When a Student Learns the Heart Through Your Veins
A reflection on adapting lesson material to increase student engagement and retention, drawing from a real tutoring moment and educational data mining research.
Posted with permission from both the student and their guardian.
Harry needed help with his biology midterm. He told me he was struggling to memorize the anatomy of the heart and trace how blood moves through the circulatory system. Normally, I teach English. Literature. Writing. But Harry asked, and I always loved Biology, so I figured we could work something out.
Adaptive Teaching and the Problem of Static Curricula
There’s a tension in education between what we plan to teach and what actually lands. A lesson can be technically accurate, well-structured, and completely forgettable. Liu and Wan (2024) put it plainly when they noted that “learning flexibility significantly influences students’ feedback and performance scores,” which “highlights the requirement for a responsive and adaptable educational strategy” (p. 4). The word “responsive” matters here. It implies listening. Adjusting. Meeting a learner where they are, not where the syllabus assumes they should be.
Harry didn’t need another diagram. He’d seen plenty. What he needed was something concrete, something he could watch happen in real time.
So I pressed down on my vein, cleared it, and let him see how valves prevent backflow. His face shifted. He got it. Not because I explained it better than his textbook, but because the context changed. The material became tangible.
Why Engagement Predicts Retention
This isn’t just anecdotal. Research in educational data mining consistently links emotional engagement with learning outcomes. Liu and Wan (2024) found that “sentiment analysis offers valuable insights into students’ mental health, classroom engagement, and openness to course content” (p. 2). When a student feels something, positive surprise, curiosity, even confusion that resolves into understanding, they encode information differently. The brain doesn’t treat emotionally neutral content the same way it treats content that sparked a reaction.
Harry wasn’t just passively receiving information. He was watching, asking questions, and then, critically, he verbally traced the journey of blood through the heart himself. That act of articulation is retrieval practice. It’s also proof of comprehension. And it emerged because the lesson adapted to him.
The Role of the Teacher in an Age of Automation
I’ll be honest. I’ve been thinking a lot about AI lately. Many of my colleagues have, too. There’s a quiet anxiety in education right now, a sense that what we do might eventually be optimized away. Machine learning models can already predict student performance with startling accuracy. Liu and Wan’s (2024) WResNeXt-GMJ model, for instance, achieved 98% accuracy in forecasting student adaptability levels (p. 1). That’s impressive. It’s also unsettling.
But here’s what I keep coming back to. Harry didn’t just learn something that day. He laughed. He leaned in. He wanted to understand, not because a system told him to, but because the moment made him curious.
Can a model replicate that? Maybe eventually. But I’m not convinced it can replicate what I felt when he finally got it. That mutual recognition, two humans both realizing something clicked, is hard to quantify. And I suspect it matters more than we give it credit for.
Closing Thoughts
If you’re teaching, whether formally or informally, consider this. Adaptation isn’t about abandoning your curriculum. It’s about finding entry points that fit the learner in front of you. Sometimes that means a diagram. Sometimes it means your own arm.
Liu and Wan (2024) described the ultimate goal of their research as providing “students with a flexible, engaging learning environment that transforms their learning approach, facilitating academic and personal success” (p. 3). I like that framing. Transformation, not transmission. The difference is who’s doing the work.
Harry did the work that day. I just gave him a reason to start.
Reference
Liu, L., & Wan, L. (2024). Innovative models for enhanced student adaptability and performance in educational environments. PLoS ONE, 19(9), e0307221. https://doi.org/10.1371/journal.pone.0307221