The purpose of this study was to benchmark the workload of online instructional designers as third-space professionals at 4-year, public institutions. Interview data regarding design team composition, courseload, responsibilities, and decision-making processes was gathered from managers of online learning units offering a “white-glove” service model for course development. Analysis revealed that these units generally consisted of 5 full-time design professionals whose primary responsibility was course development. Centralized units tended to employ more designers than decentralized units dispersed across campus. Results indicated that courseload allocations ranged from 6-9 courses per term and were strategically driven by factors such as faculty and instructional designer rapport and programmatic consistency. Implications of this study include staffing and project management guidelines for managers of online units. In addition, instructional designers might develop specialized knowledge and skills, in accessibility or multimedia production.

Key words: Staffing, instructional design, online administration, online programs, third-space professionals


Increased enrollment in online and hybrid programs across United States institutions of higher education have contributed to an unprecedented demand for instructional design expertise (National Center for Education Statistics, 2022). Instructional designers (IDs) are integral to the quality and success of online learning, as they broker technology, content, and pedagogical knowledge with faculty and subject matter experts (Pollard, 2022; Saroyan & Trigwell, 2015). Since the COVID-19 pandemic, many institutions have invested in their online infrastructure by increasing staffing levels by 17% (e.g., instructional designers, educational technologists, advisors, coaches) and centralizing online learning operations. Despite these efforts, only 10% of institutional leaders report sufficient ID capacity to meet anticipated growth in online enrollment, making “insufficient instructional design staffing […] one of online learning's most serious long-term vulnerabilities” (Garrett, Simunich, Legon, & Fredericksen, 2022, p. 20).

The gap between the high institutional demand for ID services and insufficient pool of ID professionals has raised concerns about burnout, turnover, and resilience (Kim, 2022) from a human resource management perspective. To date, there are no models that address the professional responsibilities and appropriate workload for IDs. The present study redresses this gap in research by exploring ID staffing within online learning units through an administrative lens. Drawing from theories of third space professionals (Whitchurch, 2008) and faculty workload models (Griffith & Altinay, 2020), it provides insights into the workload, responsibilities, and decision-making related to IDs working within online learning units.

Literature Review

Higher Education Online Learning Units

Since the 1960s, instructional support for higher education faculty has been primarily centralized within centers of teaching and learning (CTLs). Traditional core activities of CTLs have included facilitating workshops, consulting, and nurturing campus-wide communities of practice. Over the past two decades, the number of CTLs and the services they provide have expanded to align with the demands of online learning initiatives (Kim & Maloney, 2020). In recent years, CTLs have advanced their model to include instructional design and educational technology services by recruiting specialized personnel, such as IDs, technologists, web developers, multimedia specialists, and graphic designers in addition to faculty developers. When professionals are located within a CTL that serves the online needs of an entire campus, this integrated model is considered centralized. In a decentralized model, online learning professionals are distributed across campus, often situated within schools, academic departments, human resources, libraries, or technology units. In a decentralized landscape, CTLs become the hub or concierge that connects campus resources and stakeholders to advance institutional initiatives (Wright, Lohe, & Little, 2018). According to the CHLOE report, 24% of online learning programs were based out of a large, centralized unit; while 47% were supported by a small, decentralized unit (Garrett, Simunich, Legon, & Fredericksen, 2022).

Regardless of whether online services are centralized or decentralized, there are several models for course development that describe how faculty and IDs interface in the course design process. One model has been termed a “white glove service,” in which faculty and IDs work closely with one another to outline and develop a course (Garrett, Simunich, Legon, & Fredericksen, 2022). In this model, faculty are the subject matter experts responsible for providing the course materials (e.g., slides, syllabi) to IDs who are project managers and often build the course within the learning management system (LMS). IDs may also enlist the services of instructional technologists and multimedia specialists.

Another model is a one-on-one consultative model in which IDs coach faculty on best practices for online course design and delivery. In this model, IDs offer templates, examples, and guidance to faculty on how to effectively organize and build quality courses within the LMS. Faculty are expected to build and edit their courses, while IDs provide ancillary support.

A third option is a cohort model in which faculty enroll in a facilitated workshop with interdisciplinary colleagues and a facilitator who is often an ID. Faculty meet in regular sessions to guide their course design process. Topics might include course outcomes, authentic assessments, and instructional alignment. Cohort members receive guidance on the features of the LMS and are responsible for creating their courses. Self-assessment or peer-assessment may be incorporated to enhance course design.

Instructional Designers in Online Education

Instructional designers are central to online learning in higher education. They are responsible for conducting “the analysis of learning and performance problems, and the design, development, implementation, evaluation and management of instructional and non-instructional processes and resources intended to improve learning and performance” (Reiser, 2001, p. 53). IDs oversee the complex course development process, serving multiple stakeholders simultaneously, including faculty members, subject matter experts, internal and external multimedia specialists, instructional technologists, and ultimately learners (Pollard & Kumar, 2022). Essentially, IDs are “the glue, holding everything together as project manager, as well as providing support and assistance throughout the [design] process – brokering the services of others as needed” (Fyle et al., 2012, p. 62).

The ID role is multi-faceted and poorly understood (Mancilla & Frey, 2021). Regardless of the instructional modality (i.e., fully online, hybrid, flipped, residential), IDs work alongside faculty and subject matter experts to develop teaching materials, support educational technologies, assure course quality, and manage collaborations (Dykstra, 2020). In addition, IDs typically provide ongoing technical and pedagogical coaching for faculty once the course is launched (Anderson et. al, 2019). IDs also serve as faculty developers by offering professional development opportunities (i.e., workshops, individual consultations, or certification programs) on topics such as emerging technologies and instructional strategies.

Overarching themes in the limited design literature are the demanding deadlines, workload pressures, and limited resources (Chen & Carliner, 2020; Dykstra, 2020). Although the number of IDs working in higher education settings increased by 20% during the COVID-19 pandemic, ID staffing still fell short of the demand needed to serve the dramatic growth of online and emergency remote learners. According to the most recent CHLOE Report, as of Fall 2019, the average number of full-time IDs at public, four-year institutions was four. The range of IDs housed within centralized units was between 1-100 with a median of two; decentralized schools and departments had a median of three full-time IDs on staff (Garrett, Simunich, Legon, & Fredericksen, 2022). Few institutions outsourced ID services, with less than 10% using external designers. Given the shortage of IDs and their expanding workloads, it is critical for online administrators to develop a staffing model that is scalable and sustainable.

Conceptual Framework

This study is theoretically grounded in Whitchurch’s (2008) concept of third space professionals, referring to staff in higher education who work across academic and professional (e.g., nonacademic) domains. Third space professionals cross departmental boundaries and functions by working on multidisciplinary teams, such as those found in online learning units (White, White, Borthwick, 2021). These specialists are known by various titles, including instructional or learning designers and instructional or learning technologists, who are classified as staff, but also serve as educators (White & White, 2016).

With the continued growth of online learning, design professionals are no longer part of the invisible workforce (Rhoades, 2010). It is important that institutions utilize these uniquely positioned professionals and leverage their contributions in current and future initiatives (Whitchurch, 2008). Whitchurch’s framework is appropriate for this examination of online learning units, as research has indicated a need for administrators to better understand and balance the workload of third space professionals (Stoltenkamp, 2017).

This research further draws on institutional workload models in higher education, which have typically focused on faculty. Faculty responsibilities have been categorized as the “three pillars” of teaching, service, and research (Ward, 2003). Changing instructional environments and institutional expectations have resulted in recent efforts to expand the faculty workload model (Griffith & Altinay, 2020), making it more dynamic. Despite research indicating that ID workloads and deadlines impact job performance (Pollard & Kumar, 2022), there is no defined institutional workload model for IDs. The present study explores ID workload allocations from a managerial perspective.


The present study is a descriptive, mixed methods study that utilized interviews to solicit instructional design staffing information from online administrators.

Research Questions

The research study aimed to address the following questions within the context of higher education:

  1. What factors are considered when staffing an online learning unit?
  2. What is the workload for IDs in an online learning unit?


Participants were selected using a combination of purposive and snowball sampling techniques to inform the research questions (Robinson, 2014). All participants had to meet the minimum criteria of working at a 4-year, public institution and managing a team of IDs within a full-service online unit (e.g., white glove service). Researchers first solicited participation from online leaders within their professional networks, organizations, and list servs. Participants identified additional colleagues with similar roles as potential interviewees. In total 20 online administrators agreed to participate in the study representing institutions across the United States. After screening, 13 administrators fulfilled the criteria of the study. Participant titles included Director of eLearning, Director of Online Learning, Director of Digital Learning, and Director of Learning Innovation, among others. Their experience overseeing an online learning unit ranged from 1-12 years, with an average of 6 years.


Researchers conducted 1-hour virtual interviews with qualified administrators guided by a semi-structured interview protocol (Corbin & Strauss, 2008). The protocol contained a total of 36 questions targeting institutional and participant demographics, instructional design team composition, ID workload, and course development processes. Participation was voluntary and interviewees were not compensated. Interviews took place in the Zoom video conferencing application and were not recorded for confidentiality purposes. Researchers took detailed interview notes and followed up with participants via email for additional information as needed.

Data Analysis

Data collected from interviewees included quantitative and qualitative measures. All data identifiers, including participant and institutional names, were removed from the data. Participants were identified by a code number. Quantitative data was entered into Microsoft Excel (Microsoft 365) and analyzed using the Analysis ToolPak to calculate frequency distributions and generate descriptive statistics and visualizations. Qualitative text responses were analyzed using a hybrid approach combining inductive and deductive codes (Saldaña, 2021). Pre-structured codes were drawn from the relevant ID literature and used to create a codebook (i.e., ID workload, special projects, faculty experience). Deductive codes emerged from a line-by-line content analysis conducted by each researcher. Results were independently recorded in the codebook. Through an iterative process, both researchers collapsed codes into categories using the constant comparison method (Glasser & Strauss,1967) to reconcile discrepancies and generate themes.

Results & Discussion

Results include a profile of participating institutions followed by an analysis of data related to each research question.

Institutional Profile

Participants represented 4-year, public institutions from across the US. Eight institutions were R1 research institutions or part of an R1 system (American Council on Education, 2023). While most participants did not have data on online enrollments, overall enrollments ranged from approximately 12,000 to 88,000 students. The majority of participants worked within single institutions (62%), followed by large institutional systems (23%), and online units within schools or departments (15%). All participants reported to an academic unit on campus, such as a provost, vice provost, dean, or director. Most participants worked in centralized offices (62%), which could indicate post-pandemic support for centralized online initiatives by academic leadership and institution-wide investment capital (Kim, 2020). Also trending among respondents were hybrid or remote work arrangements for online units (85%), which originated during the COVID-19 era. Working with an online program manager (OPM) for learning design was reported by four online managers (31%). Two respondents used an OPM for course development and two used the OPM for marketing and enrollment services. This marketing trend aligns with the focus of most OPMs (Kim, 2019).

Research Question 1: What factors are considered when staffing an online learning unit within higher education?

Several factors were considered when staffing an online learning unit to ensure efficiency. Similar to findings reported in CHLOE, the white glove service (1:1) approach to course development was the most prevalent model across institutions (85%). Few institutions utilized a faculty cohort model, and none reported the use of a consultative model. Part of the white glove service includes course building by a design expert. This was reflected in the data, as 46% of institutions employed IDs, ID assistants, or ITs to author courses in the LMS. In some instances (38%), faculty and IDs shared the responsibility of course building. Only 15% of institutions reported faculty as the primary course builders, which is consistent with the faculty cohort model of course development. While the role of faculty as course builders does not align with the traditional white glove service, this could indicate an increase in faculty technology knowledge and skills following the COVID-19 pandemic.

Most participants reported that their online unit built both graduate and undergraduate courses (69%). There was an even split between units that developed undergraduate (15%) and those that developed both graduate and continuing education programs (15%). Team members contributing to these courses varied across institutions. All units were staffed with at least one IT and an administrator (e.g., director), plus multimedia support through external personnel (e.g., videographer, multimedia specialist). Most teams had several student workers and/or interns, some of whom specialized in digital accessibility. One team employed copyeditors and one had a robust internal multimedia team of six members, including graphic illustrators.

Managers of the online team performed a myriad of job responsibilities. The majority of managers conducted pedagogical and technological training within their programs and institutions (e.g., artificial intelligence). Over half of the participants reported either chairing or serving on academic committees (e.g., Digital Learning Academic Council, Educational Excellence Committee). Approximately 46% of participants were responsible for operations management, including budgeting, hiring, and onboarding new staff. In addition, 31% of online managers were the primary liaisons representing their units among academic programs and departments and leading the strategic planning initiatives. Few respondents mentioned their role in OPM management, grant-writing, and ID course development work. Most managers indicated that their teams were fully staffed (70%).

While research has indicated that the average number of IDs at four-year, public institutions totaled four (Garrett, Simunich, Legon, & Fredericksen, 2022), data from the present study revealed a range of 2-14 IDs across all 13 institutions, with an average of five full-time IDs per unit. There was little variation among respondents regarding the experience level of the IDs on their teams. In most cases, teams consisted of at least one junior ID (0-3 years), one mid-level ID (4-7 years), and two senior IDs (8 years or beyond). Two institutions employed part-time IDs. Only one institution outsourced course development work to external IDs, which is consistent with national trends on contracted design services. Contrary to the CHLOE Report, the average number of IDs was greater within centralized (n=6) than decentralized units (n=4) operating within schools and/or departments (Garrett, Simunich, Legon, & Fredericksen, 2022). Since CTLs oversee a broad spectrum of programs, they tend to house more teaching, learning, and support professionals than individual schools or departments. This finding could potentially reflect a post-pandemic institutional emphasis on streamlining online course quality and consistency through a centralized service provider (Kim, 2020).

Designer course assignments were impacted by several factors (Figure 1). Managers of online units largely reported strategically pairing IDs with faculty subject matter experts. Participants routinely dedicated an ID to a specific department or program. This approach allowed IDs to develop an overall curriculum perspective that facilitated continuity in course development and maintenance. Keeping a consistent faculty – ID dyad for consequent terms, regardless of academic discipline, was another major factor guiding course decisions. This could be because over time, IDs develop cumulative knowledge about the courses and programs (e.g., policies, course components) that enhances efficiency and benefits faculty and students. The faculty - ID rapport considers the personalities of both the ID and the faculty member, taking into consideration patience, responsiveness, flexibility, and communication styles.

Senior or seasoned IDs were frequently assigned new or complex course developments. As skilled project managers, they are better suited for multimedia asset management and communication across multiple stakeholders. Some managers used a database tool to implement a rating system documenting the level of complexity of courses in their portfolios. Respondents strived to balance the workload of IDs by considering their current courseload and overall availability. In addition to the experience of the ID, the online teaching experience of faculty also impacted course assignments. Essentially, more experienced IDs were partnered with less experienced faculty. In some cases, IDs expressed a preference for working with a specific faculty member or subject matter based on their interests or specialization (e.g., STEM courses). Some managers accounted for faculty requests for working with a particular ID. One respondent mentioned that the ID focus was referenced in their job description.

Figure 1

Table depicting the frequency distribution of factors impacting courseload decisions

While it would be beneficial to analyze the data according to institutional type (e.g., single institutions, institutional systems, and schools/departments), most participants hailed from single institutions. Thus, unbalanced data did not support disaggregation. However, the analysis did reveal preliminary differences among centralized and decentralized online units. The managers of decentralized units (80%) tended to consider ID capacity in course assignments, while those of centralized units emphasized faculty-ID consistency (63%). This may be attributed to decentralized units having access to fewer IDs.

Research Question 2: What is the workload for instructional designers (ID) in an online learning unit within higher education?

Effective workload management often contributes to employee satisfaction and longevity, plus team productivity and quality performance. Workload considerations for IDs has entailed course development and additional responsibilities that are typically determined by the institutional culture in which they work (Pollard & Kumar, 2022). According to the online managers in this study, IDs dedicated the majority of their time to course development (66%). A diverse range of tasks and responsibilities constituted their remaining workloads (Figure 2), with faculty development being the most prominent. IDs participated in various faculty development initiatives, such as facilitating technology training, providing pedagogical consultation, and designing job aids. IDs were also either appointed by their manager or volunteered to serve on university committees including the diversity, equity, and inclusion committee, design, media, and technology committee, and open educational resources committee.

Furthermore, IDs assisted with ongoing LMS and technical troubleshooting on behalf of faculty. Some IDs utilized a scheduling application or ticketing system to manage technical support. Given the close collaboration between faculty and IDs throughout the white glove service, it is logical that faculty continue to contact their IDs for guidance. Additionally, IDs worked on diversified special projects ranging from faculty-facing resources like e-bulletins and tips of the month to design-team programs such as book clubs or networking events. Participating in both internal and external professional development opportunities was also a priority to stay current in pedagogical and technological advancements. Managers mentioned limited responsibilities involving remediating accessibility barriers, mentoring junior designers, performing quality reviews, refining course development processes, and conducting scholarly research.

Figure 2

Pie chart depicting the distribution of additional ID responsibilities

The number of online courses offered per academic term ranged from 0 – 1000, with an average of 230 graduate and undergraduate courses per institution. ID workload responsibilities for these courses depended on their years of experience, with senior IDs taking on more courses. Overall, junior IDs managed approximately 6 courses per term, mid-level IDs managed 8 courses per term, and senior IDs managed 9 courses per term. Across institutions, IDs worked on a combination of new and revision courses. For most institutions, both the new course development cycle and course revision cycle were 1 semester or 15 weeks prior to course launch.


This study benchmarks the workload of instructional designers within online learning units at 4-year, public institutions from an administrative perspective. Using an interview protocol, 13 ID managers contributed information related to their unique design team composition, size, courseload, and decision-making processes. Findings indicated that on average, online learning units employed 5 full-time IDs balanced across junior, mid-level, and senior positions. Centralized units tended to employ more IDs than decentralized units operating within schools or departments. Course development was the primary responsibility of all IDs. Online managers reported a courseload of 6-9 courses per term based on the ID’s level of experience, with senior IDs overseeing more rigorous developments. Decisions regarding course assignments were strategic. The top five practices included 1) dedicating an ID to a single department/program, 2) fostering ongoing faculty-ID rapport, 3) maintaining faculty-ID consistency across semesters, 4) considering ID capacity, and 5) distributing complex course developments among experienced IDs.

Due to the small sample size of this study, caution should be exercised when interpreting the findings. Another limitation was managers who did not have access to enrollment numbers or statistics from other units, resulting in gaps in the dataset. While the focus of this study was 4-year, public institutions, future researchers might explore online staffing in private institutions, community colleges, or institutions that offer a consultative rather than white glove service model.

Implications for practice apply to both managers of online units and IDs. Most participants reported that their online units were fully staffed resulting in a manageable courseload for IDs. Maintaining a full and diversified staff is critical in achieving a balanced workload, as turnover within online units often results in the redistribution of courses and ancillary duties across IDs. Managers can engage in ongoing efforts to make the workload of IDs transparent. For instance, project management systems that display the breadth of ID responsibilities can ensure equitable dissemination of courses and projects across a design team and allow visibility for all team members. Managers might also consider developing a productivity formula, such as a ranking system, to calculate the complexity of course developments. In addition, managers can routinely re-evaluate workload by gathering input through staff and individualized ID status meetings. These meetings allow IDs to voice concerns and have the potential to increase their autonomy of their workloads, which can lead to greater job satisfaction. As third-space professionals, it was evident that IDs performed a diverse range of important functions within their units that exceeded course development. Managers might leverage IDs’ specialized interests and talents to benefit their institutions. Finally, as findings highlighted the importance of the senior ID role, managers may consider implementing mentoring programs among junior and senior IDs to cultivate internal expertise and knowledge transfer.

This study also has implications for IDs as they continue to be a mission-critical resource in higher education (Rizhaupt, Kumar, & Martin, 2021). To create a gratifying work environment, IDs might utilize an individualized project management system to monitor their workloads and share with managers. As designers gain experience, they may consider specializing their knowledge and skills within a programmatic discipline, such as clinical education or STEM education. Within the study, some IDs enhanced their units by specializing in digital accessibility or multimedia development.

As third-space professionals IDs straddle a role between staff and faculty. They comprise an invisible workforce responsible for developing thousands of online, hybrid, and residential courses that enhance the learning experience for students, yet their workload is often demanding and poorly understood. Furthermore, the rapid growth of online learning, which was compounded by the COVID-19 pandemic, has resulted in a lack of established practices, models, and metrics regarding the ID workload. Considering the growing demand for design expertise, results from this study may serve as a preliminary benchmark for managers of online units to retain ID talent and promote job satisfaction.


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