“I'm fascinated by the way humans learn”
Tanja Käser joined EPFL in the middle of the Covid-19 pandemic. Three years on, she’s teaching a class of 600 students and is the recipient of the 2023 Credit Suisse Award for Best Teaching.
When Tanja Käser landed the role of tenure track assistant professor at EPFL, she never imagined she’d spend her first day in the job alone, working from a makeshift desk in the corner of her bedroom. That was in May 2020, at the height of the pandemic. "It was strange in this situation to tell myself that I was now a professor at EPFL," she smiles. “I didn’t meet my new colleagues face-to-face for several months.” That fall, she taught her first classes online. And as spring rolled in, her daily routine remained unchanged: giving lectures to a screen teeming with faces and black rectangles bearing still-unfamiliar names.
For the head of the EPFL’s Machine Learning for Education Laboratory (ML4ED), it was an unconventional start to life as an educator. But it helped her develop a solid teaching ethos from the outset – one based on active learning, repetition, and linking theory to practice. “In my view, to be a good teacher, you have to be able to ‘read’ a class and adapt your approach to each student’s strengths and learning style,” she says. This guiding philosophy earned her the Credit Suisse Award.
A purpose-driven approach
“I’m fascinated by the way humans learn,” says Tanja Käser, whose research involves developing machine-learning models to understand and improve human learning. “For me, it’s really important to do something meaningful. I also like the ethical side of things, which is especially important when you’re applying algorithms to education.”
In my view, to be a good teacher, you have to be able to ‘read’ a class and adapt your approach to each student’s strengths and learning style.
Despite her interest in human learning, she never saw herself pursuing a career in teaching. “Quite the opposite in fact!” she says with a smile. “I come from a family of teachers, so I was dead set against the idea.” However, she always knew she wanted to do something purposeful and make a difference through her work. So it was only natural that she took her interest in computer science – something she came to by way of civil engineering – and applied it to the field of education. “During a week-long taster program for girls at ETH Zurich, I saw these amazing virtual demonstrations of civil engineering projects,” says Käser. “They really piqued my curiosity.” After completing her studies, she spent a year working as a consultant at McKinsey before returning to research.
Holding the attention of 600 students
As a researcher passionate by education, Tanja Käser is also intensely committed to her courses. Especially the weekly discrete mathematics class she’s teaching for the second time this fall, which covers some of the key concepts behind algorithms. She acknowledges that lecturing to 600 first-year students packed into a Rolex Forum auditorium isn’t ideal, and that coordinating a team of 30 student assistants and 10 PhD assistants is an energy-sapping task. So how does Käser hold the attention of hundreds of people when it’s impossible to catch everyone’s eye? “I try to speak for no more than 10 or 15 minutes at a time,” she says. “Then I ask questions using the SpeakUp app. Students discuss the questions among themselves, which tells me whether they’ve understood the material. Each semester, I also give four quizzes to make sure my students are on track. It seems to work well. I’m very happy because setting up this class took a lot of effort.”
She admits that work consumes a lot of her time, including at weekends. But that’s because she finds endless fascination in exploring algorithms and delving into the nuts and bolts of human learning. For her Master’s class – Machine Learning for Behavioral Data – Tanja Käser uses a project-based approach implemented in association with the Swiss EdTech Collider, a community of educational technology startups.
“For each Master’s class, we select two or three startups that are willing to share their data with us,” she explains. “Then we divide the students into groups of three, and they choose the dataset they want to work with. The startups benefit from having the students analyze their data, while the students get a chance to apply what they’ve learned by tackling concrete problems and doing something useful.” At the end of the semester, each group give a poster presentation to the startups and the teaching team. A stimulating moment of exchange. Fortunately, in stark contrast to her early days alone in front of her screen.
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