Beyond the early adopters of online instruction: Motivating the reluctant majority

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Abstract

Now that most of the innovators and early adopters of online instruction are comfortably teaching online, many institutions are facing challenges as they prepare the next wave of online instructors. This research study examines how faculty in this “next wave” (the majority of adopters) differ from the innovators and early adopters of online instruction. A specific online course development program is described and the experiences of the “majority” in the program are examined in relation to the experiences of previous participants (the innovators and early adopters).

Introduction

The most recent report on online learning from the Sloan Consortium indicates that online education is continuing to grow and is central to many institutions' long-term strategic goals (Allen & Seaman, 2010). Such growth will require more faculty members to teach in an online environment. It is reasonable to assume that faculty members who are just now entering the world of online education will be different from those who initially jumped at the opportunity to teach online and have likely been doing so for a decade or more already. Administrators and staff involved with programs to assist faculty with online course development will need to understand how these faculty members are different and consider how they can best accommodate their needs. This study is a helpful step in a line of research designed to inform best practices in bringing the “majority” online.

The Distance Education Mentoring program at a Midwestern university is a cohort-based mentoring program designed to support and assist faculty as they develop an online course (the program is described more thoroughly in Section 2.1). After running the program for four years, almost 100 faculty members have participated in the program. Each year, the staff and faculty mentors involved with the program have noted changes in the characteristics of the faculty participants (discussed further in Section 2.1.1). Looking at Rogers' Diffusion of Innovation theory (2003) may help us understand these changes.

Rogers (2003) defines innovation as “an idea, practice, or object that is perceived as new by an individual or other unit of adoption” (p. 12). Online education surely fits Rogers' definition of an innovation, as the relatively new format for instruction has gained acceptance and continues to grow within higher education. Rogers is careful to note that perceptions are more important than reality in determining the innovativeness of an idea. Even though many would not consider online education to be “new” any longer, Rogers emphasizes that “if an idea seems new to the individual, it is an innovation” (p. 12).

How “an innovation is communicated through certain channels over time among the members of a social system” (Rogers, 2003, p. 5) is what Rogers refers to as diffusion. He claims that individuals go through five stages as they consider the use of an innovation: knowledge, persuasion, decision, implementation, and confirmation. During these five stages, individuals are seeking to reduce uncertainty about an innovation and will thus consider five key attributes of an innovation that impact the rate of adoption. The characteristics of the innovation considered include relative advantage, compatibility, complexity, trialability, and observability. Rogers argues that innovations which offer improvements over previous ideas, are consistent with needs of adopters, are easy to use, allow for experimentation, and are visible to others will be adopted more quickly.

The rate at which individuals adopt an innovation is influenced by not only the characteristics of an innovation as described above, but also by the innovativeness of the individuals. Rogers (2003) defines innovativeness as “the degree to which an individual or other unit of adoption is relatively earlier in adopting new ideas than the other members of a system” (p. 22). He identified five categories of adopters (as seen in Fig. 1) and discussed the attributes of each group. The innovators and early adopters make up about 16% of the population and are the first ones to adopt a new innovation. These individuals tend to be younger in age, willing to take risks, more positive about the usefulness of an innovation, very social, and are often viewed as opinion leaders in relation to the new innovation. The early and late majority adopters represent 68% of the population and are typically much slower to adopt a new innovation. These individuals tend to be skeptical of new innovations and do not adapt as easily to change. Peer pressure and/or other outside forces may be the biggest factors influencing their decisions to adopt an innovation. The final group, the laggards, represent the remaining 16% of the population and are the last to adopt an innovation. The laggards are very skeptical of innovations and change in general, and resultantly want to wait until an innovation proves successful before adopting.

Numerous studies have used Rogers' theory as a framework for exploring technological innovations, including online education, in higher education (e.g., Alhawiti, 2011, Anderson et al., 1998, Arome and Levine, 2007, Bennett and Bennett, 2003, Jebiele and Reeve, 2003, Parisot, 1997, Sahin, 2006, Shea et al., 2005, Soffer et al., 2010). The theory will be considered in this study to provide insight into how a faculty development program can accommodate faculty who fall into different categories of adoption.

The development of online courses can follow one of two approaches: a traditional, independent faculty-driven approach or a more collaborative approach. In many institutions of higher education, course development has traditionally been the responsibility and privilege of individual faculty members. Perry (1977) comments, “In the conventional university course creation occurs in the mind of an individual teacher, who sorts out his own thoughts and ideas in preparing his lectures” (p. 76). This approach has been used in the development of online courses and such accounts have been documented in the research literature (e.g. Hawkes and Coldeway, 2002, Matuga, 2001, Orwig, 1999, Parise, 2000, Summerville, 2000). Hawkes and Coldeway (2002) argue that such an approach can be effective. They state, “Because the faculty-driven approach is the one-stop help center for content, assessment, technical, and procedural questions, it may in some cases be preferred if faculty have the necessary skills” (p. 435, emphasis added). Ellis and Phelps (2000) point out that many of the first online courses were developed by “online mavericks” or “early adopters” who already had considerable technical skills, and that it is not surprising that many of these courses were in computer-related fields.

However, having adequate technical skills is far from the only (or even most important) requirement for developing an online course. Many authors argue that the online environment promotes a more learner-centered instructional approach, requiring instructors to share control of the learning process with students and take on a more facilitative role (e.g., Jolliffe et al., 2001, Palloff and Pratt, 1999, Palloff and Pratt, 2001, Shearer, 2003). Research also suggests that faculty may struggle with learning the necessary technology skills (e.g., Giannoni and Tesone, 2003, Institute for Higher Education Policy, 2000, April, National Education Association, 2000, June), adapting their pedagogic strategies for the online environment (e.g., Ooman-Early and Murphy, 2009, Palloff and Pratt, 2001, White, 2000, Wolf, 2006, Yang and Cornelious, 2005), conceptualizing their course for the new environment (e.g., Kang, 2001), and finding the increased time required to develop quality online courses (e.g., Bonk, 2001, May, National Education Association, 2000, June).

Many institutions are utilizing more collaborative training and support models to aid faculty in overcoming these challenges to develop high-quality online courses. Numerous studies have examined more collaborative approaches to the online course development process (e.g., Hixon, 2008, Knowles and Kalata, 2007, Oblinger and Hawkins, 2006, Puzziferro and Shelton, 2008, Xu and Morris, 2007). The program examined in this study (described in Section 2.1) is one example of a systematic collaborative approach to the development of online courses.

Section snippets

Context for research: the Distance Education Mentoring Program

The Distance Education Mentoring Program (DEMP) is designed to educate and certify faculty members in the principles of instructional design so as to enhance the quality of their online courses. Specifically, the purposes of the DEMP are (1) to ensure the academic integrity of distance education courses and (2) to align the conditions for learning with the technology used to deliver courses. The program uses a rubric developed by Quality Matters (QM), which is a faculty centered, peer

Demographic differences

There were several differences found related to the higher education experience of the faculty participants. When faculty participants from the year 4 cohort are compared to previous faculty participants, there was a significant difference in how many years it had been since they received their terminal degrees t(38) = 5.75, p = .011. Faculty participating in the first three years of the program had earned their degrees longer ago (M = 16.96, SD = 10.36) than participants in the fourth offering of the

Discussion and conclusions

As speculated, the findings confirm that the year 4 cohort does differ from previous cohorts in some important ways. Although they are not necessarily younger, participants in the year 4 cohort are newer to teaching at the university level. They are also less likely to consider themselves to be early adopters of technology, which suggests that they may be less confident of their technological skills and its value in education. Perhaps it is their lack of experience and rank that prevents these

Limitations

This research study is intended to be a helpful step in beginning a line of research to inform best practices in bringing the “majority” online. However, like all research, it does have some limitations. First, the data was generated from a single survey instrument, which presents two potential limitations. The reliance on self-report data provides an opportunity for participants to censor their reactions which may result in a social-desirability bias. The use of a single survey instrument also

Summary

In spite of the identified limitations, this study reports findings that may be of value to other institutions in a similar situation – that is, are in process of increasing the number of online courses offered while simultaneously striving to improve course quality and university reputation. Even though the number of participants in this study may be small, it has been logically argued that the faculty are indeed representative of the categories of adopters described in Rogers' Diffusions of

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