Internet Learning Volume 6, Number 1, Spring 2017/Summer 2017 | Page 9

Internet Learning
When an invention is applied to solve a
problem or to do something completely
differently than it has been done before,
innovation occurs. Disruptive innovation
is a term, theory, and phenomenon
defined and analyzed by Clayton M.
Christensen beginning in 1995, based
upon his work in the corporate arena
(Christensen, Raynor, & McDonald,
2015). A disruptive innovation is one
that creates a new market and value
network and eventually disrupts an
existing market and value network,
displacing established market leading
firms, products, and alliances.
As demands for improving higher
education have increased, American
higher education has become increasingly
drawn to the proposition of
innovation, in general, and with disruptive
innovation in particular. Examples
of disruptive innovations include
for-profit colleges, online learning, and
competency-based education, providing
students with pathways that provide
a variety of alternative approaches
toward program completion as they
work toward high-value certificates and
degrees. Learning analytics promises to
be another disruptive innovation.
The challenge in higher education
is that the implementation of a new idea
in practice—that is, taking an invention,
and putting it to work so that innovation
occurs—depends upon implementers
willing to navigate through the myriad
changes to practice that ripple through
institutions when a new idea is introduced
to current practice. Some innovations
simply have too much associated
overhead, are not conducive to scalability,
or may be too hard for mere mortals
to use. Practitioners are much more
willing to commit to an implementation
when it solves a problem.
Finding common ground between
innovators and implementers
can be tremendously challenging. Everett
Rogers sought to explain how,
why, and at what rate new ideas and
technology spread in his Diffusion of
Innovations theory, first published in
1962. Rogers (2003) suggested that four
variables influence the spread of a new
idea: the innovation itself, communication
channels, time, and a social system
(see Figure 1). He suggested that this
process depends heavily on the people
involved in the adoption of an innovation,
since an innovation must be widely
adopted in order to self-sustain. He
described categories of adopters as innovators,
early adopters, early majority,
late majority, and laggards. He further
noted that diffusion manifests itself in
different ways and is highly subject to
the types of adopters and innovation–
decision processes.
The advent of data analytics has
brought opportunities for testing new
methodological techniques—including
business intelligence, predictive analytics,
and data mining—for measuring
the impact of innovations on educational
outcomes, and making sure
to bridge the distance from innovation
through implementation, on the way to
adoption. The excitement comes from
the promise of disrupting old, ineffective
practices by replacing them with
new innovative ones, guided by dataanalytics.
The work is motivated by
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