The 70:20:10 learning principle is golden with new holistic knowledge ecosystems (HKEs). Unlike conventional eLearning systems that lecture at users with words, graphics, and videos, HKEs allow eLearners 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 is a 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 that evolves as relevant data, subject matter content, and analytic insights morph the ecosystem, mirroring real-world knowledge environments. End users find the learning experience engaging and rewarding because they can query the knowledge ecosystem anytime, anywhere, to learn vital organization-centric knowledge within seconds.
What is also different with new holistic knowledge ecosystems is that they require a different kind of modeling expertise from that of instructional designers. That expertise is called “knowledge engineering,” a title that in general seems to many instructional designers to be overly structured and less creative. They may have a point, because knowledge engineering is about faithfully modeling existing structural, behavioral, functional, and analytic knowledge without the embellishments, while instructional design is about translating those domains of knowledge into creative expressions that are intended to make learning more 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 resolved
When walking into a customer’s office, both knowledge engineers and instructional designers have similar goals—that is, to determine the knowledge domain, function, or task that needs to be communicated and taught. The knowledge engineers, trained in the art and science of identifying, decomposing, and modeling most every form of how, why, and what-if knowledge, want to know what the requirements are for the project and what resources are currently available to support those requirements. They then need to know where 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 every possible academic, commercial, and social discipline, such as biological and chemical scientists, R&D engineers, data scientists, geophysicists, doctors, dentists, lawyers, financial analysts, accountants, and all 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 requirements and resource-gathering procedures as knowledge engineers. However, unlike knowledge engineers who are more concerned about faithfully modeling knowledge as used by subject matter experts, instructional designers 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 any knowledge-modeling methodology that works has value.
It is just that knowledge engineers lean more toward the subject-matter-expert side of the dichotomy and instructional designers lean more toward the creative-communication side. Given the widely held dissatisfaction with conventional learning management systems (LMSs), we must conclude that the time has come to look deeper into how knowledge engineering and instructional design disciplines can together solve the current LMS dissatisfaction problem.
Holistic knowledge ecosystems
New 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 modeled knowledge is consistently distributed for use by employees, consultants, suppliers, and customers. In addition, these systems are designed to accommodate many eLearning materials that have been developed on third-party applications. Everybody wins.
Likewise, holistic knowledge ecosystems provide the means for modeling disciplines to address the requirements of subject matter experts and new initiates alike. HKEs do this by using the same language and contextual formats that each class of user knows and understands. Likewise, associated scientific, engineering, and general knowledge content developed by knowledge engineers or instructional designers can be easily integrated anywhere within these ecosystems. Given this, we may be at the starting point where the combined talents of knowledge engineers and instructional designers can break through the LMS “dissatisfaction barrier” that has disappointed chief learning officers for more than a decade.
The 70:20:10 principle represents a learner-centric, action-oriented approach
The 70:20:10 principle is an essential criterion of holistic knowledge ecosystems. This principle places the emphasis on lessons-learned knowledge and its application within the real world. The most advanced systems 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 occurs while the knowledge ecosystem’s eLearning, eMentoring function guides the learner through the execution steps. As this occurs, the deepest form of learning takes place, and thus it is gained more quickly and retained longer.
To illustrate this point: In 1990, the year after the Berlin wall came down, the US government maintained more than 80 science and technology laboratories (S&Ts). These S&Ts, along with most research universities across the country, warehoused America’s knowledge in the brains of subject matter experts and in the manuals they wrote. Each discipline represented within the S&Ts was aggressively advancing its 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 down most of the S&Ts. As this process occurred, the research records and manuals that recorded each S&T’s activities were recovered and modeled using a very early HKE. What was discovered, once these resource materials were modeled and tracked through the knowledge distribution chain, was that the knowledge within the S&T resources was not, for the most part, being translated and implemented within deployable projects. That was because project managers did not understand the specialized language of the scientists and engineers who had created the inventions. The cost to US taxpayers was in the billions of 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 as its capacity to effectively communicate across a wide spectrum of organizational roles. Figure 1 is the science and technology knowledge base model map that was part of a project, “Rational Baseline Analysis of Science & Technology Source Documents,” developed by the late Dr. Richard L. Ballard along with Martha Nawrocki (co-author of this article) while they were with a now-defunct company called Knowledge Research.
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 the LMS grows. Acquiring and managing all an organization’s knowledge can be daunting, but with the right technology, it can be accomplished.
When deep knowledge is made instantly available to answer questions about functions or tasks as they occur, 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 it can reliably transfer both relevant, real-time operational and deep subject-matter-expert knowledge within seconds. 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-language knowledge-based products. Solve this problem, solve the real-time eLearning riddle facing the global enterprise market.
70:20:10 learning solution goal
From the perspective of the authors, the 70:20:10 principle, as defined by Morgan McCall and others, is not just another label to describe a learning approach; it is a practical manifesto that echoes knowledge science and cuts through all the noise to present a clear and decisive learning principle for understanding what constitutes how best knowledge is transferred and learned. As such, it presents a standard that holistic 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 other practical learning models such as those by Dr. Ballard in what he called “theory-based semantics.” This approach states that we learn through enculturation and education, and from our perspective, also from lived experience and deep analytical thought.