Thomas Lee Hench ABSTRACT
Online learning over the past two decades has grown from offerings of single courses to complete degrees. As such, online teaching represents an educational innovation that has impacted and continues to impact many individuals and institutions. Hence, knowledge of the evolution of this innovation plays an important role in maintaining its efficacy. The Diffusion of Innovation Theory provides a basis from which the evolution of such an innovation may be traced. In brief, this theory posits that the population of potential innovation adopters is represented by a normal distribution which may be divided into innovators and early, early majority, late majority, and late adopters. When viewed over time, the cumulative number of innovation adoptions is thus described by an
“s-curve” showing three stages of growth. In this paper, the Diffusion of Innovation Theory is applied to online teaching occurring over a period of thirteen years at the author’s institution. Specifically, the growth of online teaching was found to follow the predicted “s-curve”. Analysis of additional data acquired from instructor surveys further indicated that faculty experimentation with online teaching and recognition of its compatibility with and advantages over classroom instruction fueled its growth and acceptance. Furthermore, the observed rapid rate of adoption of the online teaching in the middle growth stage resulted, in part, from a change from television-based distance learning to a computer-based format. The paper concludes by proposing the concept of the “trans-classroom” as the next necessary innovative step to maintain the growth and efficacy of online teaching.
KEYWORDS
Diffusion of innovation, online teaching THEORETICAL FRAMEWORK The diffusion of innovation theory
An innovation may be defined as the introduction of a new idea, method, or device. The investigation of how innovations fail or succeed constitutes an important part of businesses and other institutions.
Research into how innovations diffuse through a social environment dates back to the work of Gabriel Tarde in 1903. Later, research by Everett Rogers (2003) resulted in the Diffusion of Innovation Theory which has successfully been applied to innovations ranging from the adoption of agricultural techniques to cell-phone usage. For Rogers, diffusion represents “the process by which an innovation is communicated through certain channels over time among the members of a social system.” The essence of the diffusion of innovation theory is that the adoption of an innovation proceeds over time, starting with an initial group of adopters and then proceeding through subsequent groups. More specifically, Rogers indentified five groups of adopters, each group possessing the general characteristics. Table 1 shows these groups and their characteristics relating to the adoption of new technology.
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Table 1. Adopter groups and characteristics
In addition, Rogers also concluded that, over time, the number of adoptions of an innovation by these groups was described by a normal distribution (Figure 1).
Figure 1. Adopter distribution.
As the
adoption process proceeds over time through each group, the cumulative total of adopters follows what is known as an s-curve, as shown in Figure 2. More specifically, as indicated in Figure 3, the s-curve consists of three stages (Table 2) which describe the innovation-adoption process as a function of time.
Also shown in Figure 3 is the critical mass point which represents that point in the innovation adoption process when the innovation becomes self-sustaining. Typically, the critical mass is reached when the cumulative total of adopters is between 5 and 20 percent (Rodgers, 2003).
Adopter Groups Characteristics Innovators Source of new ideas
Willingness to try unproven technology
Accepting of unsuccessful attempts at implementation Early Adopters Opinion leaders
Use innovator results to adopt new technology Viewed as careful users of new technology Source of information for future adopters
Early Majority Rely on recommendations from previous adopters Avoid risks involved with the technology
Relatively longer adoption times
Late Majority Skeptical and cautious regarding technology
Recognize the technical value but adopt out of necessity or peer pressure Late Adopters Avoid change
Suspicious of innovation and other adopters
Adopt when traditional approaches are no longer available
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Figure 2. Cumulative total. Figure 3. s-curve stages
Table 2. Stages of innovation adoption
Applying the diffusion of innovation theory
Online instruction represents an innovation in education that has shown a rapid increase in use over the past decade. Indeed, the author’s institution is representative of this innovation adoption. Starting in 1999, the school has experienced a growth in the number of instructors teaching at least one course online per semester as shown in Figure 4. In this study, online teaching refers to courses taught solely over the internet and does not include television courses or hybrid courses.
Figure 4. Number of instructors per semester teaching online at the author’s institution
Stage I Initial growth of innovation from Innovators and Early AdoptersExperience and knowledge base developed
Stage II Innovation “takes off” with entry of Early and Late Majority adopters
Innovators and Early Adopters provide support and direction Positive results leads to greater acceptance and increased adoptions
Stage III Growth rate slows down as Late Adopters enter Resource and potential adopter limits reached
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Figure 5. Separate full-time and adjunct online instructor distributions
A first analysis of this data suggests that the adoption of online instruction followed an s-curve and thus allows for an interpretation using the innovation diffusion theory. Specifically, the number of online instructors initially increased at a steady but low rate from 1999 to 2006. This period of time represents Stage I of the innovation adoption process. During this time, experience in online instruction was acquired and a knowledge base established. From 2007 to 2009, the number of instructors teaching online rapidly increased (Stage II) and then appears to head toward Stage III (2010 to present). The underlying structure of the s-curve in Figure 4 may be further understood by separately examining the number of full-time and adjunct faculty teaching online over the thirteen year span of the study (Figure 5).
As illustrated in Figure 5, full-time faculty adoption of online instruction proceeded at a steady rate from 1999 to 2007. During the same time, the number of adjunct faculty teaching online remained relatively low. Then, in 2007, both rates of adoption increased, with the adjunct rate increasing at a greater rate than the full-time rate. This observed Stage II growth is attributable to the decision of the author’s institution to eliminate television-based distance learning courses. To maintain this distance learning option for students, many sections of the eliminated courses were re-established as online courses. This decision provided the critical mass required to initiate the growth observed in this stage, which continued until 2010. At this point Stage III growth for full-time faculty adoptions of online teaching leveled out and the adjunct rate increased at a slower pace. It should be noted that both the full-time and adjunct graphs follow the s-curve behavior. In addition, the full-time faculty acted as Innovators and Early Adopters with the adjunct faculty assuming the role of the Early Majority. Of particular interest to this study is the identification of the factors that permitted the Stage II to growth to occur and the determination of recommendations for the continued evolution of online learning.
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To explain the Stage II growth mentioned above, Rogers (2003) identified five specific factors which influence the rate of innovation adoption (Table 3). By examining the presence or lack of these factors in the adoption process at the author’s institution, the observed adoption rate during Stage II may be described in greater detail.
Table 3. Factors influencing innovation adoptions
It should be noted that the presence or absence of these factors are evaluated in this study from the prospective of instruction. While online courses certainly provide distinct and documented advantages to students, the focus of this study is on the appearance of the factors in Table 3 relative to adoption of online instruction by faculty.
Relative Advantage
To assess this factor the results of a survey conducted at the author’s institution are shown in Table 4. In particular, the survey asked online instructors to rated the change brought in their classroom courses as a result of teaching online. Of the 36 responses, 29 (81%) taught both online and classroom courses and thus qualified to complete the survey. The choices were presented as a Likert scale, where 1 = No changes, 2, 3 = Some changes, 4, and 5 = Major changes.
Table 4. Percentage of changes to classroom instruction as a result on teaching online
More specifically, in the area of assignments/assessment, survey responders noted more specific and focused assignments, the submission of assignments electronically, the posting of rubrics and project grades, and a more precise way of evaluating classroom participation through participation of weekly online discussion as change items resulting from teaching online. Examples of the changes made to classroom instruction in the area of course design/redesign included the creation of teams and group projects, the elimination required texts, more flexibility in teaching style, more supplemental material, and more independent activities and ways to monitor student involvement. While no major changes were reported in the area of communication, the use of forums for discussions, making materials available for the classroom course, more communication with students, and direct access to the instructor were examples of how online instruction has influenced classroom instruction. Similarly, responders indicated greater use of videos in classroom courses and a more flexible course organization Relative Advantage The degree to which an innovation is perceived as being better than the idea
it supersedes
Complexity The degree to which an innovation is perceived as difficult to use
Compatibility The degree to which an innovation is perceived to be consistent with the existing values, past experiences, and needs of potential adopters
Trialability The opportunity to experiment with the innovation on a limited basis Observability The degree to which the innovation results are visible/available to others
Area of change
Percentage responding “some to major changes”
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as benefits deriving from the transfer of online materials to the classroom. It is important to note that for the change areas shown in Table 4, some instructors (possibly the Innovators and Early Adopters previously described) were employing certain techniques and materials in their classroom course in advance of teaching online. Thus, when asked about the change in their classroom instruction brought about by teaching online, these individuals may respond with “No change”.
These results may be compared to other studies examining the “reverse” benefits to classroom instruction of online teaching. Kassop (2003) has identified 10 areas in which online teaching may benefit or improve classroom teaching, including but not limited to more frequent writing assignments, enriched courses materials, immediate feedback, and faculty development and rejuvenation. Research by Lowes (2008) indicates that teaching online has a substantial impact on the teaching of classroom, resulting in improvement in both environments. Table 5 shows the areas in which Lowes found at least 60% of responders making changes to their classroom courses as a result of teaching online. In addition, Lowes further found that change was present to varying degrees across disciplines (Table 5).
Table 5. Top areas of change in classroom instructor resulting from teaching online
When looking at the percentage of responses of 3 (Some change) or higher in Table 4, the results are consistent with those reported by Lowes in Table 5. This suggests that online teaching has made a recognizable impact on classroom instruction at the author’s institution and contributed to the observed Stage II growth, thus demonstrating an advantage relative to classroom instruction by providing options for improving face-to-face instruction. The data suggests that Innovating and Early Adopting full-time faculty demonstrated this advantage which then provided a faster adoption rate by Early Majority of adjunct faculty.
Complexity and Trialability
To address the other factors listed in Table 3, information from surveys conducted by the author of both full-time and adjunct online instructors prior to the beginning of Stage II (2006) and at the beginning of Stage III (2011) is presented (Table 6). Survey questions addressed both the frequency and usefulness of pedagogical practices of Student/Student Interactivity, Student/Instructor Interactivity, Student Content Interactivity, and Student Assessment.
As seen from the data, there were modest gains in the overall frequency and usefulness in Student/Student and Student Content Interactivity areas. More specifically, activities which were initially frequently used with high usefulness ratings (regular communication, course calendars, and summative assessments) showed little change. This suggests that these practices have reached a critical mass in their adoptions and acceptance. In addition, some activities declined or remained stable in their frequency yet increased in usefulness (collaborative group projects, resource and information sharing, ice breaker activity, automated testing and feedback, and formative assessment) while others showed increases in both frequency and usefulness (threaded discussions, PowerPoint or similar presentations, audio/visual materials, and games/puzzles). Finally, other practices (group problem solving, learning style matched activities, animations, and self and peer review) have increased in frequency but remain at the same usefulness level. These instances suggest presence of experimentation by instructors to
Area of Change
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determine the most effective pedagogical practices for their courses and that “trialability” was a factor in determining the Stage II adoption rate of online teaching. Furthermore, these results also imply that the “complexity” factor was addressed and overcome for certain practices. At the same time, some useful practices (portfolios and animations) remain underutilized. Finally, the required certification of online instructors in the use of the online course management system provided further means to address issues of complexity. Since the initial survey occurred prior to Stage II, the data therein applies primarily to full-time faculty. The continued experimentation of this group provided the basis to overcome complexity and trialability issues, thereby contributing to the acceptance by the adjunct Early Majority and the documented Stage II growth.
Table 6. Pre- and post Stage II survey results of online instructors
frequency Usefulness
2006 2011 2006 2011
Student/ Student Interactivity
Threaded discussions 0.83 0.93 3.6 4.0
Collaborative group projects 0.52 0.40 2.3 2.8
Group problem solving 0.39 0.49 2.8 2.8
Resource and information sharing 0.87 0.84 3.2 3.8
Peer review of projects or reports 0.26 0.44 3.2 2.7
Learning style matched activities 0.17 0.40 3.0 2.9
Average 0.51 0.58 3.0 3.2
PowerPoint (or similar) presentations 0.83 0.95 3.8 4.1
Audio/visual materials 0.65 0.88 3.8 4.4
Regarding the underutilization of technology, online teaching as well as teaching in the classroom with technology grows out of immediate need. In particular, technology for teaching is underutilized until
“something” becomes a real problem. At the authors’ institution, the problem was the need to provide more online courses to offset the elimination of courses taught via other distance methods. As indicated in this study, that need was met primarily by the adjunct early majority. Furthermore, while there are Innovators and Early Adopters, many still do not “get involved” until it is an absolute necessity. At this stage they either embrace the technology and wonder why they did not get involved sooner (Late Majority) or they complain that technology will only frustrate/interfere with what they need to
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accomplish (Late Adopters). Showing faculty how to do something and then backing it up with support can expedite the usability/adoption process. The excellent ongoing faculty workshops held at the authors’ school played a critical role in providing this support for the Innovators, Early Adopters, and Early Majority and continue to assist the Late Majority and Late Adopters.
Compatibility
The adoption of online instruction has met the needs of the faculty and institution by providing the population in its region with a greater access to education. In addition, as shown in the discussion of the relative advantage of online teaching, the pedagogical opportunities have increased and met the needs of some adopters. A critical point in gauging the compatibility of online instruction with classroom teaching is the issue of security. In particular, two concerns must be addressed; 1) the need to verify that the person taking summative assessments is indeed the person who signed up for the class and 2) whether any prohibited material (text, notes, etc.) been used by the student in completing the work.
While meeting the former concern is difficult and represents an ongoing both at the authors’ school and others, the latter concern has been addressed by previous work by one of the authors (Hench, 2010). By controlling the content, duration, and question type, scores for online assessments were found to be consistent with similar assessments given in a proctored classroom environment. Furthermore, options such as group projects, formative assessment, essay questions, and portfolios offer ways to make online assessment more secure (See Table 6 for the frequency and usefulness of options currently employed at the authors’ school). However, online security remains a research area.
Observability
Faculty get involved in technology or alter their classroom pedagogy with technology when they perceive it to be purposeful to students, a worthwhile investment of their time, relatively easy to implement and is something that will be sustainable. Once the Innovators and Early Adopters have opened the way, online teaching becomes more available for others to adopt. Figure 6 illustrates the growth of online teaching, which shows the number of course sections taught online for both full-time and adjunct faculty compared to the number of online instructors.
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Figure 6. Full-time and Adjunct online instructors and courses offered
Initially, the ratio of sections of online courses offered was approximately one to one for both groups.
After the Stage II growth, the ratio is approximately two to one for full-time faculty and approaching that ratio for adjunct faculty. However, as noted in the figures, the number adjunct faculty and the number of courses taught by them eclipsed the full-time faculty in approximately 2007, which corresponds to the start of Stage II. Hence, any additional increase in the number of online instructors will come from either the Late Majority or Late Adopter full-time faculty or the continued use of more adjunct online instructors. In a real sense, the Early Majority of adjunct instructors has acted as a self-replenishing population of online teachers.
CONCLUSIONS
By utilizing the Diffusion of Innovation Theory, the evolution of online teaching was investigated at the author’s institution. This investigation yielded the following conclusions
1) The adoption of online teaching is describable by an s-curve and occurred in three stages with a rapid growth of online course offerings observed in the middle stage. Thus, Diffusion of Innovation Theory was applicable employed to describe the evolution of online teaching.
2) Full-time faculty provided the initial Stage I growth of online teaching by accumulating experience and developing a knowledge base. Presently, the growth of full-time faculty teaching at least one course per semester is in Stage III.
3) The factor supplied the critical mass necessary to initiate Stage II growth was identified as the institution’s decision to eliminate television-based distance learning in favor of an online environment.
4) The sustained middle Stage II growth was the result of the innovators and early adopters’
ability to demonstrate the relative advantage, compatibility, complexity, trialibility, and observability of the online instruction as a viable innovation.
5) The increase in Stage II and III instructors teaching online is driven primarily by adjunct faculty. The data also suggests that adjunct faculty may be nearing Stage III.
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6) The continuance of workshops in using technology in instruction played a critical role in supporting faculty to innovate by the adoption of online teaching. Furthermore, the teaching of online courses has resulted in a positive impact on classroom instruction for those teaching in both environments with one-on-one mentoring of new innovators strongly recommended.
As stated in conclusions 2) and 5), online teaching has entered or may be entering Stage III of the
As stated in conclusions 2) and 5), online teaching has entered or may be entering Stage III of the