Analytics in Online Higher Education: Three Categories

The field of analytics in higher education is relatively new and descriptions are often imprecise. Different types of analytics, with little in common, are regularly lumped together. At the 1st International Conference on Learning Analytics and Knowledge in 2011, analytics was defined broadly as the “measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environments in which it occurs.”

shutterstock_184507556Analytics comes in many shapes and sizes, depending on:

  • End-users
  • Type of data required
  • Sources of the data captured
  • Purpose or intended goal

We propose three categories of analytics in online higher education, each concerned with student performance:

  • Institutional Analytics
  • Engagement Analytics
  • Learning Analytics

Identifying and defining the different categories is an important first step in helping educational professionals more quickly focus on the analytics that are of most value to their institutions.

Institutional Analytics

Institutional analytics is primarily concerned with tracking learners through their educational lifecycle, from enrollment to graduation. The data collected focuses on information such as student profiles (age, address, ethnicity), course selections, pace of program completion, use of support services and graduation rates.

Data needed for institutional analytics is often readily available in colleges and universities within the institution’s registration system. The task, then, is to organize the data and identify the metrics that are most important to the institution. The value of the information can be multiplied by linking this data to other systems, such as enrollment and learning management systems, student support applications, and customer relationship management software. Analytics of this type is commonly the purview of the institution’s internal research and business analysts.

Much of the data required can be found within the institutions’ registration system, but the value of the information can be multiplied by linking this data to other systems, such as enrollment and learning management systems, student support applications and customer relationship management software. Analytics of this type is commonly the purview of the institution’s internal research and business analysts.

Institutions use this information in a variety of ways to:

  • Align recruiting tactics with institutional aid
  • Identify better recruiting practices to improve retention and completion rates
  • Predict high-risk students earlier in order to provide more targeted support

External demand for this kind of information is growing as state and regulatory bodies seek to better monitor (and reward) certain types of institutional performance. Similarly, more institutions are distributing information to help students and parents make more informed decisions about programs and schools.

Examples of software used to support institutional analytics includes:

Engagement Analytics

Unlike institutional analytics, engagement analytics track student activity within the course environment, which is typically the learning management system (LMS). The information generated can be of value to the institution, students and instructors. But most of the information is designed with the instructor in mind, keeping with the overarching instructional model of higher education.

The type information collected in engagement analytics often includes:

  • Number of page views (per page)
  • Contributions by students to discussion threads
  • Which students (and what percentage of the total cohort) have completed the assignments
  • Number of logins

This information, as with other types of analytics, is presented in a visual format, often as a dashboard, with its roots in business intelligence software. A well-constructed visual display of data makes interpreting course activity faster and simpler.

When used effectively, this information can help instructors and institutions identify students who may need additional support and encouragement, and help determine the most effective type of student support intervention.

However, engagement analytics do not necessarily measure learning, per se. What’s measured is student activity, which may or may not signal actual learning. For example, engagement analytics is often used to track student page views. The student’s presence on that particular page within the course site tells us that the student has been exposed to that part of the curriculum. But it doesn’t tell us whether the student understands the curriculum. In fact, it may be that the student inadvertently left the browser window open while searching the Internet.

Writer and researcher Stephen Downes, who specializes in online learning, describes the challenge of using engagement analytics this way:

“There are different tools for measuring learning engagement, and most of them are quantificational. The obvious ones [measure] page access, time-on-task, successful submission of question results – things like that. Those are suitable for a basic level of assessment. You can tell whether students are actually doing something. That’s important in certain circumstances. But to think that constitutes analytics in any meaningful sense would be a gross oversimplification.”[1]

Examples of software used for engagement analytics includes

Learning Analytics

Learning analytics measure the student’s actual learning state; what students know, what they don’t know, and why. We propose that the category of learning analytics be reserved for analytics that actually measure changes in a students’ knowledge and skill level, with respect to specific curriculum. The insights generated from true learning analytics support optimization of learning through information, recommendation and personalization. Learning analytics are actionable.

Examples of the type of information that can be captured by learning analytics include:

  • What aspects of the course did the student master?
  • Which students are struggling, and with which concepts, topics and problems?
  • What misconceptions about the curriculum are leading to poor performance?
  • What topics require more attention or better presentation?

The data for learning analytics is captured through frequent formative and summative assessments. Based on the data generated from a student’s interaction with these assessments, it is now possible, as a result of extensive research at Carnegie Mellon University and elsewhere, to derive strikingly accurate measurements of student knowledge and skills. Learning theory offers explanations of the mechanisms of learning (e.g., the power law of learning, cognitive load, rate of learning, learning decay, etc.) and these cognitive factors can be incorporated into learning analytic models to measure, predict and respond to student performance in the online course.

The insights produced by learning analytics can be used to create dashboard-style reports of student performance or to modify a student’s experience in real-time, or both. Learning dashboards give learners, faculty and institutions a visual snapshot of each student’s performance, as it relates to specific learning objectives.

The information can also be used in real-time to continuously adapt the instructional activities (e.g., level of difficulty) presented to the learner to match individual needs. Automated recommendations help create a personal path of learning for each student. Practice is personalized so that students receive the right amount of practice, targeted at the right level, for the right topics. Instructors and mentors receive dashboards and alerts to guide more timely and effective interactions and interventions. Instructional design teams use the data to measure efficacy of course materials, and continuously improve the learning experience, saving time and resources.

Each of the three types of analytics offers value. And there is inevitably some overlap between the approaches. But learning analytics is the only approach upon which educators can confidently determine the actual state of a student’s learning. It provides a true foundation for new opportunities to improve and optimize learning.

[1] Collaboration, Analytics, and the LMS: A Conversation with Stephen Downes. Retrieved February 6, 2014.

Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.

Instructional Resources For Online Higher Ed: Draw On The ‘Best Available’ (Part 1)

A few years ago, a major survey asked university leaders if their online programs were profitable – 45% of respondents said they didn’t know.

This is both odd and predictable. Odd because online education is not the type of initiative in which cost and revenue are less important – quite the opposite. Institutions typically pursue online learning for “business-like” reasons, such as increasing access, market reach, campus capacity issues, and cost management. At the same time, it’s predictable because defining costs in higher education is a murky process; full of ambiguity. “Profit” is calculated with social responsibility in mind. Most units are budget-based, rather than “cost recovery” or profit centers. University managers are often one step removed from major financial discussions. Many positions don’t require advanced financial literacy.Handwritten numbers in ledger

Another factor is that there is simply a remarkable shortage of information about costs in online higher education.

Over the next two months we will prepare a set of blog posts, white papers and articles that address costs in online higher education. We recognize that this is not a small undertaking; the issue of costs in online education is surprisingly complex and can be approached from a number of angles. Issues pertaining to costs include:

  • Productivity
  • Economies of scale
  • The role of new business models that offer new cost structures
  • Institutional differentiation and specialization
  • Class size and instructional value
  • The role of courseware and its capacity to reduce faculty workload
  • Intellectual property
  • University consortia
  • Freemium and MOOCs
  • Sharing courses to reduce duplication
  •  . . . and more.

As a first step, we’re sharing a list of articles and reports that address one or more aspects of the cost issue. The collection includes reports, opinion pieces, articles and news. They come from very different perspectives, from inside and outside of the academy.

Please share other resources you’ve come across in the comments section below.

Resources: A Starter List

How Online Learning Affects Productivity, Cost and Quality in Higher Education: An Environmental Scan and Review of the Literature. 2013.

Massive Open Online Forces. The Rise of Online Instruction will Upend the Economics of Higher Education. Economist. February 6, 2014.

New Players, Different Game: Understanding the Rise of For-Profit Colleges and Universities by William G. Tierney and Guilbert C. Hentschke. 2007.

One Business School is Itself a Case Study in the Economics of Online Education. Goldie Blumenstyk. Chronicle of Higher Education. October 1 2012.

MOOCs and Economic Reality. Clay Shirky. Chronicle of HIgher Education. July 8 2013.

The Online Evolution: When Technology Meets Tradition in Higher Education. Andrew Norton for Gratton Institute. 2013.

The Coming Higher Ed Revolution. Stuart Butler. National Affairs. Winter 2012. 

The Scary Economics of Higher Education. William Baldwin. Forbes. January 15 2013. 

How Universities are becoming more labour intensive. Alex Usher. Higher Education Strategy. January 7 2014. 

UC Strives to Compete in an Era of Free Courses. Alisha Azevedo. October 1 2012.

Technology and the Broken Higher Education Cost Model: Insights from the Delta Cost Project. Rita Kirshstein and Jane Wellman. September 5 2012.

Twelve Inconvenient Truths About Costs in American Higher Education. Richard Vedder. Center for College Affordability and Productivity. March 2012. 

Managing Online Education. Campus Computing Project, 2010.

Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.

Business Model Innovation in Online Higher Education

Note: Part 2 on Business Model Innovation is available here.


Yes, the concept of “business model innovation” sounds like something a management consultant would conjure up. But I encourage you to suspend your initial reaction. The growing interest in business model innovation during the last five years is in response to challenging conditions facing a number of sectors, including higher education.  And the concept provides a useful framework for imagining new approaches.

References to “business model” in Inside Higher Ed, 2005-2013 

A business model is simply the way in which an organization fulfills its mission; how it creates, markets and funds the goods and services it creates for stakeholders – whether they are waste management companies, families buying groceries or students pursuing degrees.

Note: Using the business model concept does not imply that higher education is a business, or that it should be more businesslike. Every organization has a business model, whether it’s IBM or Greenpeace. It’s simply a way to analyze how different types of organizations operate.

Quality > Innovation > Business Model Innovation

Business model innovation is a product of its time. In the 1980s, the watchword was “quality.” (Ford’s slogan was “Quality is Job 1″).  Success was thought to be the result of creating better quality goods and services.

More recently, “innovation” has ruled supreme. Quality alone is insufficient. Quality is merely the “price of entry,” as Tom Peters said. Now, organizations need to become more creative; to “fail fast, fail often.”

Business model innovation takes the change imperative to a whole new level. It calls for organizations to not merely innovate with new and better offerings, but to reinvent themselves in order to survive.

The concept took hold as we witnessed major 20th century companies and institutions falter: Kodak, General Motors and Sears, for example, as well as entire industries, such as music and journalism.

By employing new business models, new organizations emerged and upended established industries. Craigslist cut deeply into newspaper advertising. Blogs pulled audiences away from magazines. Warehouse-style retailers like Home Depot made life difficult for many small retailers. Cirque du Soleil reinvented the circus by creating an entirely new category of entertainment and became a billion dollar company in the process.

Components of Higher Education’s Business Model

Most non-profit higher education institutions in North America operate under essentially the same business model. The differences lie in what they choose to emphasize. For example, some institutions place greater importance on faculty research than others. But most institutions only hire faculty, who have demonstrated the capacity to do university-level research and teach. Faculty are typically charged with doing both.

Clay Christensen and others have argued most higher education institutions, regardless of ranking, share a common notion of what constitutes a great institution. Many seek to emulate the more prestigious institutions, thereby creating greater homogeneity in the sector.

The elements of a business model can be divided in different ways. I’m using the approach developed by Alex Osterwalder, author and advisor on business model innovation.

Business Model Generation by Alex Osterwalder

Market segments

Who does the institution serve? Many universities focus on 18-24 year olds, who have recently graduated from high school. Some universities widen their focus with programs serving adults, who are returning to complete undergraduate degrees.

Value proposition

What are the reasons students and other turn to your institutions? E.g., Widely-recognized credentials that have value in the labor market; ranking as a top research university.

Key activities

Which activities are fundamental to your organization? E.g., Research, teaching, evaluating student performance, developing programs in subjects of value to society, and granting degrees.

Revenue streams

What are the sources of funds that make your institution sustainable and how do you capture these funds? E.g., Government capital grants, tuition, philanthropists and research grants.


How does your institution interact with stakeholders? E.g., Through on-campus teaching, conference participation, scholarly journals and media.

Key partners

What are the other organizations your institution partners with on a regular basis? E.g., Research granting organizations, private companies seeking research partners, and regulatory/accreditation bodies.

Cost structure

What are your main costs, and how do you go about paying for these costs? E.g., Faculty and instructor salaries, administrative staff, building maintenance and marketing.

Key resources

What are the key resources that every college and university must possess? E.g., Faculty, buildings and accreditation.

Stakeholder relationships

How does your organization build and maintain relationships with its key stakeholders? E.g. alumni organizations, university email systems, university social networking (Facebook), learning management systems, and media relations officers.

In the second post, I’ll touch on the analytical value of business models and examples of business model innovation in higher education.

Business Model Innovation in Higher Education, Part 2


Business Model Innovation: A Blueprint for Higher Education

Exploring Higher Education Business Models (If Such a Thing Exists)

University Business Models and Online Practices:  A Third Way

The Higher Education Business Model: Innovation and Financial Sustainability



Good Strategy/Bad Strategy and Higher Education

Sorry to be so harsh, but strategic planning in higher education borders on the absurd. I’ve been witness to my share. More often than not, the participants in the process – of which there are many because we wouldn’t want to leave anyone out – agree to do what they’ve always done, but more of it.  It’s not actually strategic planning at all – it’s cheerleading and goal setting.

Yes, I know, the circumstances in higher education – both internal and external – make strategic planning difficult. Alex Usher of Higher Education Strategy Associates offers this insight:

“There’s a basic problem with trying to get universities to compete with one another: most of them are structurally incapable of following any coherent competitive strategy at all.

Michael Porter posited that there were basically three generic types of competitive strategies. Those competing on a broad scale could compete on cost (e.g., WalMart), or they could compete on product differentiation that allows them to charge a premium (e.g., Apple, Mercedes-Benz). A third option is to limit oneself to a particular niche and compete in a very small market (e.g., Porter Airlines, which only tries to serve a few destinations).

Universities have a hard time restricting themselves to niches, as breadth is one of the things that distinguishes universities as an institutional type. Competing on cost is also extremely difficult for them to do. That’s not just because of their well-known tendency to conflate quality and expenditures; it’s because low-cost (and hence low-margin) strategies tend to work through expanding production and becoming a high-volume producer. Needless to say, exorbitant physical infrastructure costs make this an unviable strategy for all physically-based universities (though distance and e-learning providers can obviously make it work).

That leaves only product differentiation as a viable strategy. But deep down, this idea scares everyone because higher education is an almost comically conservative and isomorphic industry; what Harvard and Stanford do, virtually everyone else wants to copy, to at least some degree. At the margins, there are some value-enhancing alternate delivery models, with Waterloo’s co-op model probably being the best (McMaster could have done it with problem-based learning, but was so conservative that it never fully capitalized on its medical school’s breakthrough). But even that’s too much for most; hence the rush to differentiate on meaningless points such as food quality.

So – no strategy, no differentiation, just individual universities all providing the same services with some different marketing attached and hoping people will think they’re a “brand.” This kind of thing leads governments to believe that institutions are really undifferentiated and should be treated like utilities; institutions, meanwhile, think their “brand” status entitles them to think of themselves as luxury goods.”

I’m slightly more optimistic than Alex. (Alex is the master of writing provocative statements in order to stir useful discussions.) I think universities can move in new directions; they can identify new opportunities and shift resources accordingly. But they don’t have much practice. Good strategy is about making tough choices: this direction, rather than that direction. For last several decades, universities have had the luxury of not needing to make especially tough decisions. Demand for university spaces has grown dramatically since the mid 20th century and barriers to entry in the industry remain strong – keeping new competitors at bay. Today, though, budget realities, government pressure, and real competition (coming soon) are putting a slow and awkward end to this era. It’s time we learnt how to do strategy.

A good place to start is Richard Rumelt’s book, Good Strategy/Bad Strategy: What’s the Difference and Why it Matters, Rumelt provides dozens of examples of strategies developed by a wide of organizations that are not . . . well . . . strategic. Most strategies, he argues, are nothing more than vaguely defined goals. It’s rare that an organization actually combines these goals with tactics that will make achieving the goals more likely.

Rumelt has little patience for bad strategy – particularly for the “strategy template” – the common practice of creating vision statements, missions, values – which are so common in higher education. Consider his interpretation of Cornell University’s mission statement:

Cornell’s mission statement reads: “To be a learning community that seeks to serve society by educating the leaders of tomorrow and extending the frontiers of knowledge.”

Rumelt’s interpretation:

“In other words, Cornell University is a university. This is hardly surprising and is certainly not informative. It provides absolutely no guidance to further planning or policy making. It is embarrassing for an intelligent adult to be associated with this sort of bloviating.”

Any day someone uses “bloviating” in a sentence is a good day.

Author: Keith Hampson, PhD

Related Posts:

The Concept of Competition in Higher Education

Digital Higher Ed Content and the Long Tail

New Contexts for Educational Content

Contact Keith Hampson > > Research and Consulting for Digital Higher Ed

The Long Tail

Digital Higher Ed Content & The Long Tail

The focus of digital higher education during the previous decade was overwhelmingly on the technology itself – learning management systems, bandwidth, faculty literacy with technology, student technology support, and so forth. But I entered the world of higher education through an interest in interaction of culture and markets, and for me digital content (or media) is key.

Digital content is where people, culture, technology, organizations and markets meet. It’s messy, human and creative. And when you include analytics and social platforms, the potential of rich media to radically improve the quality and economics of higher education is extraordinary.

In 2012 it appears that digital education content is finally getting some attention. 2012 is offering us OER (as well as OER with credentials), new authoring platforms, content-friendly devices (e.g. tablets) and aggressive innovation in the textbook publishing industry.


Chris Anderson's Long Tail Graphic

In order to help make sense of these developments, I recently reread Chris Anderson’s “The Long Tail” (2004).  Anderson’s theory, if you’re not familiar with it, argued that the Internet has fundamentally changed the economics of producing and distributing digital products. “Shelf space” on the Internet is virtually infinite and increasingly inexpensive. It’s now financially feasible for vendors to sell a much wider variety of digital products, particularly books, films, and music and other media. Consequently, marketing strategy is shifting from a dependence on a relatively limited number of “hits” or “blockbusters”  (e.g. Top 40 radio; New York Times bestseller lists) to serving niches.

Higher Ed and Content Variety

Anderson contends that consumers have tended to purchase “hits”, not because they are indifferent to less popular fare, but because of a lack of choice. But now the Internet is removing the bottleneck between suppliers and consumers. And as search and distribution technologies improve, and costs continue to decrease, Anderson forecasts that the top sellers in a variety of markets will constitute a smaller share of total sales, and the number of different products available will increase dramatically (i.e. further flattening and lengthening of the distribution of sales). This is the “long tail”.

Of particular relevance to higher education, Anderson also predicts that more products will come to us by way of “amateurs”. These products created and sold by individuals (often on a part-time basis) are often presented alongside those from large commercial enterprises. Youtube is an example of this, blogs are another.

We are seeing a similar trend in higher education. Content development and distribution is being pushed down to the most local-level: the individual instructor. The role of the instructor is unusually well-suited to this trend because, firstly, academics are subject-matter experts, and are expected to be able to create their own instructional materials. They create course notes, slides (powerpoint), and research papers. The difference is that now they have the capacity to build this content on better platforms and distribute it widely.

The idea that “everyone is an author now”, which is made possible by the changes Anderson identifies, fits perfectly with the pursuit of originality that is fundamental to the social and labour market value of academics.  An academic’s value is largely based on their knowledge of subject matter. And in order for the academic to be valuable, their contribution to the subject must be in some respect original. Consequently, academics have a vested interest in maintaining the notion that their work is original; publishing is the means by which this originality is publicly demonstrated.

Third and finally, there is a cultural and political component to self-publishing. For many, the act of self-publishing is a means of working outside and beyond the control of larger organizations, typically commercial ones. This is a very appealing notion to many people in the field of education, in so far that it is consistent with the political leanings of many academics and their attitudes with respect to the limited role that commerce should play in education.

In one respect, content has always been local in higher ed. Higher education is one of the most decentralized enterprises in the 21st century. But the interest of individuals in self-publishing is now matched by the emergence of a near-complete eco-system that enables them to create, manage and distribute educational content. Authoring can be done on ScholarPressCreative Commons can serve as the legal framework for copyright and reuse; repositories such as Connexions and Merlot provide the technical infrastructure to house and distribute the content.

But while there is potential to produce an ever-increasing range of digital educational content in higher ed, this supply needs to be matched with demand. Is there, as Anderson argued with respect to other markets, demand for a far greater variety of content?

The Surprising Endurance of “Hits”

Before trying to answer this question, it may be useful to consider the work of Anita Elberse. Elberse took a second look at the Long Tail argument. Her analysis, Should You Invest in the Long Tail?  (Harvard Business Review) suggests that the market for “blockbusters” remains largely safe from the onslaught of multiplying niche markets. Despite the changing economics of content authoring and distribution that Anderson describes, the bulk of sales are still found in the “head” and the “tail” is remarkably flat. For example,  24 percent of the nearly 4 million digital songs available for sale through stores like iTunes sold only one copy each in 2007.

Apparently, we aren’t quite as adventuresome in our tastes as we’d like to believe. We are attracted to products and services that are validated by other consumers. We rely on each other as guides. The great growth in product variety may not be matched by an equally great growth in choices made by consumers. ,

The degree to which Elberse’s argument invalidates the Long Tail theory is somewhat dependent on where we choose draw the line between the “head” and the “tail”; that is to say, what level of consumption/sales we think constitutes a “hit”. For me, though, the most useful aspect of her work is that it reminds us that there are important forces at play that give shape to the distribution of sales (head and tail). The increased variety of products sold is not merely the result of increased choice, as one might believe from reading Anderson’s work.

The Limits of the Long Tail in Higher Education

The insights from Anderson, and the questions posed about these insights by Elberse, can help us understand and anticipate the changes in the market for digital higher ed content. What, for example, is the necessary variety of digital educational content?  What are the factors that might influence the demand for a more diverse range of content in higher education? Do we have a preference for “hits” in higher education?

The implications are considerable. Answers to these questions will determine who produces the educational content, what gets produced, who pays for it, and what it ultimately costs.

As a starting point for addressing these questions, I offer three issues that may effect the length of the “tail” of content in digital higher ed:

Quality (Re)assurance. Do we need assurances from others in the field about the quality of content, and from whom, exactly? There are conventions in place: In traditional textbook publishing, it is common to employ currently employed academics from well-known (preferably) institutions of higher education as authors. In OER, we find the use of simple rating systems, such as stars (one-to-five), to crowd-source evaluations. To what degree will the need for assurance from others limit the expansion of the “tail”? What will new systems for validations look like?

Consistent and Coherent Curriculum. To what extent must the content be consistent with the curriculum within the institution and other institutions? Although not to the same degree as K12, higher education is a “system” in which students progress through curriculum in a step-by-step fashion. Consequently, there are levels into which digital content must fit. When a student moves from first year to second year, or transfers from one school to another, there is an assumption (hope?) that the first year accounting course at University A is roughly equivalent to the same course at University B. (The Bologna Process is relevant here.) How will the proliferation of content sources fit into the need for common, coordinated curriculum?

Production Quality. How important is it to educators and students that the content that they use meet a minimum standard of production quality? That is to say, at what point does “home-made” content become a liability because it is either difficult to integrate into an LMS, “buggy” (in the case of content embedded in applications), or simply difficult to use for students and instructors? How far along the ever-extending “tail” will content of sufficient quality be found?

As the variety of content increases in the coming years, educators, institutions and publishers may want to pay attention to these and other issues to determine how they go about creating, acquiring and distributing content. Although it’s too early to be certain, my suspicion is that like the markets of music, film, and books, the demand for “hits” in digital edu content will remain surprisingly strong.

Note: A number of people have written about the relationship between The Long Tail and education – I’ve included a list below. You’ll recognize, though, that some of them use the concept of the Long Tail to analyze the diversification of students. That is, the tail gets longer as more people participate in higher education.  Instead, I use the concept to analyze the diversity of educational content. Although the former approach may be of great value, my focus on educational content is more in line with Anderson’s original use of the concept.  


Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.

 Related Articles

Minds on Fire: Open Education, the Long Tail, and Learning 2.0

The Long Tail Cometh in Education . . . But Slowly

The Long Tail in Education

The Long Tail Model of Higher Education

Long Tail Learning and Curriculum

Long Tails and Big Heads

Should You Invest in the Long Tail? (Anita Elberse)

BISG: Higher Ed Student Attitudes to Content Research Report


Coherent, Coordinated, and Consistent Design of Online Courses

shutterstock_168469727It’s not uncommon for online courses in higher education to include instructional resources from a wide range of sources. Resources may include digital content from textbooks (e.g. flashcards), images used in campus-based courses, freely available content from the Internet, print or ebooks from publishers, activities pulled from open education resource repositories, and others.  Some of the material is placed within the course environment, some sits outside.

The “cut-n-paste” functionality of the Internet has made this “bricolage” approach to course design easy and, therefore, inevitable. However, the bricolage approach has also increased the prevalence of online courses with weak instructional coherence, coordination and consistency. By relying on materials from a wide range of sources – each built by different organizations, to serve different users, and to fit into different contexts – we, inevitably, decrease the degree to which each unit of instructional material aligns with the other materials. In the end, it’s learning outcomes that are compromised.

Symptoms of Incoherent Course Design

Instructional materials and activities drawn from a variety of sources can differ in a variety of ways that impact instructional quality. Differences include:

  • Level of difficulty. Instructional materials gathered from different sources are designed for students at very different levels of subject mastery and comprehension.
  • Terminology. Different sources often employ different terminology to describe similar information.  While these differences are often small, and may seem inconsequential to subject matter experts, they can easily confuse learners that are new to the curriculum.
  • Pace of instruction. Each instructional element implicitly assumes a certain pace of instruction through which the student will progress through the material.
  • Level of detail/depth. Instructional elements include different amounts of detail. Asking students to move between instructional materials that include different levels of detail may make it more difficult for them to identify what information is essential, and what is not.
  • Organizational principles. Every instructional element is designed to operate in a particular structure and design environments. Pulling items out of one context and dropping them in another adds unintended (and instructionally useless) complexity.
  • Design features. Visual design features, such as use of color and icons, can be used effectively to improve comprehension and ease of use, but they must be applied consistently.

Barriers to Coherent Course Design

In the classroom setting, the bulk of the instruction is created by and funneled through a single source: the instructor. As a result, instructional coherency tends to occur naturally. In online education a number of factors work against coherency:

  • The ease with which we can find related instructional content on the Internet;
  • Confusing regulations concerning use of copyrighted material on the Internet;
  • The inability of institution staff to produce a wide range of instructional materials at a low cost (due, largely, to the lack of economies of scale in the dominant business model of online education);
  • Insufficient incentives for faculty to dedicate additional time to course design and development, given prevailing compensation and incentive models.
  • The lack of professional development resources for instructors responsible for course design.

These inconsistencies make it more difficult for the educator to provide students with coherent and effective learning. The quality of learning can suffer and the need for student support – from the instructor, staff and others – is heightened. Students should be able to focus all of their limited energies on learning, not on trying to understand the different levels, styles, pace, and sequencing in a grab-bag of instructional element. This coherency, in turn, allows the instructor to focus her time on teaching and supporting students, rather than compensating for inconsistent and instructionally incoherent course design.

In a well-designed course, the instructional materials are fully integrated and coordinated, pitched at the appropriate level of difficulty, presented to the learner with the ideal amount of detail, and have consistent design features (color, navigation). Each element in a course should be built according to a single, overarching design – coherent, coordinated, and consistent.

Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.

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World Economic Forum: Higher Education: Investment or Waste (Video)

World Economic Forum: Higher Education: Investment or Waste


Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.

Report: Higher Education Market Leadership

Research firms are often the first to see patterns in industries – a result, I imagine, of their working with so many different organizations. 

Hanover Research has a substantial higher education practice. They’ve just released a new report, Higher Education Market Leadership.

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Hanover anticipates that we will be hearing more about these issues in 2014: 

  • Measuring the impact of a college degree.
  • Measuring institutional effectiveness.
  • Cost of college and the impact of student debt.
  • Innovative higher education models.
  • Continued expansion, analysis and refining of onlineeducation.

The full report is available here.


Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.


The Rise of Alternatives to University Continuing Education (Part 1)

cropped-chalkboard.jpegHistorically, continuing education schools (CE) assumed responsibility for tackling some of the more important tasks facing universities. Online programs were often first tested in continuing education. CE catered to the non-traditional learner long before this group became the norm across higher ed. The financial model of CE forced these schools to be market focussed, well before the budget/tuition crisis hit higher ed proper.

Things are not about to get any easier for CE, particularly for those schools that rely in whole or in part on non-credit programming. Alternative education providers – from outside of colleges and universities – are getting their acts together and are likely to capture a growing share of the non-credit education market.

Examples include:

  • General Assembly offers both f2f  (London, New York, Toronto) and online courses in the related fields of technology, small business and design.
  • Codecademy provides free programming lessons on Python, JavaScript, PHP and more. 
  • Nomadic Learning offers a set of short courses (e.g. 5 hours in duration) on topics such as Critical Thinking and Strategic Thinking.
  • Udemy  offers online courses on a number of practical subjects like Excel as well as general interest courses like “Capitalism in Crisis: The global economic crisis explained.”
  • “Maker” culture, which really took off in 2013, promotes the idea that we need to get back to “making stuff” (rather than just consuming or manipulating); it’s the ultimate in learn-by-doing. See here for more on their education dimensions of Maker culture.

Alternative providers like these are growing in number. Course quality is improving and learners seem more inclined to accept the legitimacy of non-university learning providers.

The days when colleges and universities could use their formidable reputations to reach into the non-credit market unchallenged are over, and the economics of the Internet makes it easier than ever for small companies to compete with the once dominant footprint of higher ed.

Let’s be clear, we need these new learning providers. We are living through what appears to be a “jobless” economic recovery and people need a way range of options – at different price points – in order to quickly retrain themselves for a rapidly changing job market. A robust and diverse continuing education market is a priority for the 21st century and our government leaders and regulators should be crafting policy to make it happen.

In a second post on this subject, I will consider some of the tactics continuing education schools might explore as they adjust to the rise of alternative education providers.


Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.

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Ivory Tower – Upcoming Documentary by Andrew Rossi

Andrew Rossi’s last documentary, Page One: Inside the New York Times (2011) provided a glimpse into the changes going in newspaper journalism, one of the key  institutions of the modern era. In Ivory Tower, he turns his attention to the university. Based on his last effort, it should be interesting.

Below, Forbes contributor, Dorothy Pomerantz, interviews the Director. (Article to accompany the interview can be found here: Is Higher Education Worth It? Documentary Filmmaker Andrew Rossi Investigates. 

Other documentaries about higher education that you might want to check out:

College Inc. 

Declining by Degrees

Radio Documentaries (multiple)


5 Factors Influencing Design in Digital Education in 2014

When we shift the focus of higher education from the physical classroom to the digital environment, design becomes a much greater factor in creating successful student experiences.

Design, here, refers to graphic and industrial design, where aesthetics and function merge.

In previous posts –  Why design matters and Design and screen-based learning - I made a number of assertions:

  • There’s a growing recognition that the ‘look and feel’ of products is fundamental to their value.
  • Design is not merely about surface aesthetics. Design involves aligning the needs, sensibilities and behaviors of people with the things they use.
  • The value of screen-based experiences (e.g., laptops, tablets, smartphones) is highly dependent on the quality of design.
  • Design is a powerful tool for making it easier for us to live with technology’s  over-caffeinated rate of change.
  • After centuries of classroom education, design can help us make the transition to digital education easier.

For a variety of reasons, the software and content created for digital higher education has largely ignored the role of design – and it shows.

However, there are five factors at play that may give the field of design a more central role in digital higher education in 2014.

1. Design and learner data
The use of analytics is driven by a growing interest in measuring the efficacy of learning. As the education sector sharpens its focus on results of its investments and strategies, ambitious and innovative institutions are paying more attention to how courses are designed and developed.

Well-designed courses can increase retention and improve learning. They are easier to use, allow students to focus on learning rather than courses logistics, reduce demands on support, and present the right instructional resources at the right time. Intelligently crafted analytics captures these improvements, which leads to greater attention to course design.

2. Design as a competitive differentiator

Pundits have been talking about the highly competitive landscape of online higher education for almost 15 years. Yet, it is only recently that colleges find themselves offering very similar online programs as their competitors, and at similar prices. (For now, this is only acute in certain disciplines, such as business and nursing.)

Real choice leads to real competition. And competition requires differentiation. Design is one of the few tangible ways – beyond price – that institutions can demonstrate the value of their online programs to prospective students. (For more on differentiation and the use of “surrogates of quality,” see Lloyd Armstrong’s excellent post on competitive higher education).

3. Consumer-education apps crossover
Educational technology has historically advanced less quickly than consumer technologies. This is also true in terms of the quality of design. But consumer-industry design is finding its way into education in two ways:

  • Educators now regularly use consumer applications in their courses, such as Twitter, WordPress and Facebook. For more information on using Twitter in higher ed teaching, check out this article and YouTube video.
  • Edtech vendors are adopting the qualities and characteristics of consumer technologies. An example is Instructure’s Canvas learning management system, which gained favorable reviews for its ease of use, and more broadly, its consumer-style user interface.

4. Big media investing in education
There is growing interest in digital higher education among traditional media companies.  While many in education bristle at this trend, these corporations bring deep experience in packaging and delivering information-related products with high quality design.  Among them: News Corp. (Amplify), New York Times (The Learning Network), The Washington Post (Kaplan Inc.), Bertelsmann AG (Brandman University), and Condé Nast (Condé Nast College of Fashion and Design).

Internet traffic

5. The rise of apps
A 2010 Wired article by Chris Anderson pronounced, “The Web is Dead” making the point that more people are accessing the Internet from applications than browsers. Internet traffic is increasingly managed by applications like Netflix, Facebook, and Xbox. And as more people access the Internet via mobile devices, the trend will continue.

Applications offer a superior user experience. Possibly more so than any other consumer product category, applications compete on the basis of design.  Consider task management apps. These tools compete largely on the quality of the experience they offer; the way they manage and display information. The actual information available through these tools is pretty much the same, but the user experience isn’t. The consumer can quickly and easily switch from one app to another in seconds, without disruption. Design is the difference between success and failure.

These five factors – for different reasons and in different ways – are elevating the role of design in digital higher education, and specifically, in the course design and development process. Those institutions that find ways to leverage design to improve their digital learning programs will benefit.

Keith Hampson, PhD

KPCB’s 2013 Internet Trends (Education Data)

Pages 98 to 101 and page 108 concern digital education.


Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.

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Design & Screen-Based Learning in Higher Education

“. . . design has spread like gas to all facets of human activity from science and education, to politics and policy making. For a simple reason: one of design’s most fundamental tasks is to help people deal with change.” (Economist, 2011).

iStock_000025705001XSmall copyIt’s widely thought that the rise of design during the past couple of decades owes much to the rapid pace of change that characterizes modern life. Design facilitates change for those seeking to stimulate change.  And for end users, design serves as a means of making changes less jarring and uncomfortable.

As Ray Kurzweil, author, inventor and director of engineering at Google has argued, the rate of change is accelerating as never before.  The time between invention and mass adoption of, for example, consumer technologies, such as mobile phones and social networking, has dropped dramatically.

The migration from the classroom environment to the Internet is one of the most dramatic changes in the history of higher education.  Although the Internet has been in Western households for 20 years, the vast majority of students and educators have grown up in the physical classroom model. Our institutions are designed according to the needs and logic of place-based learning. (We shouldn’t be surprised that the initial approach to web-based education was to try to replicate the classroom environment).

Screen-Based Learning

But the migration from the classroom to a screen-based environment is a change like no other. It’s a migration to a design-dependent environment. The digital learner’s experience is highly-dependent on the quality of design. The particular mix of colors, layout, audio, animation, words per page and other design elements can make the difference between a good and bad experience for learners on laptops, smartphones and tablets.

To date, digital higher education has largely ignored the role of design in online learning. It’s not part of the conversation. You’ll be lucky to find it discussed at conferences or in journals. This is partly because good design practices are not part of most institutions’ DNA.  (Have you ever tried to find your way around an unfamiliar campus? Signage, anyone?). And partly because institutions often frame aesthetics and related matters as enemies of science.

It’s time that digital higher education recognize the demands of this new online environment. The factors that determine the quality of learning are different than those that ruled the classroom in which we grew up. We need to include design talent and processes in our course design and development practices if we are going to make better use of this (still) new environment. As the saying goes, “When you pick up one end of the stick, you pick up the other end.”

11-Minute Webcast on this topic, Leveraging Design, on December 19, 2013


Dr. Keith Hampson is Managing Director, Client Innovations at Acrobatiq, a Carnegie Mellon University venture born out of CMU’s long history in cognitive science, human-computer interaction, and software engineering. In addition to adaptive “intelligent” courseware and learning analytics, we offer a range of consulting and professional development services for colleges and universities that increase the quality of their digital programs.


Understanding the Accelerating Rate of Change


Why design matters in digital higher education

534676_thumbnailDesign is having its moment.  Apple’s  Jonathan Ive, Philippe Starck and Michael Graves are among a growing number of designers enjoying rock-star status. Businessweek, Fast Company and other pubs now dedicate entire issues to design. Enrollment in college design programs has spiked.

But what role does – or should – design play in education, specifically digital higher education? A lot, it turns out. As we move from the classroom to the screen, design matters more than ever.

The qualities that create great design are also the qualities needed to create great online learning experiences. 

The relationship of design and higher education is the theme of a series of posts we’re kicking off.  This first post highlights what great design and great educational experiences have in common. The parallels are many.

Next, I’ll explore the forces of competition and change driving the need for design in higher ed. The third installment will review the state of design in higher ed.  I’ll wrap up the series by exploring the parallels between design and learner data.

So, exactly what is design?  There isn’t a single definition; the field is broad and expanding. In the context of this series, think of design more as user experience (UX), than instructional design.

Design in digital higher ed is about how people interact with screens, software, interfaces and information in a holistic, multidisciplinary way.

Similarities between design and education:

  • Great design and great education is user/student-centric.
  • A great designer, like a great educator, takes what is complicated and makes it easy to understand.
  • Well-designed services and systems are elegantly integrated and easy to use; so are the best educational web sites, services and systems.
  • Great design leverages the user’s existing knowledge, just as great education builds upon the learner’s prior knowledge.
  • Great design connects users with information and experiences in ways that makes it memorable and “sticky.” So does great education.
  • Great design attracts the user by making the experience as compelling as possible. Great education strives to engage learners and increase interaction – a key determinant of learning success.
  • Great design evokes an emotional response, which can alter the user’s cognitive state. Great education can evoke positive emotions that make students more creative and open to new approaches when learning.
  • Great design saves time by focusing the user’s attention on the most important information. Great online learning experiences maximize students’ time by focusing their attention on the key learning objectives and outcomes of the course.
  • Great design seeks to transcend passive, one-way communication towards active engagement with the user. Isn’t this the goal of all great educators and institutions?

We know from retention and completion rates that just providing knowledge is not enough. Other sectors and industries have recognized this. Design is a differentiator in the market because it adds real value. It’s a lesson that higher ed is just beginning to learn.

Reminder: We will be looking at design in an upcoming 11-minute webcast on December 19, 2013 at 12:30pm ET. Register here.

How do you see the relationship between design and higher education? Share your comments here or on twitter: @KeithHampson or email:

Next post: Design and Screen-Based Learning in Higher Education

Resource: Glossary of Design Terms


By Design: Selected Resources from the World of Design

As part of our series on the importance of intelligent, high quality design in online education, we’ve prepared a list of articles, books, videos and other resources for your perusal. Most of the selections below come from the world of design rather than education, specifically. Hopefully, in time, more writing on design will address education.

Design library, reorganized by topic
(Photo credit: juhansonin)

If there is another resources we ought to include on this list, please let us know by sending me a note at

Reminder No. 2: Related Posts:

1. Why Design Matters in Digital Higher Education

2. Design and Screen-Based Learning in Higher Education


A Brief History of Design Thinking

Design Thinking Sparks Learning in Rural North Carolina

The Three Future Waves of Design and How to Ride Them

Welcome to the Era of Design

In Defense of Eye-Candy

Academic Articles/Books

Designing for People 

A Designer’s Art

The Substance of Style by Virginia Postrel

The Design of Everyday Things

Improving the Environment in Distance Learning Courses Through the Application of Aesthetic Principles

The Impact of Design and Aesthetics on Usability, Credibility, and Learning in an Online Environment

The Pleasure of E-Learning: Toward Aesthetic E-Learning Platforms

The Influence of Aesthetics and Content Structuring to E-Learning Systems’ Users Behavior 


Design Thinking for Educators

Don Norman: Designing for People

Design Genius (BBC Documentary)

A List Apart (Magazine)


A Glossary of Design Terms

Dr. Keith Hampson :: Support for Innovators in Digital Higher Education

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