In our last post, we introduced the concept of personas and discussed how we are using personas to create a compelling and approachable representation of our aggregated interview data with working learners. Our hope is that the personas we develop will help practitioners and program developers better understand the working learners’ experiences. We have found that personas help us illustrate who working learners are and situate our findings within the existing instructional design paradigm. In this post, we discuss our process for creating personas.
In their article ‘Thick Personas–Using Ethnographic Methods for Persona Development as a Tool for Conveying the Social Science View in Technological Design, Jacobs, Dressen and Pierson (2008)  present a process for persona development. They argue that personas help make research findings understandable and actionable. The steps they describe include:
- Identify the different persona to be developed
- Gather and analyze data (demographic information, interview, etc) to illuminate patterns of behavior, expectations and motivations
- Create a skeleton that lists bulleted characteristics (this is generally determined by the research question)
- Prioritize the skeletons based on their relevance
- Develop the skeletons into a descriptive persona which should include a name, a face (a picture or drawing), and other socio-demographic information. It should also include goals, competencies, tasks, expectations, and relationships.
- Confirm that the persona(s) accurately reflect the research data.
Guided by this work, we mapped our own process to develop personas for learners who participated in four categories of learning opportunities: 1) foundational skill development to return to a prior career, 2) foundational skill development to start a new career, 3) required for the learner to stay in a current position, and 4) to earn a degree or certificate.
The systematic process we developed helps ensure that our personae are grounded in the data. Here is the process we used to develop the persona:
- Initial High-Level Tagging. We read interview transcripts tagged according to categories that aligned with our research questions. These became ‘parent’ codes to a series of more in-depth ‘child’ codes:
- Learner characteristics
- Learning opportunity description
- Reasons, goals, feeling, emotions (regarding the learning they were doing)
- Sorting of Transcripts According to Type of Learning Opportunity. We divided learner transcripts into the three categories, which represented the different reasons learners were participating in the learning opportunity of focus in our interviews.
- To learn foundational skills
- To satisfy a job specific requirement
- To earn a degree or certificate (without identifying a career goal)
These first two steps align with step 1 of the process presented in Jacobs et al. (2008). Our research questions were: 1) Who are frontline service workers who participate in employer sponsored learning? What are their characteristics? and 2) What are the different avenues of learning opportunities? guided this work. Previous analysis led us to form categories of reasons learners participate; we needed to know who these learners were.
- Coding Interview Transcripts. We read each set of transcripts and analyzed them again, this time employing an inductive coding process whereby we identified key categories represented in data across the transcripts. Figures 1, 2, and 3 show the result of this inductive coding process – the child codes associated with each of the 3 parent codes.
This process followed what Jacobs et al. (2008) suggest is step 2. Our analysis was based on demographic information about frontline service workers and the geographical contexts in which they live and work.
Steps 3, 4, 5, and 6 from Jacobs et al. (2008) are reflected in our work described below. Rather than moving through a tightly structured process, our approach was iterative.
4. Elucidating Persona Categories. We reviewed the child codes within the parent codes for each of the three groups of transcripts. The goal of this analysis was to create groupings to find defining categories that would shape the components of a narrative persona for that set of interviewees. Jacobs et al. (2008) might consider this as the beginning of a persona skeleton.
We used a spreadsheet to log the parent code, the child code, and the code groupings. We added notes and data from the transcripts to illustrate representations of the grouped codes across the transcripts. In this way, the collective data shaped the persona, which is intended to represent a generic learner from this group. We then used the categories and crafted narrative text to represent the data across all transcripts from that grouping.
Throughout this process, we repeatedly returned to the transcripts to check the accuracy of the initial parent codes and child codes – relying on the data to support decisions made about persona categories. Rather than validating our work at the end, we continuously checked our coding to be sure our skeleton and persona development stayed true to what we saw in our data.
An example of this process can be seen in Table 1 where the middle column represents the categories identified. The categories, or mid-level codes became the characteristics that were the organizing structure of the persona skeletons. We used a simple spreadsheet to log the data tagged with each code.
Persona development as an analytical tool
At the time of writing this blog post, we have moved through this process with two of the three learning opportunity categories: 1) foundational skill development and 2) to satisfy a job requirement. As we worked through the data and analysis, we found that the foundational skills development category required further nuance. Thus we created an additional persona under the category of foundational skills. We need more data to create a persona for the third category – to earn a degree or certificate.
Through this work, we have come to appreciate the power of persona development to represent findings about our data. By following a systematic approach, we have been able to create personas that accurately reflect the lives of working learners whom we interviewed.
We have remained cognizant of the risk that personas can contribute to sweeping generalizations about a population, but by following the data we believe we are accurately reflecting the voices of the study participants. These personas are showing us that working learners should not be treated as a monolith. As those who work and support working learners already know, each learner has a unique story; we used persona development to find patterns across those stories. It is those patterns that can be used to strengthen learning opportunities. Stay tuned, we will be sharing the personas in forthcoming posts.
 Jacobs, A., Dressen, K. & Pierson, J. (2008). ‘Thick’ personas – Using ethnographic Methods for Persona Development as a Tool for Conveying the Social Science View in Technological Design. Observatorio Journal, 5, 079-097.