February 22, 2024
TeachFX, an ed-tech company aiming to enhance classroom interaction, partnered with Refuel to evolve their product by introducing ML features for detecting key educational moments in classroom sessions.
Refuel enabled TeachFX to quickly generate accurate training datasets, achieving over 92% agreement with expert annotators, and significantly reduced the time required for feature development from months to just two weeks.
This collaboration supports TeachFX's mission to improve education outcomes by accelerating the development of their ML features, underscoring the potential of LLMs for data labeling efficiency and quality.
TeachFX is a Bay Area-based, ed-tech company with a mission to promote more meaningful and equitable classroom dialogue by superpowering teachers’ work — using technology to provide educators with regular, automated feedback on their practice.
TeachFX is in the process of evolving their product offering — going from a detailed review generated after every single classroom session to providing teachers an interactive experience that enables them to dive deep into critical learning moments from the classroom and connect them across other sessions they’ve had with students.
The TeachFX team identified several product features they’d like to build that would allow teachers to gain an in-depth understanding of each classroom session. For example, detecting when a teacher created an “opportunity to respond” - a learning moment that facilitated responses from students, or when there was a moment of “teacher feedback” - a moment when a teacher provided feedback to a student.
Detecting such moments in a classroom session requires an extremely nuanced understanding of teacher-student interactions, which requires capable ML models, and in-turn, high-quality training datasets.
TeachFX was looking for a solution that could help them consistently create high-quality training datasets and fine-tune LLMs for their ML features, and so they turned to Refuel.
The TeachFX team used Refuel to build two new ML features — detecting “opportunities to respond” and “teacher feedback”. For each of them, they followed a consistent process:
As Berk Coker, CTO of TeachFX says:
”In a world where we are fine-tuning LLMs, all we need to do is find some good examples. We have immense unlabeled data, and Refuel’s product can go find many other examples for us.
When the examples we’re looking for are not common, finding them and hand labeling takes a lot of time. So just being able to use LLMs to find the data, train a model and then boom, we have the insight. So it is a much, much faster iteration.”
“Refuel drastically reduces time to market for our ML features. Our team used to spend so much time on getting our training datasets in order for every new feature, but with Refuel, we can dramatically accelerate our roadmap.”
Berk Coker, CTO at TeachFX
The TeachFX team has an aggressive product and ML roadmap to deliver to on their mission for teachers and improving education outcomes. Refuel will be a key partner for the TeachFX engineering team to improve data quality and fine-tune custom LLMs to support TeachFX’s inspiring mission.
If you’re interested in how LLMs can help speed up data labeling and improve data quality, sign up here to get a demo of Refuel
