Monthly Archives: September 2014

Ten Important Concepts in Online Education

In 2012 most of us learned that MOOC was an acronym for a Massive Open Online Course, an online course format designed for widespread access and consisting of online video lectures, assessments, and discussions.  The recent the shift toward modularization of content, highlighted by the recent report from MIT’s Task Force on the Future of Education, is bringing a new set of concepts, and their descriptors to the fore.   Ten of the most important new concepts, and their new or or re-purposed descriptors, include:

  1. Learning Object – A video lecture, text file, graphic or picture, game, simulation, or other item designed for presentation and often interaction as tutorial or learning activity.   This Khan Academy video calculating the take-off distance of an Airbus 380 is hand drawn with voice over and is part of a lesson on “acceleration.”  Another example of a digital learning object as is this New York Times app for comparing, and learning about, rent vs. buy economics in residential real estate.
  2. Repositories and Collections – A repository is a location where digital learning objects are stored.   A repository may be indexed, like a library or an archive, but is not necessarily structured for a purpose.   As modularization is recognized as important, the focus on MOOCs and scalable online education has been transformed into the push to develop repositories of digital learning objects.  A prominent example is Merlot II.  Another illustrative example is RIT’s Digital Media Library which captures, distributes and preserves RIT’s digital products for research and teaching.  A collection is a set of digital learning objects that is structured for teaching and learning purposes.   A collection may reside in a single repository, consist entirely of a set of links to digital objects not resident in a single repository, or a blend of both.   An example of a collection is the  Afghanistan Digital Library, a project of New York University Libraries, which is collecting, cataloging, digitizing, and making available over the Internet Afghan publications from the period 1871–1930.
  3. Curator – The person, persons or organization responsible for determining what is included, and what is excluded, in a collection.   Like the editor and publisher of a reference work, textbook, or manual the curator of a collection bears responsibility for the scope, currency, veracity, and functional performance of the items in a collection.   For example the curator of the Afghanistan Digital Library mentioned above are the NYU Libraries and a named advisory board.  The curators of MIT Open Courseware collections are the MIT faculty and named members of advisory boards.
  4. Content and Media Management Systems – As digital content has exploded, many enterprises have adopted systems to manage the content used in their work.  For basic content management, a very common system is Sharepoint from Microsoft but new cloud based models from the likes of Dropbox,, and Google are increasingly popular.  In content management, vendors abound as do open source options.  Media management has some similarities to content management but was pioneered by organizations whose product was content.  Now also widely used in marketing departments, media management systems are able to gracefully deal with the large file sizes of rich media, their complex intellectual property, the need to track derivative works, and include many other features specifically for managing rich media such as transcoding among formats and packaging multi-part products.
  5. Learning Management Systems – About 15 years ago academia began to adopt specialized content management systems that helped automate a many administrative processes.  These Learning Management Systems are now pervasive at many universities and are based on both open source platforms and a wide variety of commercial vendors.  While not without critics, these platforms have in many respects laid the digital foundation for today’s rapid pace of digital innovation in higher education.
  6. Learning Community – This term was coined (or re-purposed) to describe an online community of learners, especially those that emerged as an aspect of a MOOC, or were created by online class discussion forums supported by a Learning Management System. Some digital learning communities have fairly traditional boundaries (students in a course on a single campus) while others are something entirely new, such as the learning community that surrounds and supports the Animal Diversity Web.
  7. Adaptive Learning Technology – Adaptive learning refers to the use of educational engagement and performance data to adapt, customize or personalize the learning experience for a group or individual. Typically this involves a data collection, a map or outline of the educational topic areas (possibly a Knowledge Representation — see number 9 below) and a set of mechanisms to modify and adapt the learning output, such as a recommendation engine.  A prominent example of adaptive learning technology is delivered by Knewton, which has partnerships with a wide range of academic publishers to incorporate adaptive learning technology into collections of published works.  Other good examples include an IBM – Skillsoft collaboration and the language learning app Duolingo.
  8. Gamification – Gamification is the use of techniques and platforms from computer and online gaming for purposes of education or problem solving. It is based on the recognition that computer gaming in all its forms (e.g. online, PC, consoles, casual, etc.) have tremendous reach in terms of usage, and the concomitant technologies of simulation, communication, shared objects, and data can be used to enhance learning.   Duolingo’s home page, mentioned above, proudly announces “Gamification poured into every lesson.”
  9. Knowledge Representation – Based on a long history as a research field in Artificial Intelligence, Knowledge Representation (KR) focuses on machine readable representation of knowledge such as facts, definitions, relationships, causal factors, and implications. Typically KR systems are able to use a set of facts and knowledge to derive new relationships or knowledge via a reasoning or inference system.  In an educational context, KR has been used to create question answering capability, derive summaries of material, and form the basis for automated tutoring systems.
  10. Cognitive Assistance – Learning support provided by computer-based reasoning, usually manifest as personalized guidance for learners in the form of responses to individual queries.   Cognitive assistance is nicely illustrated in higher education by Inquire, an intelligent textbook app developed and demonstrated by SRI International.

New and re-purposed language are common in times of rapid innovation.   We’ve addressed these before (see an earlier post on “platforming”) and in the next week we’ll post our top new ten terms for collections-based decision support and digital knowledge management, such as we are seeing in or from publishers such as Reed Elsevier or O’Reilly Media.

What We Should Learn from the Ebola Outbreak

Ebola doesn’t spread by mysterious means, we know how it spreads. So we have the means to stop it from spreading, but it requires tremendous attention to every detail.

 Tom Frieden, director, U.S. Centers for Disease Control and Prevention, August 25, 2014

The current West African Ebola crisis brings into sharp relief the challenges of pre-service and real-time workplace education for health workers in areas of greatest need.  Countries dealing with the Ebola crisis face multiple challenges, chief among them a shortage of trained and adequately equipped health workers.

Anthony Fauci, director, U.S. National Institute of Allergy and Infectious Disease, made this point in an August 13, 2014 article in The New England Journal of Medicine, titled Ebola — Underscoring the Global Disparities in Health Care Resources:

[T]he chance that the virus will establish a foothold in the United States or another high-resource country remains extremely small…[hospitals] generally have excellent capacity to isolate persons with suspected cases and to care for them safely should they become ill. Public health authorities have the resources and training necessary to trace and monitor contacts. Protocols exist for the appropriate handling of corpses and disposal of biohazardous materials. … Isolation procedures have been clearly outlined by the Centers for Disease Control and Prevention (CDC). A high index of suspicion, proper infection-control practices, and epidemiologic investigations should quickly limit the spread of the virus.

While some of the disparity can be alleviated by providing equipment and supplies, the heart of the problem lies in the breadth and depth of education and on-the-job decision support for health care workers.  High-resource countries are distinguished, in Dr. Fauci’s article, by their capacity to respond which is based largely on sound “training,” “protocols,” and “procedures.”

While there is no single remedy for global disparities in health care, the current Ebola outbreak draws our attention to the need for expanded deployment of the new generation of digital educational and decision support technologies, and in building related communities of practice.  These decision support and educational technologies have already revolutionized health care delivery in the United States and other high-resources countries (see July 16 post featuring

Finally, the current crisis in West Africa represents an opportunity to identify the most important elements of decision support for policymakers, program leaders, and frontline health workers and, to determine the most context-appropriate delivery systems for advanced preparation and real-time support in future outbreaks of viral hemorrhagic fever.