Faculty Support, Analytics, Financial Models and Program Development

Faculty Support

Supporting faculty in online environments is critical to the success of learners—just like it is in face-to-face and blended environments.  The below section is drawn from Quality in Online Learning, Instructional Design and Professional Development (S. Thackaberry, 2015).

Responding to the needs of online teachers through effective professional development programs in higher education has a strong association with the quality of online programs (Baran & Correia, 2014)[i].  A study of the results of implementing training for faculty in the Universal Design for Learning (UDL) – one of the set of tools analyzed here – indicated that students’ perceptions changed about how their instructors share content, engaged with students, and enabled students to demonstrate their learning as a result of their intervention (Schelly, Davies & Spooner, 2011)[ii].  Research on the impact of professional development on student outcomes has largely indicated that professional development has a positive effect on student outcomes (Stes, Gijbels, & Petegem, 2012)[iii].  The wide variety in breadth, depth, methodology and engagement in professional development programs for faculty may contribute to the difficulty in determining which types of professional development programs are most effective.

Penn State World Campus, which originated in 1998, initiated the Faculty Development Unity in 2008.  It was designed to positively impact student retention and success, as well as student and faculty satisfaction and other measures (Ragan, Bigarel, Kennan & Dillion, 2012).  Their faculty professional development program was led by the Penn State Online Coordinating Council which delineated three competency categories and twenty-seven discrete competencies.  Successful completion of faculty development program leads to a Certificate for Online Teaching.  The authors of the article noted the importance of institutional culture and climate:  “the institutional context greatly informs and shapes the speed, direction, and effectiveness of implementation” (Ragan et al., 2012, p. 82)[iv].

The Center for Distributed Learning at the University of Central Florida leads professional development programs for faculty teaching online courses.  Their program consists of a minimum of 80 hours of professional development training that encompasses a mainly online model, with in-person seminars during weeks 1, 5, 8 and 10 of the 10-week class (Center for Distributed Learning University of Central Florida, 2015)[v].

Valencia Community College utilizes a series of stackable courses for faculty professional development.  Their base level is an Introduction to Online Teaching for faculty who are exploring online learning, but does not qualify faculty to teach online.  Subsequent levels include a Boot Camp for Online Instruction and a Digital Professor Certification Program (Office of Faculty Development Valencia Community College, n.d.)[vi].

Santa Fe College – an Aspen Prize Winner for Community College Excellence in 2015 – has a Center for Teaching, Technology and Training.  Their professional training (Center for Teaching, Technology and Training Santa Fe College, n.d.)[vii].  They have a professional development program that enables faculty to earn a Certificate for Online Teaching and Learning.  Faculty who engage in this program must have already taken one of two existing quality online learning courses:  Applying the Quality Matters Rubric (the flagship course for the Quality Matters program,) or Canvas:  The Basis or Canvas:  Beyond the Basics.  The certificate program includes an exploration of best practices, strategies to increase social presence online and improve communication as well as how to employ active learning strategies (Center for Teaching, Technology and Training Santa Fe College, n.d.).

Data and Analytics

Just as “Big Data” is being utilized in consumer applications and towards the implementation of Artificial Intelligence (AI), so too does it have applications in education.  Most institutions are struggling with how to integrate their data across systems in order to create good descriptive statistics, much less predictive analytics.

Learning Analytics Diagram

Being able to gather basic data on learner behavior is critical to compliance, such as for attendance reporting for online courses due to regulations for Title IV.  Other basic data, such as if a learner utilizes Pell Grants, what their high school or previous college GPA was, and how often they login to their online course, can all be brought together to create “student risk scores” in order to intervene early, even before there are warning signs that a learner might be at risk of dropping out.

[i]Within the learning environment, data can show how engaged both learners and faculty are in the online environment, and also even contribute to investment in course design and development.  For example, if all learners in a given course struggle with one particular assessment, that assessment should be examined to see if it is the best one for the job, or if the activities and resources used were not sufficient to prepare the learner for success on that assessment.  Data also needs to be gathered on the amount of time and therefore expense associated with the design and development of courses and programs; without this, institutions cannot determine where to invest their resources.

Read the pages 3 and 4 (which are the Executive Summary and Key Findings) from The Analytics Landscape in Higher Education, 2015.  

Financial Models

There are many financial models that are used in online programs.  Variables that go into these financial models are:

  • Tuition & fees
  •  Cost of creating the program and courses
  •  Cost of maintaining and refreshing the courses
  • Faculty instructional cost
  • Expected enrollment
  • Expected rate of enrollment growth
  • Expected retention
  • Cost of academic support
  • Cost of marketing and recruitment
  • Overhead costs
  • Start-up funding (i.e. grant funding, foundation funding, etc.)

Many more traditional institutions conduct less detailed pro formas than institutions that have successfully scaled online programs.  If tuition is not carefully considered, making assumptions about the cost of the program, instruction, and maintenance can result in a program that is not financially solvent, despite robust enrollments.  Financial modeling for new programs can often be difficult to create accurately, as the market of online education moves fast.

As competition increases, there will likely be price compression.  Additionally, alternate providers like MOOCs are becoming part of the competition, offering increasingly fee-based (not as much offered free), short-form credentials with immediate workforce relevance.

Program Development

How online programs are selected for development vary widely, again dependent upon the type of institution and that institution’s strategic and enrollment goals.  In many cases, existing programs can be put online with notification or previous agreement from a regional accrediting body.  In one notable case, Maricopa Community College District was denied the request for many additional programs being put online as the accrediting agency had concerns about the quality controls in place, as well as no required faculty training, and inconsistencies between courses.

While some institutions enable individual programs, departments or colleges individually determine what programs to put online, institutions that have scaled online programs have a program development process.  Typically in this process there are considerations that include academic governance in addition to market demand, the results of financial projects, and other institutionally-specific criteria in accordance with strategic and enrollment goals.  One example of a program development process is that from Ohio State University (below).[i]

Ohio State University Program Development Flowchart


[i] https://odee.osu.edu/program-development-definition

[i] https://www.unicon.net/about/articles/getting-started-open-learning-analytics-storage

[i] Baran, E., & Correia, A. (2014). A professional development framework for online teaching. TechTrends TECHTRENDS TECH TRENDS, 58(5), 95-101. doi:10.1007/s11528-014-0791-0

[ii] Schelly, C. L., Davies, P. L., & Spooner, C. L. (2011). Student Perceptions of Faculty Implementation of Universal Design for Learning. Journal of Postsecondary Education and Disability24(1), 17-30.

[iii] Stes, A., Maeyer, S. D., Gijbels, D., & Petegem, P. V. (2012). Instructional development for teachers in higher education: Effects on students’ learning outcomes. Teaching in Higher Education, 17(3), 295-308. doi:10.1080/13562517.2011.611872

[iv] Ragan, L. C., Bigatel, P. M., Kennan, S. S., & Dillon, J. M. (2012). From Research to Practice: Towards the Development of an Integrated and Comprehensive Faculty Development Program. Journal of Asynchronous Learning Networks, 16(5), 71-86.

[v] Center for Distributed Learning University of Central Florida. (2015). IDL6543 Online@UCF. Retrieved from http://online.ucf.edu/teach-online/professional-development/idl6543/

[vi] Office of Faculty Development Valencia Community College. (n.d.). Online teaching and learning. Retrieved from http://valenciacollege.edu/faculty/development/programs/online/

[vii] Center for Teaching, Technology and Training Santa Fe College. (n.d.). About us. Retrieved from http://www.sfcollege.edu/ct3/about-us/index