How an AI talk meter can prompt teachers to talk less and students to talk more

When just the tutors saw the talk meter, they tended to curtail their explanations and talk much less. But despite their efforts to prod their tutees to talk more, students increased their talking only by 7%. 

When students were also shown the talk meter, the dynamic changed. Students increased their talking by 18%. Introverts especially started speaking up, according to interviews with the tutors. 

The results show how teaching and learning is a two-way street. It’s not just about coaching teachers to be better at their craft. We also need to coach students to be better learners. 

“It’s not all the teacher’s responsibility to change student behavior,” said Dorottya Demszky, an assistant professor in education data science at Stanford University and lead author of the study. “I think it’s genuinely, super transformative to think of the student as part of it as well.”

The study hasn’t yet been published in a peer-reviewed journal and is currently a draft paper, “Does Feedback on Talk Time Increase Student Engagement? Evidence from a Randomized Controlled Trial on a Math Tutoring Platform,” so it may still be revised. It is slated to be presented at the March 2024 annual conference of the Society of Learning Analytics in Kyoto, Japan. 

In analyzing the sound files, Demszky noticed that students tended to work on their practice problems with the tutor more silently in both the control and tutor-only talk meter groups. But students started to verbalize their steps aloud once they saw the talk meter. Students were filling more of the silences.

In interviews with the researchers, students said the meter made the tutoring session feel like a game. One student said, “It’s like a competition. So if you talk more, it’s like, I think you’re better at it.” Another noted:  “When I see that it’s red, I get a little bit sad and then I keep on talking, then I see it yellow, and then I keep on talking more. Then I see it green and then I’m super happy.” 

Some students found the meter distracting.  “It can get annoying because sometimes when I’m trying to look at a question, it just appears, and then sometimes I can’t get rid of it,” one said.

Tutors had mixed reactions, too. For many, the talk meter was a helpful reminder not to be long-winded in their explanations and to ask more probing, open-ended questions. Some tutors said they felt pressured to reach a 50-50 ratio and that they were unnaturally holding back from speaking. One tutor pointed out that it’s not always desirable for a student to talk so much. When you’re introducing a new concept or the student is really lost and struggling, it may be better for the teacher to speak more. 

Surprisingly, kids didn’t just fill the air with silly talk to move the gauge. Demszky’s team analyzed the transcripts in a subset of the tutoring sessions and found that students were genuinely talking about their math work and expressing their reasoning. The use of math terms increased by 42%.

Unfortunately, there are several drawbacks to the study design. We don’t know if students’ math achievement improved from the talk meter. The problem was that students of different ages were learning different things in different grades and different countries and there was no single, standardized test to give them all. 

Another confounding factor is that students who saw the talk meter were also given extra information sessions and worksheets about the benefits of talking more. So we can’t tell from this experiment if the talk meter made the difference or if the information on the value of talking aloud would have been enough to get them to talk more.

Demszky is working on developing a talk meter app that can be used in traditional classrooms to encourage more student participation. She hopes teachers will share talk meter results with their students. “I think you could involve the students a little more: ‘It seems like some of you weren’t participating. Or it seems like my questions were very closed ended? How can we work on this together?’”

But she said she’s treading carefully because she is aware that there can be unintended consequences with measurement apps. She wants to give feedback not only on how much students are talking but also on the quality of what they are talking about. And natural language processing still has trouble with English in foreign accents and background noise. Beyond the technological hurdles, there are psychological ones too.

 “Not everyone wants a Fitbit or a tool that gives them metrics and feedback,” Demszky acknowledges.