to face-to-face instruction discussions.
- AI was not in our search terms. Every AI mention in this corpus appeared organically. The 12.3% figure for AI as a pain is a floor, not a ceiling. The actual share of faculty thinking about AI in engagement contexts is almost certainly higher.
References
- Roediger, H. L., & Karpicke, J. D. (2006). Test-enhanced learning: Taking memory tests improves long-term retention. Psychological Science, 17(3), 249–255. View PDF
- Credé, M., Roch, S. G., & Kieszczynka, U. M. (2010). Class attendance in college: A meta-analytic review of the relationship of class attendance with grades and student characteristics. Review of Educational Research, 80(2), 272–295. View article
- Brame, C. J., & Biel, R. (2015). Test-enhanced learning: The potential for testing to promote greater learning in undergraduate science courses. CBE—Life Sciences Education, 14(2), es4. View article
- Fan, Y., et al. (2025). Beware of metacognitive laziness: Effects of generative artificial intelligence on learning motivation, processes, and performance. British Journal of Educational Technology, 56(2), 489–530. View article
- Anthropic (April 2025). Analysis of 574,740 anonymized student AI conversations. Reported by Hechinger Report (May 2025). View reporting
- Bunce, L., Baird, A., & Jones, S. E. (2017). The student-as-consumer approach in higher education and its effects on academic performance. Studies in Higher Education, 42(11), 1958–1978. View article
- SERU Consortium (March 2025). Longitudinal student engagement data: 1.1 million survey responses, 22 major research universities, 2016–2023. Reported by Inside Higher Ed (April 2025). View reporting
- National Survey of Student Engagement (2021). Annual results: The pandemic’s influence on student engagement. Indiana University Center for Postsecondary Research. View results
- Mobile phone distraction meta-analysis (2024). 27 randomized controlled experiments, 55 effect sizes, n=2,245. Lecture-recall effect: Hedges’ g = −0.70, 95% CI [−0.86, −0.54]. Computers in Human Behavior. View article
- Chronicle of Higher Education (2023). Higher education is exhausted. National survey of faculty, staff, and administrators. View article
- Chronicle of Higher Education (2022). The great faculty disengagement. View article
- Tyton Partners (2024). Time for Class 2024. National surveys of approximately 1,600 students, 1,800 instructors, and 300 administrators, Spring 2024. Lumina Foundation. View report
- Kahoot! meta-analysis (2025). 43 experimental studies, n=1,706 experimental, n=1,647 control. Achievement effect g=0.77 (95% CI 0.54–1.00). Computers & Education. View article
- PRISMA systematic review of audience-response systems in higher education (2025). 11 methodologically rigorous studies identified from 653 screened articles. Active Learning in Higher Education. View article
- Sailer, M., & Homner, L. (2020). The gamification of learning: A meta-analysis. Educational Psychology Review, 32, 77–112. Cognitive g=0.49 (stable in rigorous subset); motivational and behavioral effects non-significant in high-quality studies. View article
- AI-generated formative feedback quality study (2025). AI feedback matched human feedback on correctness and cognitive dimensions; exceeded it on metacognitive and motivational dimensions. Computers and Education: Artificial Intelligence. View article
Produced by Openfield · June 2026 · Data collected June 2024 through June 2026