Stanford Moves Education Forward with Deepgram

Educational research is important for understanding how to enhance educational outcomes and experiences for everyone. To do this, quantitative and qualitative analyses can help researchers determine what goes into effective teaching. Unfortunately, quantitative and qualitative studies in classrooms are also notoriously time intensive and laborious. And, of course, the bigger the study, the longer it takes. Or, does it? With Deepgram's help, researchers at Stanford's Graduate School of Education have embraced natural language processing and machine learning to elegantly speed up research and make it more useful for teachers and students.

The impact of MOOCs

In recent years, teachers have increasingly turned to MOOCs, massive online open courses, as a way to "take responsibility for their own professional learning" and learn about new tools and techniques. However, it's difficult to quantify the impact of these professional development resources on teachers and their students. Are MOOCs actually useful for teacher education? To answer this question, a team of researchers at Stanford is asking K-12 science teachers in a professional development MOOC to audio-record their own teaching for a couple of months. This data will enable the research team to ask:

• When do teachers have opportunities to use tools and techniques presented in the MOOC?

• How do they fit these tools and techniques into their own repertoires of professional practices?

The answers to such questions could indicate what sort of impact MOOCs do (and don't) have as a medium for teacher education, and can help improve the design of future MOOCs to make them more useful for teachers.

An automated solution to a tedious task

Attempting to answer those questions presents a time-consuming obstacle: transcribing hundreds of hours of classroom audio for analysis. Traditionally this task would be passed on to students for manual transcription or outsourced to a transcription service. However, this process has its disadvantages. As Quentin Sedlacek, a PhD student and one of the principal investigators on the study, explains:

However, advances in computational methods and machine learning have the potential to help resolve this conundrum. Aaron Alvero, a PhD student and member of the research team, has experience using such methods to quickly uncover hidden patterns in large corpora of educational data. To use such methods for this project, however, the team first needed a way to rapidly convert their audio-recordings into reliable transcriptions of text. Enter Klint Kanopka, another PhD student and research team member with expertise in natural language processing and computational text analysis. Kanopka believed he could find an automated solution to what he describes as the "miserable task" of transcribing and analyzing months' worth of audio recordings. After evaluating a number of automatic speech recognition services, Kanopka said "Deepgram became a clear winner for what we wanted to do". Given the sheer quantity of recordings his team hopes to collect, they needed a consistently accurate tool that can speed up the process of transcription.

Automating processes and saving money

Kanopka found that using Deepgram sped up research in a number of ways. Contrasted with Google, which was "the hardest of any of them to actually get up and running" and Nuance Dragon which was tied to a single machine, he said "Deepgram is really easy to use." Using both Google Cloud and PocketSphinx required him to write programs to interface with the APIs, but Deepgram was functioning right out of the box. That enables the team the freedom to interact with Deepgram's in-browser and API options without hassle and in any location.

Whereas CMU Sphinx or Nuance Dragon took up to several hours for each file to transcribe on his local machine, Deepgram's cloud platform allows him to batch upload multiple files and be done in under a minute.

By choosing Deepgram, Kanopka not only significantly shortens transcription time but also reduces costs.

As researchers at one of the top education schools in the country, Kanopka and his team have the potential to play a big role in how teachers go about securing the tools to improve their student's educations. With Deepgram, they can automate research processes and maintain focus on their goal of pushing education forward.

If you have any feedback about this post, or anything else around Deepgram, we'd love to hear from you. Please let us know in our GitHub discussions .