This session presents qualitative research analyzing job descriptions for instructional designers across a diverse set of higher education institutions. By exploring the evolving nature of instructional design in higher education, participants will learn how to craft effective job descriptions that attract top talent and align with industry and institutional expectations.

Introduction In an era where technology rapidly transforms the landscape of learning, the role of instructional designers has become increasingly vital in shaping effective educational experiences. In the following conference session, we will describe a research exploration of nuances in instructional design roles across diverse higher education institutions. Our session aims to showcase the intricacies of job descriptions within this field, offering insights and recommendations that can inform and enhance the practices of instructional designers and educational leaders alike. This session is based on a research study conducted by the presenters. This qualitative study aimed to examine job descriptions for instructional designer roles at higher education institutions by answering the following research question: Research Question: What trends exist in job descriptions for instructional designers at different higher education institutions in the United States? - What are the most common duties/responsibilities/competencies cited? What are the least common? - How does the type of institution impact the job description? Background The field of instructional design within higher education institutions is multifaceted, encompassing a wide array of responsibilities and skill sets. In the past decade, research has ensued in the exploration of specific competencies in which a successful instructional designer should obtain (Martin et al, 2022), but these skill sets can be vastly different depending on the industry of focus. In higher education, for example, competencies often cater toward curriculum design, as well as faculty development, rather than specific skill sets in authoring tools (Klein and Kelly, 2018).To better understand the current priorities of institutions, a thorough examination of job descriptions from various institutions will reveal both commonalities and unique aspects that reflect the evolving nature of the profession. Effective job descriptions not only attract the right talent for the role, but also set the stage for performance management and professional development of the incoming designer. Clear descriptions enable both supervisors and candidates to understand the expectations of the role, particularly given the varying contexts of an ID at a higher education institution (Patterson et al, 2008). Whether the instructional designer sits within a functional department or a center for teaching and learning will impact the way in which the role is designed and tailored to the unit’s needs. Recent surveys of instructional designers across industries noted 13.7% of individuals working as IDs do so in the higher education field. As of May 2024, there are over 200 job openings for instructional designers on HigherEdJobs.com, a common jobs board within the industry. Further, growth of this role across industries has increased substantially in the last decade and is estimated to increase by an additional 15,000 jobs in the next decade (Zippia, 2024). This growing pressure of a competitive job market suggests universities and colleges should prioritize the way in which they market and communicate instructional design roles to ensure they remain aligned to industry standards, as well as to the specific job functions required within their context. Methodology Data Collection Given the focus and analysis of the language utilized in instructional design job descriptions, we have chosen a discourse analysis for this qualitative study. Discourse analysis focuses on the use of language and aims to understand the norms and preferences within a certain context or setting (Brown & Yule, 1983). In this qualitative study, we will use purposeful sampling to select sites that “can best help us understand our phenomenon” and to which we had access (Creswell & Guetterman, 2019; Plano Clark & Creswell, 2014). To recruit participants, we will share a Google Form request with participants in our ID Research Collaborative, send an email to colleagues, and post the link on social media. To preserve the confidentiality of our participants, we will not ask for institution names but rather for publicly accessible metadata, including institution type, type, size, number of students serviced, and a PDF copy or a link to a job description. Additionally, participants will be asked the following questions to help us better correlate and theme the types of institutions, with how they define instructional design, and their official instructional design job descriptions. - How do you define ID? - Are your job titles ID? - If not, what are they? Data Analysis To analyze the instructional design job descriptions, we will first add the metadata provided by participants to the job descriptions and create one PDF of this information. These PDFs will be renamed with the labels of “Job Description #1, Job Description #2, Job Description #3, … Job Description #6” and will be shared to a Google Drive folder only accessible between both researchers. After this process, we will upload the job descriptions to ChatGPT 4.0 and ask the AI Tool to identify the most commonly used job duties and the least commonly used. We will also ask the tool to identify any trends among the job descriptions. We will use this information as an initial look into the data and to help guide our manual analysis of the job descriptions. To ensure validity and reliability, both researchers will conduct an analysis of the job descriptions individually and then meet to compare notes and findings. First, we will upload the job descriptions into NVivo and organize the interview data into cases containing each of the job descriptions. We will then conduct two cycles of coding. Due to the nature of discourse analysis and the focus on the language used in the job description, the first cycle of coding will consist of line-by-line coding analysis using in vivo codes. The main goal of utilizing in vivo codes is to “preserve the participants’ meanings of their views and actions in the coding itself” (Saldaña, 2021, p. 141). Once the job descriptions are coded, we will reorder the codes in alphabetical order and begin our second round of coding. Pattern coding is a way to group initial codes to condense the information into more meaningful information we can use to find patterns and commonalities among the job descriptions (Saldaña, 2021, p. 322-323). Our last step will be to interpret these condensed patterns to propose a common job description for instructional designers. Session Format In this educational session, we plan to present our research findings and facilitate a discussion to compare what we found in our study with the participants’ own experiences. We will loosely follow the outline below: - Introductions - Research Presentation - Group Activity - Wrap-Up References: Brown, G., & Yule, G. (1983). Discourse analysis. Cambridge University Press. Creswell, J. W., & Guetterman, T. C. (2019). Educational research: Planning, conducting, and evaluating quantitative and qualitative research. Pearson. Instructional designer trends. (2024, Mar. 27). Zippia. https://www.zippia.com/instructional-designer-jobs/trends/ Klein, J. D., & Kelly, W. Q. (2018). Competencies for instructional designers: A view from employers. Performance Improvement Quarterly, 31(3), 225–247. https://doi.org/10.1002/piq.21257 Martin, F., Chen, Y., Oyarzun, B., & Lee, M. (2022). Learning and development roles and competency domains in higher education: A content analysis of job announcements. Journal of Computing in Higher Education, 34(2), 297–320. Plano Clark, V. L., & Creswell, J. W. (2014). Understanding research: A consumer’s guide. Pearson Higher Ed. Saldaña, J. (2021). The coding manual for qualitative researchers. SAGE Publishing. Patterson F, Ferguson E, & Thomas S. (2008). Using job analysis to identify core and specific competencies: implications for selection and recruitment. Medical Education, 42(12), 1195–1204. https://doi-org.ezproxy.queens.edu/10.1111/j.1365-2923.2008.03174.x