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Repurposing Faculty Research Skills for Student Success

Students today expect and need access to various learning modalities that fit their respective learning styles and schedules, which creates new challenges for administrations and faculty to meet without overburdening themselves.

The traditional faculty development models, which offer workshop after workshop about one or more countless topics intended to promote teaching and learning excellence, are no longer sustainable. For decades, faculty have been attending sessions on best practices, pedagogy/andragogy, learning theory, course and syllabi design, course facilitation, assessment and emerging trends like generative AI and the hyflex modality.

While it is exciting to see advancements in all these areas of teaching and learning, higher education faculty do not have the time to learn them all. Additionally, knowledge continues to march forward, and faculty need a sustainable way to keep up without sitting in endless training sessions. Faculty have limited time to devote to growing their teaching skills, and professional development units have limited resources to design and deliver this training.

This is particularly true at Kennesaw State University, where an innovative approach to faculty development and course design is being implemented to take advantage of evolving student demand for online and hybrid course sections.

The Context

Online and mixed modality scheduling has been on the rise at Kennesaw State, resulting in a hybrid campus. The popularity of online courses among graduate students at Kennesaw State University is evident, with 57% opting for fully online schedules. This preference has been consistent for years, and the proportion of students choosing fully online schedules continues to grow. Only 19% of graduate students have fully face-to-face course schedules, making it the least popular scheduling pattern for graduate students today.

Similarly, only 19% of undergraduate students have a fully face-to-face course schedule, a declining trend since 2017. Unlike graduate students, the most popular scheduling pattern for undergraduates is a mixed mode semester, with 65% of students picking this schedule. Additionally, institutional course registration patterns indicate that online and hybrid courses fill up before face-to-face course sections, leaving some learners on a waitlist hoping for a more flexible option. These patterns increasingly require all faculty to teach across all modalities and shift modalities from semester to semester. Online and hybrid course design is no longer a skill set for a small subset of faculty. Instead, these skills are necessary for all faculty, as the learning management system supports faculty to manage all learning—even in the face-to-face classroom. 

The Challenges

The university’s growth, the course registration patterns and the growing popularity of online and mixed modality student schedules have created new tensions on campus. Not only do faculty need to seamlessly move between course modalities but they also need to improve fidelity of course delivery and students want a reliable digital course shell for all their class materials. As more instructors join our teaching force to accommodate the growing student body, greater variability in student experiences is inevitable. Therefore, we need to continue to build systems that promote fidelity in course delivery beyond standard program assessments. 

As the institution grows and finding qualified contingent faculty in the local area becomes increasingly difficult, online courses allow the institution to hire instructors outside the local area, significantly expanding the potential instructor pool. However, ensuring such a large faculty can move between course modalities complicates the challenge of faculty training.

In a postpandemic planning meeting of our digital learning team, we found the list of modalities for which we needed to train faculty unmanageable. Faculty do not have the availability to attend training on hybrid 50%, hybrid 30%, hybrid 60%, hybrid-flipped, the emporium model, synchronous online and all the other possible course modalities. While it would be ideal for faculty to receive a host of training on the plethora of research-based best practices, interventions and every possible modality, it is neither sustainable for a training team or faculty to invest so much time. Our faculty training needed to be concise and transferable across modalities. In response, our team decided that we needed a sustainable solution that harnesses faculty’s research skills to continuously improve their instruction, no matter the modality in which they are teaching. To meet this need, KSU revised our digital learning team’s mission to focus on this challenge.

The Solution

We call this pragmatic approach to faculty development: faculty as learning scientist. Given that doctoral programs prepare faculty to employ scholarly processes, our new approach depends on faculty sharing a common strength: the application of research skills. To scale the faculty preparation to become learning scientists, we need them to effectively use a learning management system that automatically generates student and course data, and we need them to know how to interpret and respond to that data. With this model, they do not need to have prior training in a host of best practices they could possibly employ in their class. Instead, they must be prepared to translate those research skills to the classroom’s processes to improve student success. The faculty as learning scientist model at Kennesaw is built on five key components. 

Sustainable design

Faculty must teach their courses with fidelity by using the same course design housed in the LMS for any modality in which they teach. We call this sustainable course design.

Multimodal delivery

Faculty use the LMS content hiding or selective release features to fit the delivery mode for that semester and course section. We call this multimodal course delivery.

Learning analytics

Faculty are provided with automated learning analytics the LMS generates to make it easier review student and course statistics in real time. We use a tool we developed for D2L (Desire 2 Learn) called uHoo Analytics.

Data literacy

Faculty can interpret the analytics to diagnose challenges, predict outcomes and prescribe solutions. This interrogation of data is the work of a learning scientist.

Targeted solutions

Faculty use the knowledge they gain from interpreting the analytics to provide immediate student interventions or to seek out best practices that target the challenges evident in the course data and revise course content toward optimization for student success semester over semester.

All five components are interdependent. The first key component of our approach, sustainable course design, is based on Michael Moore’s (1997) transactional distance theory. Sustainable course design means to first design your course in the LMS for the greatest transactional distance—asynchronous online instruction. The resulting course design components can then be selectively hidden and released to fit the modality and schedule of any specific course section, which allows the course design to be used across modalities like face-to-face, hybrid, flipped, synchronous or asynchronous, as described in the second component.

The third component, uHoo Analytics is a custom D2L PowerBI dashboard of drillable reports designed for the faculty audience. Faculty are trained on how to responsibly engage with and interpret the data to plan actions that support students or address course issues, which is the fourth component. These prerequisite components all come together to provide the conditions necessary for faculty to critically examine the teaching and learning processes that take place in their classroom, reflect on the data, improve instructional methods and make data-informed modifications to the learning environment.  

By shifting to a faculty as learning scientist approach to faculty development, Kennesaw State hopes to equip faculty to transfer their research skills to sustainably become increasingly effective instructors. Other institutions with limited resources, growing student interest in digital modalities and fully scheduled faculty may consider a similar approach.

Prior to learning management systems and automated learning analytics, the process of in-course data collection and analysis was time-consuming and required faculty depth of expertise in classroom instruction and assessment. This process still did not create time to learn about research-based best practices. Advancements in automation through instructional software tools, and likely generative AI, are changing what is possible in the classroom, making it an opportune time to reimagine how we support faculty to promote student success. The faculty as learning scientist model provides a sustainable and effective approach to meet the evolving needs of students and faculty alike without overtaxing faculty time and institutional resources.


Moore, M. G. (2018). The theory of transactional distance. In Handbook of distance education (pp. 32-46). Routledge.