Dr. Katherine Miller addressed a critical education challenge in her dissertation: preparing teachers to teach data literacy effectively.
As students navigate an increasingly data-driven world, the ability to understand and interpret data has become essential. However, without established frameworks for instruction, many educators struggle to develop their students’ data literacy skills.
Through her research using Delve to organize her qualitative analysis, Dr. Miller developed a comprehensive framework for content and pedagogical knowledge that provides clear guidance on both what to teach and how to teach data literacy effectively.
We would like to thank Dr. Christina Silver and the University of Surrey’s CAQDAS Networking project for conducting this great webinar.
Challenge: Analyzing 18 hours of workshop and interview recordings to complete a dissertation
Dr. Miller set out to help teachers learn how to teach data literacy. But first, she needed to understand how teachers themselves make sense of data literacy — what they know, how they plan to teach it, and the challenges they face.
Dr. Miller collaborated with four high school science teachers implementing semester-long STEM bioinformatics workshops with their high school students, gathering over 19 hours of recordings and interviews.
But then came the challenge of managing and making sense of all that data.
Dr. Miller turned to Delve’s qualitative tool to tackle this challenge. With the platform’s flexible coding system, she systematically organized her extensive data into structured, accessible insights.
Solution: Use Delve qualitative tool to organize qualitative codes with ease
Delve’s qualitative analysis platform provided three key capabilities that transformed her research process:
1. Iterative code development
Dr. Miller used Delve as her central hub for organizing complex data. She started with deductive codes and seamlessly added inductive codes as patterns emerged. All transcripts lived within the platform and linked directly to relevant codes, making it simple to trace connections between teaching strategies, student misconceptions, and content knowledge — something impossible with traditional spreadsheets.
The platform’s intuitive structure helped Dr. Miller identify emerging themes in how teachers articulated their knowledge and developed new strategies, with easy organization of codes and subcodes as her analysis evolved.
(Delve) allowed me to sort my codes to combine them into these bigger ideas or to tease them apart as I was going through this process.
2. Identify key codes at a glance
Delve automatically generates snippet counts for each code, showing exactly how many quotes are tagged with each theme. This feature enabled Dr. Miller to quickly assess the comparative significance of different codes. High snippet counts indicated themes worth breaking into subcodes, while single-snippet codes could be combined or removed, keeping her analysis focused and meaningful.
(Delve) gives you the numbers of how many snippets you have coded into each of those buckets and so I used that to look for the relative significance of each code.
3. Seamless and easy to use interface
The clean, intuitive interface made moving between transcripts, codes, and snippets effortless. Dr. Miller could easily navigate between transcripts in the right panel, explore the codebook, and dive into specific snippets. Within each code, she had instant access to the full snippet list and count, streamlining her analysis workflow.
I really liked the interface of Delve because it allowed for that easy drag and drop of coding and reorganizing things. I’m a fairly visual learner so this process made my thinking a lot easier.
The impact: Dr. Miller successfully defended her dissertation and contributed to data literacy research
Delve’s streamlined analysis process enabled Dr. Miller to successfully defend her dissertation and contribute valuable insights to the field of data literacy education. Delve helped her identify three key findings that might have been missed using traditional spreadsheet methods:
- Dr. Miller discovered that teachers’ development of “big ideas” for data literacy was crucial for surfacing their tacit knowledge.
- She found that collaborative knowledge building was essential for developing pedagogical content knowledge around data literacy.
- Most importantly, she identified that teachers needed specific language and frameworks to articulate the data teaching knowledge they already possessed.
What started as overwhelming transcripts and complex data became a successful dissertation defense and meaningful contribution to educational research — all with the help of Delve’s intuitive qualitative analysis platform.
If you’re looking for a streamlined way to manage your qualitative analysis, try a free trial of Delve.
Frequently asked questions
How do you manage 18 hours of interview and workshop recordings for a dissertation? The volume isn’t the hardest part. Making sense of it all is. Dr. Miller’s approach was to upload all transcripts into a single platform where she could code across them consistently, trace connections between themes, and track how her codebook evolved over time. Having everything in one place meant she wasn’t toggling between files or losing context mid-analysis. Delve’s qualitative coding platform keeps all your transcripts, codes, and memos connected in one workspace, which becomes increasingly important the larger your dataset gets.
What does it mean to use both deductive and inductive coding in the same project? Deductive coding means you start with categories drawn from theory, prior research, or your research questions and apply them to the data. Inductive coding means new codes develop from what you actually find as you work through the transcripts. Dr. Miller started with deductive codes and added inductive ones as patterns emerged, which is how most rigorous analysis actually develops in practice. Most qualitative researchers end up working with some combination of both approaches, and the balance depends on how much existing theory applies to what you’re studying.
What are snippet counts and why did they matter to Dr. Miller’s analysis? Snippet counts show how many coded excerpts are tagged to each code in your project. Dr. Miller used them to assess the relative weight of different codes. A code with one excerpt might need to be merged into a broader category. A code with dozens might be significant enough to break into subcodes. That kind of signal is easy to miss when you’re managing codes in a spreadsheet, but Delve generates snippet counts automatically as you code, so you can see at a glance where your data is clustering.
Is Delve used in other academic and education research, or mainly for dissertations? Delve is used across the full range of academic research, from dissertation projects to peer-reviewed published studies. For a look at how it holds up in funded, published research, Dr. Jennifer Garcia Ramos at LSU used Delve to handle a multi-source dataset including student interviews, surveys, and AI disclosure forms for NSF-funded research published in Frontiers in Education.
How do I cite qualitative coding software in my dissertation methodology? Most dissertations include a tools section within the methods chapter describing the software used for analysis. You’d name the platform, note its key capabilities as they relate to your methodology, and explain how it supported your analytical process. Dr. Garcia Ramos cited Delve directly in the methodology sections of her published papers. Delve’s citation format is: Delve, Ho, L., & Limpaecher, A., followed by the relevant year and URL. If you’re still working out your methodology, the six-phase thematic analysis process covers the full analytical framework from coding through to write-up.
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