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70:20:10 Holistic Knowledge Ecosystems

by Dennis Thomas, Martha NawrockiFebruary 28, 2017

The 70:20:10 learning principle is golden with new holistic knowledge ecosystems (HKEs). Unlikeconventional eLearning systems that lecture at users with words, graphics, and videos, HKEs alloweLearners to experience learning processes as they navigate through proprietary, real-world, organization-centric knowledge ecosystems. The learning experience is both conscious and subconscious. But there isa catch.

New HKE technologies are holistic, requiring that knowledge domains—such as organizations,departments, projects, job functions, and their tasks—be faithfully modeled to achieve the highest results.When properly modeled, what remains is a living, breathing, digitized holistic knowledge ecosystem thatevolves as relevant data, subject matter content, and analytic insights morph the ecosystem, mirroringreal-world knowledge environments. End users find the learning experience engaging and rewardingbecause they can query the knowledge ecosystem anytime, anywhere, to learn vital organization-centricknowledge within seconds.

What is also different with new holistic knowledge ecosystems is that they require a different kind ofmodeling expertise from that of instructional designers. That expertise is called “knowledge engineering,” atitle that in general seems to many instructional designers to be overly structured and less creative. Theymay have a point, because knowledge engineering is about faithfully modeling existing structural,behavioral, functional, and analytic knowledge without the embellishments, while instructional design isabout translating those domains of knowledge into creative expressions that are intended to make learningmore effective and engaging.

Thankfully, there is a middle ground, so neither side must yield. Let’s explore this further.

The knowledge engineer/instructional designer dichotomy resolvedWhen walking into a customer’s office, both knowledge engineers and instructional designers have similargoals—that is, to determine the knowledge domain, function, or task that needs to be communicated andtaught. The knowledge engineers, trained in the art and science of identifying, decomposing, and modelingmost every form of how, why, and what-if knowledge, want to know what the requirements are for theproject and what resources are currently available to support those requirements. They then need to knowwhere those resources are located and which subject and operational experts can answer both their deep-knowledge questions and their higher operational questions.

The definition of subject matter experts: Scientists, engineers, and advanced practitioners related to everypossible academic, commercial, and social discipline, such as biological and chemical scientists, R&Dengineers, data scientists, geophysicists, doctors, dentists, lawyers, financial analysts, accountants, andall other professionals who apply their hard-gained education and expertise to every form of commercial,government, and academic concern.

Instructional designers come in contact with these same experts, and they follow the same requirementsand resource-gathering procedures as knowledge engineers. However, unlike knowledge engineers whoare more concerned about faithfully modeling knowledge as used by subject matter experts, instructionaldesigners have greater interest in applying their talents to addressing the psychological makeup,motivations, learning traits, and so forth of targeted learners. Neither approach is right or wrong, since anyknowledge-modeling methodology that works has value.

It is just that knowledge engineers lean more toward the subject-matter-expert side of the dichotomy andinstructional designers lean more toward the creative-communication side. Given the widely helddissatisfaction with conventional learning management systems (LMSs), we must conclude that the timehas come to look deeper into how knowledge engineering and instructional design disciplines can togethersolve the current LMS dissatisfaction problem.

Holistic knowledge ecosystemsNew holistic knowledge ecosystem technologies present a powerful alternative to conventional LMSs.These systems are designed to faithfully simulate the lessons-learned structural, behavioral, functional,and analytic knowledge of headquarters, departments, projects, job functions, and tasks so that modeledknowledge is consistently distributed for use by employees, consultants, suppliers, and customers. Inaddition, these systems are designed to accommodate many eLearning materials that have beendeveloped on third-party applications. Everybody wins.

Likewise, holistic knowledge ecosystems provide the means for modeling disciplines to address therequirements of subject matter experts and new initiates alike. HKEs do this by using the same languageand contextual formats that each class of user knows and understands. Likewise, associated scientific,engineering, and general knowledge content developed by knowledge engineers or instructional designerscan be easily integrated anywhere within these ecosystems. Given this, we may be at the starting pointwhere the combined talents of knowledge engineers and instructional designers can break through theLMS “dissatisfaction barrier” that has disappointed chief learning officers for more than a decade.

The 70:20:10 principle represents a learner-centric, action-oriented approachThe 70:20:10 principle is an essential criterion of holistic knowledge ecosystems. This principle places theemphasis on lessons-learned knowledge and its application within the real world. The most advancedsystems teach eLearners, on the spot, how to effectively apply the knowledge they need to learn, at the

same time they apply that knowledge to solve problems. It is a synergistic learning experience that occurswhile the knowledge ecosystem’s eLearning, eMentoring function guides the learner through the executionsteps. As this occurs, the deepest form of learning takes place, and thus it is gained more quickly andretained longer.

To illustrate this point: In 1990, the year after the Berlin wall came down, the US government maintainedmore than 80 science and technology laboratories (S&Ts). These S&Ts, along with most researchuniversities across the country, warehoused America’s knowledge in the brains of subject matter expertsand in the manuals they wrote. Each discipline represented within the S&Ts was aggressively advancingits projects and invention cycles with great intent and purpose.

At that time, since the Cold War had been declared officially over, the government decided to close downmost of the S&Ts. As this process occurred, the research records and manuals that recorded each S&T’sactivities were recovered and modeled using a very early HKE. What was discovered, once theseresource materials were modeled and tracked through the knowledge distribution chain, was that theknowledge within the S&T resources was not, for the most part, being translated and implemented withindeployable projects. That was because project managers did not understand the specialized language ofthe scientists and engineers who had created the inventions. The cost to US taxpayers was in the billionsof dollars, and as the S&Ts shut down, most of the paid-for knowledge was lost forever.

By extension, the S&T study led to the conclusion that a successful knowledge system is only as useful asits capacity to effectively communicate across a wide spectrum of organizational roles. Figure 1 is thescience and technology knowledge base model map that was part of a project, “Rational Baseline Analysisof Science & Technology Source Documents,” developed by the late Dr. Richard L. Ballard along withMartha Nawrocki (co-author of this article) while they were with a now-defunct company called KnowledgeResearch.

Figure 1: Science and technology knowledge base model map

As the users/learners become more numerous and diverse, the complexity and number of demands on theLMS grows. Acquiring and managing all an organization’s knowledge can be daunting, but with the righttechnology, it can be accomplished.

When deep knowledge is made instantly available to answer questions about functions or tasks as theyoccur, knowledge workers will happily use those technologies. Smartphones are a perfect example.Whether for communication via voice, text, email, Twitter, Facebook, or hundreds of other apps,smartphones win hands-down. As a system’s reliance increases, so does the ROI on that technology.

For this reason, the most important criterion when choosing a holistic knowledge ecosystem is whether itcan reliably transfer both relevant, real-time operational and deep subject-matter-expert knowledge withinseconds. This huge requirement places an almost impossible demand on conventional technologies,which are not designed for deep knowledge modeling or the layering of multi-country, multi-languageknowledge-based products. Solve this problem, solve the real-time eLearning riddle facing the globalenterprise market.

70:20:10 learning solution goalFrom the perspective of the authors, the 70:20:10 principle, as defined by Morgan McCall and others, isnot just another label to describe a learning approach; it is a practical manifesto that echoes knowledgescience and cuts through all the noise to present a clear and decisive learning principle for understandingwhat constitutes how best knowledge is transferred and learned. As such, it presents a standard thatholistic knowledge ecosystems must fulfill to be effective. This principle, again, is as follows:

70 percent from performing challenging functions and tasks (life experience)20 percent from developmental relationships (mentoring by subject matter experts and peers)10 percent from coursework and training (formal and self-directed learning)

The 70:20:10 learning model has been challenged by academics, but its principles resonate with otherpractical learning models such as those by Dr. Ballard in what he called “theory-based semantics.” Thisapproach states that we learn through enculturation and education, and from our perspective, also fromlived experience and deep analytical thought.

ConclusionKnowledge is the lifeblood of every individual and organization. To the degree that knowledge is relevantand made available to stakeholders in a language they can understand, they will advance their personalgoals and objectives, and the goals and objectives of their organization. Without relevant, understandableknowledge, neither can succeed.

Topics Covered

Design Strategies, Emerging Topics, Management, Training Strategies

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