Barriers To EdTech Innovation In Higher Education

Education Technology Innovation is highly desirable, yet its adoption has often lagged...

An ever-growing number of universities are adopting blended learning, this having been coined a “new normal” – the change is driven by various expected advantages such as increased student and faculty satisfaction, more efficient use of resources or more convenient teaching. Regardless of the many specific advantages which could be derived from successfully adopting blended learning, most meta-analyses on the topic agree on one thing: Blended learning is superior to both its exclusively traditional and exclusively online counterparts.

This is definitely good news for education! However, the adoption of new technologies for teaching has sometimes been found to lag and perform beneath expectations. Given the clear benefits that can be reaped from these new technologies and inherent learning/teaching methods, it is important to consider the barriers standing in the way of their adoption, as well as factors facilitating the process. These, as will soon be made evident, are not necessarily straightforward or easy to tackle. Nonetheless, awareness is often the first step to finding solutions, so read on and find out what several experts have concluded based on three studies investigating different angles of the issue, as well as what their findings imply when taken together.

"Institutional Level Barriers and Drivers to Adoption of Blended Learning" - (Porter & Graham, 2016)

In this first study, the authors start by listing insights from past literature, based on surveys in various universities and countries: They count, among the (arguably perceived) blockers to innovation unavailability of technology and support systems, time constraints impeding the preparation learning activities and content for the online environment, lack of motivation or financial support, difficulty dealing with technology, as well as a fear that “rich interaction” with students is lost. Some of the facilitators and incentives to adoption were equipment availability, ease of use, improved student learning, increased student interest and the general advantages over traditional education. These are all worth taking into account when attempting to change an institution, and plans as well as budgets to tackle them are recommended.

Theoretical Framework

To build on these findings, the authors combine two frameworks, one developed by Graham and colleagues in 2013, regarding the institutional adoption of blended learning, and one developed by Rogers in 2003, concerning the diffusion of innovations, defining some categories of individuals based on their propensity to adopt novel developments.

The former theory by Graham et al includes, first of all, key markers related to adoption. These are:

1. Strategy, which includes issues regarding the design of blended learning such as policies, definitions, advocacy methods, and degree/purposes of implementation.

2. Structure, which revolves around the technological, administrative and pedagogical frameworks supporting the blended learning environment, such as blended learning models, planning and evaluation, as well as governance.

3. Support, which concerns the ways in which institutions enable faculty implementation and maintenance of the blended learning design, such as pedagogical and technical support, as well as incentives for faculty.

Based on Rogers’ work, Graham et al also created three stages of institutional adoption:

Stage 1 is called “Awareness/Exploration” and represents the time when an institution has not set forth a strategy regarding blended learning, however, perhaps showing some limited support to staff who might want to employ such techniques.

Stage 2 is called “Adoption/Early Implementation”: This is when institutions have adopted a strategy regarding blended learning, and attempts the creation of policies and practices to support deep implementation.

Stage 3 is called “Mature Implementation/Growth” and describes institutions that have well established blended learning strategies, policies, structures and support in place, allowing for smooth, stable operation.

In addition to the aforementioned markers and stages, Rogers’ 2003 work is used in more depth to complete the framework for the summarized research. “Diffusion” is defined as “the process by which an innovation is communicated through certain channels over time among the members of a social system”. As soon as this process is started, people tend to pick up the signal sooner or later, based on some defining characteristics. Starting from this premise, Rogers created 5 categories of adopters:

1. Innovators: They are the first to adopt technological innovations, representing, in the original study, about 2.5% of people. They are tech-savvy pioneers, actively pursuing new technologies and methods, and even maintaining links to the various sources of innovations for their fields (e.g., companies, researchers etc.). They are the only ones truly willing to experiment without having any guarantee of success.

2. Early Adopters: They represent about 13.5% of adopters and also have an above average openness to innovations, being technologically savvy and being quite eager to test out new developments. Nonetheless, they are more cautious and want at least some proof that a technology is worth adopting before they make this step. Due to their more precautious nature, they serve as good examples for those to follow.

3. Early Majority: The early majority represents about 34% of people. This group is also quite comfortable with technology, and is constituted of true pragmatists: They will only adopt a new technology once it’s tried and tested, and there is proof that it will truly improve their work.

4. Late Majority: They also represent about 34%, and they are not very comfortable with technology, requiring more support than average to adopt innovations, nonetheless being willing to do so if it is proven to work well.

5. Laggards: This group represents approximately 16% and is made up of those who actively resist change due to various reasons, such as aversion to technology and various other non-constructive beliefs. They might even resist change when it is absolutely required, and will often require special measures to innovate.

* The details for each of the 5 categories are attributed to multiple studies using Roger’s theory

Methods and Findings

Based on this theoretical framework, Porter and Graham created a survey to record necessities and attitudes of university teachers related to the 3 markers (i.e., Strategy, Structure and Support) and various concerns relevant to each, and also to place them within one of the 5 categories from Roger’s theory. This was done with the aim of better understanding which matters should be attended to by change-makers so that all educators are motivated to adopt innovations, no matter within which of Roger’s groups they fit, and also to provide a finer grained understanding of this latter theory, for future research to build upon.

The survey was sent to 226 professors of a US-based university (BYU-Idaho) of which 214 answered. The institution was rated by researchers as being on the second stage of blended learning adoption (Adoption/Early Implementation), so deemed appropriate for collecting this data.

The findings reveal several interesting aspects:

Initially, the researchers had planned to use both people’s self-rating and blended learning adoption score to place them within Rogers’ 5 categories, as they expected these to be similar: This was not the case, as teachers tended to overestimate themselves most of the time, with some also underestimating themselves, inasmuch as there were 36 actual laggards but only 2 placed themselves in this category; 89 within the late majority, with only 35 based on self-reports; and 12 actual innovators, with only 6 believing it. As such, they decided to use the more objective measure (i.e., the blended learning score) for the analysis. Nonetheless, this raises a point of caution for institutions that might use this or similar frameworks which imply categorizing staff based on self-rating to understand their concerns: These may not be reliable, and efforts should be taken to implement objective measures of people’s characteristics. Self-ratings could also be useful, in that those more optimistic about their performance might want to confirm these beliefs, so be more open to change, but this is not a given, as some might simply be shielding themselves from an unpleasant truth.

Regarding the actual preferences respondents had, findings pointed to the fact that most in the sample listed structure (i.e., having adequate infrastructure) and support (i.e., benefiting from technological and pedagogical support) concerns as most important in their decisions to adopt blended learning, while not perceiving financial concerns and promotion considerations as vital. Based on innovator type, the findings revealed that:

Innovators, early adopters and the early majority

Innovators and early adopters found it especially important that support networks are established and that the university’s reasons for technology adoption match their own. Furthermore, innovators in particular seemed to have a greater desire for access to an online professional development support system, this being incongruent with theory-based expectations coining them as technology-savvy, but explained as a probable need for the most efficient system available if one would have to learn more. Importantly, and confirming theoretical predictions, the early majority were the only group to score high on existing evaluation data as a necessity for adoption: As such, changemakers are advised to produce this data early on from innovators and early adopters, so that the later ones can more easily be convinced. The early majority also considered ideological alignment with the change important – as all first three categories shared this, institutions would be wise to conjure up a vision/an ideal when promoting blended learning.

The late majority and laggards

The late majority quoted technological infrastructure, technical support and one-on-one trainings as most important, aligning with Rogers’ account: This group lacks technical skills and needs to be helped along the way for the transition to be successful. As such, it might be more difficult without extensive funding and a well-designed strategy to get this group on board with the changes. Laggards are, as expected, the most resistant to change: Nonetheless, they pointed to similar needs as important: Support, infrastructure and perceived value alignment. It would, however, be wise to consider that these similarities might exist mainly at surface value, and that these highly technology-averse individuals will require extensive support to actually make the transition.


This study is useful in conceptualizing some of the factors which may act as facilitators or barriers to change in the context of EdTech innovation within higher education: Various stages of the adoption process might raise different relevant concerns, which is also true of the type of person involved. Members of faculty are different: These differences should be unearthed and understood if effective change is the goal.

Nonetheless, the study (like most in this relatively young literature) is not appropriate for generalizing, as it was done within only one university, one national/organizational culture, and did not employ an experimental design, so the differences discovered can only be considered tentative. It is however a good starting point for pondering the implications of attempting to implement or deepen blended learning practices within an educational institution. The framework it uses is also intuitive and easy to apply, and could be used by future research to add depth to these observations.

“Understanding the real barriers to technology-enhanced innovation in higher education” – (Schneckenberg, 2009)

Using the observed and documented underdevelopment of e-learning within European universities as a starting point, Schneckenberg challenges in his conceptual paper the notion that the obvious barriers to change such as infrastructure, lack of interest in technology, budget constraints or other oft-quoted issues are the ones truly holding back adoption. He proposes instead that the interplay between many complexities and subtleties, for example in the way higher education is organized by tradition and academic culture, or the characteristics of academics, are the true culprits behind the slow evolution of these organizations.

Institutional barriers: The system may not be geared for change...

Universities everywhere are faced with a great need to change: The dissolution of national borders means that they are now entrained in a global competition, and need to become entrepreneurial in approach, with an ability for executing institution-wide strategies and adapting to the ever-faster demands of the education market in order to keep relevant.

Nonetheless, these new needs have often revealed the inadequacies of universities. First, they are defined by decentralization, the various departments/faculties often being afforded high degrees of independence. This creates a tendency for insular decision-making, based more on the specific (and often contradictory) concerns and needs of staff within clusters of disciplines, predictably leading to great difficulties when attempting to exact institution-wide changes such as the uniform introduction of new technologies, as it significantly raises time and money costs, as well as creating many logistical bottlenecks. Second, universities are often conservative, slow-paced, bureaucratic, “bottom-heavy” organizations, which impedes collective actions being taken under strong, unidirectional leadership, as would be, for example, characteristic for the world of business. Third, as universities tend to not expend long-term effort to develop staff based on a shared vision or mission, the possibility for eliciting coordinated action is further hindered. Lastly, the tendency of academics in more traditional universities to only symbolically adopt changes exacted at the macro level, while actually continuing to act based on their habits, might contribute to the illusion of change, without actual improvements taking place. The author concludes that, ideally, the impasse would be solved with the application of strategic HR practices taking all of the above into account, but that in practice, these changes will be very difficult and costly to implement because of the same reasons that warrant them.

Change strategies and their likeliness to succeed: Superficial interventions will fail!

Various universities have attempted to implement innovations related to e-learning via institution-wide strategies. Some of them were documented.

For example, the Open University (UK) tried to tackle the lack of awareness among faculty by sharing good practices, attempting to support strong leadership, explaining the benefits of using learning technologies, while also attempting to convince academics to use technology to move from a “teaching” to a “learning”, collaborative, student-centered focus. Along with these, it also offers various trainings. Albeit seeming like an elaborate effort, this was not deemed enough in practice, as the results were suboptimal. Sclater, the author of the research documenting this campaign, recommends the addition of financial incentives, promotions/career advancement prospects linked to technology use, and an attempt to make known the people working to enable the desired innovation.

The University of Leicester (UK) attempted an incremental innovation strategy: This implied the taking of small steps towards technology introduction, assisted with constant training and staff development approaches. This was quite effective, albeit very costly. The author of this study, too, notes that HR incentive schemes that explicitly reward innovators would be highly desirable. Similar attempts at other universities across Europe also added recommendations such as involving staff directly into innovation and development of teaching methods by participating in research on the matter, networking approaches, and quality assurance programs for integration.

Finally, more ‘radical’ approaches were noted, such as those undertaken at the NUI Galway (Ireland) or University of Twente (Netherlands). The former has increased the relative weighting for the adoption and use of new technologies in its faculty promotion scheme. Thus, staff are evaluated based on this factor, and have to innovate in order to obtain high assessment scores, this being justified via the university’s dedication to educational quality. The latter has split academic staff within teaching and research departments, highlighting different aptitudes that the two cohorts must hone, and also using different incentive systems to motivate efforts in the appropriate directions. As such, teaching staff cannot simply write off improving their teaching as irrelevant and secondary in nature, and are forced to improve. The efforts of teaching staff are peer-reviewed during evaluation, and the resulting score is also based on the integration of eLearning.

As can be seen from this review of institution wide initiatives for change within higher education, extensive efforts based on inventive and radical approaches might be the defining factors for effective strategies. This will likely be costly, but if a teaching organization is truly dedicated to offering the highest quality service, they will have to start the process sooner rather than later, of course, guided by the already available examples in the literature.

The particularities of academic staff and their motivations

Two main perspectives directly concerning academic staff themselves are pointed out.

The first aspect relates to the aforementioned tendency for atomism among the departments, and the likeliness for a mismatch between university-wide concerns and those of members of particular disciplines/departments. Teachers/researchers within STEM, for example, are likely to have different targets and idiosyncrasies of organization as well as performance demands than, for example, those in the art or literature departments – additionally, it is important for academics to have a high degree of independence when doing their work. As the main concern is to create quality research/outputs, and as what precisely “quality” constitutes varies, it may well be that a researcher might be acting against their own interest if trying to conform to strictly to institution-wide changes, as they don’t take into account all of the details of their situation. The inherent cultures of these various departments might also be upholding attitudes of resistance to change. As such, universities are likely to be limited in their attempts to control their human resources on the micro level with macro approaches alone.

The second perspective relates to the motivations and interests of academics. To succeed as a young academic, one must respect the four tenets of the so-called “codex of science”: universalism, communality, disinterestedness and organized skepticism. These essentially mean that a researcher’s output and reputation within their fields should be assessed based on how universally valuable their contributions are, whether these were made available to the research community (i.e., published), the need for maintaining a strong ethical standing and publishing research that is not based on either corporate or public interests, and the requirement to be highly objective and skeptical of research findings until they are proven valid. These pillars of science, while aiming to maintain a high quality for research output, have some unintended negative side-effects for the adoption of new teaching technologies: First, the aforementioned problem of fragmentation is upheld, second, teaching concerns are rendered secondary to research concerns, and younger and older academics alike will tend to focus on producing excellent research outputs in order to build strong careers within academia – teaching aptitudes rarely make for strong arguments in obtaining a better position, as research portfolios take the spotlight. As this is the case, strategies aiming to improve teaching will often require far more effort and resources, and might still risk producing minimal results. Finally, the above is further enforced by a tendency of governments to underfund universities, both raising the barrier to entry for young and innovative staff (and when they do enter, they need to conform to the aforementioned codex even more to be successful) and making parsimony when considering new initiatives aimed at improving teaching seem justified, as research output and investments related to it still have priority.


All in all, the paper by Schneckenberg raises many likely valid points, adding several layers of complexity on top of the more visible concerns discussed by Porter and Graham, to fostering changes regarding the use of teaching technology in particular, and any other aspects, more generally: The underlying culture(s), structures, motivations and creeds of universities and academic life are challenging aspects to tackle that must be carefully considered when attempting to apply any institution-wide change strategy. A deep integration of incentive schemes and redesign of the factors that motivate academics, with additional eyes for combatting or at least accounting for atomism among disciplines, as well as tailoring changes to the real needs of staff are prerequisites for effective and resource-efficient change.

“Understanding the relationship between teachers’ pedagogical beliefs and technology use in education” – (Tondeur et al., 2016)

In a systematic review of 14 studies on the pedagogical beliefs (i.e., beliefs related to teaching) of teachers related to technology, Tondeur and colleagues uncovered some important dynamics which should also be considered when attempting to apply change strategies regarding technological teaching innovations.

Theoretical Premises: A tale of two belief systems...

As a rule of thumb, the authors note based on past findings, teaching-related beliefs teachers carry tend to be from two categories: Instruction-centered beliefs, where the emphasis is on knowledge transmission and the teacher is the “center of attention”, and constructivist, student-centered beliefs, where the emphasis is on knowledge (co)creation, cooperation, and independent learning. The former belief system tends to hinder or constrain the use of technology (e.g., usually employed for assessment or simple information/skill transfers), while the latter often creates an innovative attitude, in which attempts are made to use technology to enable a more active, engaging, self-driven learning process for the students.

Selected Findings: Beliefs may change at times, but don't be too quick to bet on it!

Based on this categorisation, one of the main findings of the review was that beliefs, albeit divisive and often fixed, might actually be, to some degree, fluid in nature. It was observed that the more time was spent by teachers within a technologically rich environment, the more their beliefs tended to shift towards the constructivist/student-centered side of the spectrum, thus making (creative) technology use more likely in the future. As such, the relation between beliefs and technology use is bi-directional, and this is a finding that should be kept in mind when attempting such changes: Exposure to technology coupled with appropriate support is likely to be a valuable way to remove belief barriers. Nonetheless, extremely instruction-centered teachers are likely to not readily change, falling in the pattern of mainly using technology for purposes aligned with an instruction focus.

The next finding from the study highlights that the belief system, especially the teacher/instruction-centered one, does indeed hinder the adoption of technology, as these educators tend to perceive technology as less essential to the teaching process, often invoking the status-quo and the fact that old methods work fine. Another subtlety layer is that this belief system can become more or less emphasized due to various requirements or contextual factors such as time constraints, (mis)alignment of technologies with the teacher’s pedagogical approach, rigid examination requirements allowing little room for new additions, as well as more unusual ones, such as perceptions that students would not be ready to learn via technology – these may in some cases be defense mechanisms to resist change, while in others, very real concerns, and this should be taken into account when designing change strategies.

A final note-worthy finding is that although professional development programs might work to change beliefs and behaviors for some teachers, those responsible for the change are well advised to not overestimate their effectiveness. Changes like these often take years of constant exposure to become embedded, and teachers might at times mimic the change for the sake of their careers or making a good impression, while not truly internalizing it. Core beliefs might be extremely hard to shift, as they have multiple ties with the person’s sense of identity, and it is advised to make these assumptions and values clear if changing them is to be attempted.


As was made evident in the three pieces of research summarized above, those aiming to enact institution-wide changes such as adopting technology-based teaching practices need to plan these interventions with great care, by taking into account everything from the most obvious and visible aspects such as time or resource constraints, technological literacy of staff, and their personalities and propensities, to the less evident factors related to the structure, traditions, culture, and goals of the higher education system and how these translate into the dispositions, motivations and interests of faculty across various departments, as well as the belief systems of each and every individual and how it can be changed or influenced by other contextual factors. Although this might, understandably, seem like a daunting task, it can be stated with high certainty that only by comprehending the system in which change is planned, can it be effectively brought to fruition without wasting too many resources on dead ends. As technology-based teaching innovation and the ability for rapid change are at this point clear requirements for universities to maintain their competitiveness and ability to attract good staff and students from the global market, finding the most efficient way to implement and maintain them is likely the wisest course of action.