Few empirical studies have examined the relationship between learning organization dimensions and nonprofit performance. Susan McHargue’s study was conducted to understand this relationship and how it impacts nonprofit organizations’ ability to become nonprofit learning organizations. The results offer guidance to human resource developers and managers who desire to integrate learning organization concepts into nonprofit organizations.
Conceptual and performance model
Source: McHargue, S.K., 2003. Learning for performance in nonprofit organizations. Advances in Developing Human Resources 5, 196–204.
A learning organization is an organization that has an enhanced capacity to learn and change.
Watkins and Marsick dimensions of a learning organization
Source: Watkins, K.E., Milton, J., Kurz, D., 2009. Diagnosing the learning culture in public health agencies. International Journal of Continuing Education & Lifelong Learning 2.
“In a knowledge economy, the flow of knowledge is the equivalent of the oil pipe in an industrial economy. Creating, preserving, and utilizing knowledge flow should be a key organizational activity.” – George Siemens, Knowing Knowledge (2006)
Photo: Oil Pipeline Pumping Station in rural Nebraska (Shannon Ramos/Flickr)
This is the third in a three-part presentation about learning strategy for development and humanitarian organizations. It was first presented to the People In Aid Learning & Development Network in London on 27 February 2014.
Burck Smith describes how accreditation is based primarily on a higher education institution’s inputs rather than its outcomes, and creates an “iron triangle” to maintain high prices, keep out new entrants, and resist change.
To be accredited, a college must meet a variety of criteria, but most of these deal with a college’s inputs rather than its outcomes [emphasis mine]. Furthermore, only providers of entire degree programs (rather than individual courses) can be accredited. And even though they are accredited by the same organizations, colleges have complete discretion over their “articulation” policies—the agreements that stipulate the credits that they will honor or deny when transferred from somewhere else. This inherent conflict of interest between the provision of courses and the certification of other’s courses is a powerful tool to keep competition out. Articulation agreements, like API’s for computer operating systems, are the standards that enable or deny integration. In short, by controlling the flow of funding, accreditation insures a number of things: All colleges look reasonably similar to each other, the college can’t easily be “disaggregated” into individual courses, and coursework provided by those outside of accreditation can’t easily be counted as credible.
Lastly, to further tip the scales toward incumbent providers, accreditation bodies are funded by member colleges, and accreditation reviews are conducted by representatives from the colleges themselves. The “iron triangle” of input-focused accreditation, taxpayer subsidies tied to accreditation, and subjective course articulation ensures that almost all of the taxpayer funds set aside for higher education flows to providers that look the same. And by keeping innovations out, colleges can maintain their pricing structures [emphasis mine].
This explains why most online courses are priced the same or higher than face-to-face courses despite massive cost efficiencies. Such enormous profit margins available to the delivery of accredited online learning explains the quick growth of for-profit colleges, nonprofit colleges offering online degree programs in conjunction with private-sector providers who share in tuition revenue, and colleges running separate online divisions that subsidize face-to-face operations.
A more accurate characterization of today’s higher education is that individual colleges offer online learning as a “feature,” but use their regulatory clout as a group to resist disruption.
My own practice (and no doubt yours) has been shaped by many different learning theorists. George Siemens, for me, stands out articulating what I felt but did not know how to express about the changing nature of knowledge in the Digital Age. Below I’ve compiled a few of my favorite excerpts from his book Knowing Knowledge, published in 2006, two years before he taught the first Massive Open Online Course (MOOC) with Alec Couros and Stephen Downes.
Learning has many dimensions. No one model or definition will fit every situation. CONTEXT IS CENTRAL. Learning is a peer to knowledge. To learn is to come to know. To know is to have learned. We seek knowledge so that we can make sense. Knowledge today requires a shift from cognitive processing to pattern recognition.
Construction, while a useful metaphor, fails to align with our growing understanding that our mind is a connection-creating structure. We do not always construct (which is high cognitive load), but we do constantly connect.
We learn foundational elements through courses…but we innovate through our own learning.
The changing nature of knowledge
The Achilles heel of existing theories rests in the pace of knowledge growth. All existing theories place processing (or interpretation) of knowledge on the individual doing the learning. This model works well if the knowledge flow is moderate. A constructivist view of learning, for example, suggests that we process, interpret, and derive personal meaning from different information formats. What happens, however, when knowledge is more of a deluge than a trickle? What happens when knowledge flows too fast for processing or interpreting?
Knowledge has broken free from its moorings, its shackles. Those, like Francis Bacon, who equate knowledge with power, find that the masses are flooding the pools and reservoirs of the elite. […] The filters, gatekeepers, and organizers are awakening to a sea of change that leaves them adrift, clinging to their old methods of creating, controlling, and distributing knowledge. […] Left in the wake of cataclysmic change are the knowledge creation and holding structures of the past. The ideologies and philosophies of reality and knowing—battle spaces of thought and theory for the last several millennia—have fallen as guides.
Libraries, schools, businesses—engines of productivity and society—are stretching under the heavy burden of change. New epistemological and ontological theories are being formed, as we will discuss shortly with connective knowledge. These changes do not wash away previous definitions of knowledge, but instead serve as the fertile top of multiple soil layers. […]
Or consider email in its earlier days—many printed out a paper copy of emails, at least the important ones, and filed them in a file cabinet. Today we are beginning to see a shift with email products that archive and make email searchable and allow individuals to apply metadata at point of use (tagging).
Knowledge has to be accessible at the point of need. Container-views of knowledge, artificially demarcated (courses, modules) for communication, are restrictive for this type of flow and easy-access learning.
Everything is going digital. The end user is gaining control, elements are decentralizing, connections are being formed between formerly disparate resources and fields of information, and everything seems to be speeding up.
“Know where” and “know who” are more important today that knowing what and how.
Once flow becomes too rapid and complex, we need a model that allows individuals to learn and function in spite of the pace and flow.
We need to separate the learner from the knowledge they hold. It is not really as absurd as it sounds. Consider the tools and processes we currently use for learning. Courses are static, textbooks are written years before actual use, classrooms are available at set times, and so on.
The underlying assumption of corporate training and higher education centers on the notion that the world has not really changed.
But it has. Employees cannot stay current by taking a course periodically. Content distribution models (books and courses) cannot keep pace with information and knowledge growth. Problems are becoming so complex that they cannot be contained in the mind of one individual—problems are held in a distributed manner across networks, with each node holding a part of the entire puzzle. Employees require the ability to rapidly form connections with other specialized nodes (people or knowledge objects). Rapidly creating connections with others results in a more holistic view of the problem or opportunity, a key requirement for decision making and action in a complex environment.
How do we separate the learner from the knowledge? By focusing not on the content they need to know (content changes constantly and requires continual updating), but on the connections to nodes which continually filter and update content.
Here is what the connectivism implementation cycle looks like as a mind map. (Click on the image to download the PDF).
What is the unknown? What are the data? What is the condition?
Is it possible to satisfy the condition? Is the condition sufficient to determine the unknown? Or is it insufficient? Or redundant? Or contradictory?
Draw a figure. Introduce suitable notation.
Separate the various parts of the condition. Can you write them down?
Devising a plan
Second. Find the connection between the data and the unknown. You may be obliged to consider auxiliary problems if an immediate connection cannot be found. You should obtain eventually a plan of the solution.
Have you seen it before? Or have you seen the same problem in a slightly different form?
Do you know a related problem? Do you know a theorem that could be useful?
Look at the unknown! And try to think of a familiar problem having the same or a similar unknown.
Here is a problem related to yours and solved before. Could you use it? Could you use its result? Could you use its method? Should you introduce some auxiliary element in order to make its use possible?
Could you restate the problem? Could you restate it still differently? Go back to definitions.
If you cannot solve the proposed problem try to solve first some related problem. Could you imagine a more accessible related problem? A more general problem? A more special problem? An analogous problem? Could you solve a part of the problem? Keep only a part of the condition, drop the other part; how far is the unknown then determined, how can it vary? Could you derive something useful from the data? Could you think of other data appropriate to determine the unknown? Could you change the unknown or data, or both if necessary, so that the new unknown and the new data are nearer to each other?
Did you use all the data? Did you use the whole condition? Have you taken into account all essential notions involved in the problem?
Carrying out the plan
Third. Carry out your plan.
Carrying out your plan of the solution, check each step.
Can you see clearly that the step is correct?
Can you prove that it is correct?
Looking Back
Fourth. Examine the solution obtained.
Can you check the result? Can you check the argument?
Can you derive the solution differently? Can you see it at a glance?
Can you use the result, or the method, for some other problem?
Summary taken from G. Polya, “How to Solve It”, 2nd ed., Princeton University Press, 1957, ISBN 0–691–08097–6.
