Learning Model Attributes

Learning Model Attributes

I had a briefing this morning with the education technology provider and publisher Cengage. During the discussion, I brought up a few of the things I have learned while speaking on education over the last couple of months. I want to reiterate those here because I think they are important elements of any learning model that we eventually develop.

Before we can start discussing the use of analytics in learning, we must create a standard data model that captures all of the necessary learning attributes, including the subject, the approach, the specific content, the learning outcomes, etc. I plan to get much more definitive on this over the next several months as I work to develop such a model, but let me illustrate two examples:

Feedback loops. Let’s say a middle school students learns about the universe from a basic science text that is 5 years old. For all practical purposes, neither the student, nor the universe,  will be negatively affected by any factual errors found in the text. Outside of missing a Jeopardy about the age of the universe (the next says 12.5 billion years – current estimates report it to be more than a billion years older), gravity will still exert its force and planets will orbit and comets will heat near the sun and shed their ancient ice and dust, and super novae will explode and the cosmic background radiation will continue to fade. But not everything is so certain. What we know about dark matter and dark energy evolves daily. The reconciliation between gravity and quantum mechanics remains unclear. If the universe emerged from a singularity or from the ripple of two Branes skipping by one another is also unknown.

Theories abound that illustrate the scientific method at its best, grabbling with uncertainty, asserting a hypothesis and testing it against other theories and experimental outcomes. In astronomy and cosmology, in biology and environmental science, much of what we think we know (as individuals) comes from assertions of the moment that we are seldom forced to reconsider unless we subject ourselves to a new learning experience. And if astronomy isn’t what we plan to pursue, we probably choose not to entertain such an experience.

But what I just wrote, about that wonderful experience of unknown and unknowing, the struggle to find the pattern in the noise of history, that is relevant to everything from strategy to accounting, from factory operations to nurturing customer relationships. Our learning model, however, has no way to check the past against the current—no way to say that this student used this book and learned this concept which has now changed, and not only should we tell them that the fact now has a new value, but we should share with them the path that led to that new value. In the information age and the knowledge economy, conceptual feedback loops are essential to staying current, and a wonderful ways of teaching learners about bias—feedback loops for people to confront the source of bias, deconstruct it and incorporate both agile, critical thinking and skepticism into their mental models.

Contextual continuity. For those who read my thoughts on education regularly, you know I am not an advocate of gates. I think we create artificial entry and exit points for learning that mean more to the learner than a moment of celebration (which I think is fine)—on the downside, they signal the end of some phase of learning, and the beginning of another. And the message is ultimately interpreted as the last gate, be it high school or college, undergraduate or professional doctorate, that when one is done, one is done. Of course, doctors and lawyers know that isn’t the case (driven by professional and regulatory strictures), but life long learning is not a mantra taught in K-12, and it needs to be. Everyone needs to continue to learn, and to relearn—this the contextual continuity. If I am taught something in one grade, the next person who teachers me something similar should not just know, intellectually or conceptually, about what I was supposedly taught, but specifically what I learned. I was given an example recently of a grant provided to an middle school for dissecting squid. As far as records go, those students learned biology as part of their standard curriculum. A future high school biology teacher will not know, unless he or she asks, if they students have any past, hands on dissection experience—and he or she will know even less about about the intention of the activity that lead to the experience, even after asking the learners. Any model that we build needs to be specific and contextual. It needs to capture the actual learning that takes place, as well as its context, methods and materials. And our learning experiences need to cross over these artificial gates. One time grants for one set of students may be wonderful for the teacher and the student, but they don’t lead to systemic shifts in experience—and they don’t lead to knowledge that can be built upon by future educators, because those educators are ignorant of the learning that has taken place. Learning should be an end-to-end experience, which not only means a model, and I am not suggesting we over engineer the experience, but that we capture the goals, the objectives, the methods, the outcomes, the facts in play. With everything digital, we can create a model of learning experiences that both learners and educators can connect with over the time learners are engaged with formal education (and the learners can turn into a life-long resource). And then of course, we have to given the learners and educators time to take advantage of the insights such a system may have to offer.

The ultimate model of learning is probably years away, but its creation should be a public and private investment that drives strategic dialog today. How we reinvent learning will be determined very much by how we determine its value and measure its outcomes. If we continue to determine value and measure outcomes with industrial age, linear thinking, then we will create efficiency without being effective – and when I hear about budget cuts and trade-offs between salaries and text books, I fear we do not understand the value of education as we plan its evolution – and I want to be clear, it is not that we have lost our way, but that we have always funded education without a clear consensus about its goals and objectives. Today multiple models exist, and those multiple models, from vocation to personal intellectual growth, will likely persist. They cannot be measured by the same means, nor judged by the same outcomes, which means we not only need a model, but we need competing models so that people can make rational choices about which educational system best aligns with their perceived needs.

Daniel W. Rasmus

Daniel W. Rasmus, Founder and Principal Analyst of Serious Insights, is an internationally recognized speaker on the future of work and education. He is the author of several books, including Listening to the Future and Management by Design.

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