The future of education
For more than a decade, this column has explored many of the challenges encountered when building knowledge-based enterprises. High on the list is building and sustaining an educated and engaged workforce. Education not only drives much of our modern economy, it plays a key role in shaping our scientific, political, social, and cultural discourse.
Most people accept the notion that our education system is a product of the Industrial Age. And, as with any large legacy institution, systemic reform is difficult and must come from both within and outside the system.
The needed change starts with a basic understanding of the difference between formal and natural systems. Formal systems are complicated (think Industrial Age automation); natural systems are complex (think human behavior).
Many of the tools needed to drive change are already in place. But a lack of understanding of the categorical distinctions between formal and natural systems is why these tools are often either misapplied or not used at all. Once these categorical distinctions become better understood, we can begin the reform process.
With recent advances in behavioral neuroscience and technology, we have an opportunity to contribute to an emerging social movement in which mass education is transformed into individualized learning. The following three changes will drive this transformation. Each contrasts a formal, mechanized, assembly-line approach with one that is consistent with our natural human capacity for deep, lifelong learning.
Change #1: Less rote memorization; more self-discovery
In the near-future, educators will base then actions less on B. F. Skinner-style reinforcement psychology and base them more on recent research in behavioral neuroscience. In this new pedagogy, learning occurs at a memory engram level. Although invisible, engrams are the most fundamental elements of human memory. When aggregated according to a set of underlying principles acquired over time, engrams are molded into thoughts, ideas, and behaviors in much the same way that the laws of valence combine atoms into chemical compounds. The difference is that combining human memory engrams is not a fully logical process, but rather, a natural process.
Today’s world places demands on the knowledge worker to solve complex problems in shorter periods of time, as opposed to simply regurgitating facts and memorizing formulas (something computers can do better than most humans anyway). Grading a student’s performance based on “getting the right answer” provides narrow and misleading metrics based on a “snapshot” at a single moment in time.
In contrast, we’ve seen remarkable success, particularly with struggling high school and college mathematics students, by giving them freedom to select a topic, then thinking, reflecting, and writing about it, using blank, unlined paper and pencil. We call this “writing higher mathematics to learn higher mathematics.” (Albert Einstein made extensive use of a note pad and pencil during much of his thought process.) Such a process cycle, e.g., internalization followed by written expression, activates learning at an engrammic level. Deep learning pedagogy based on this cycle increases a student’s capacity for problem-solving in an environment of increasing speed, complexity, and novelty.
Change #2: Less normalization; more individualization
By normalization we mean grouping students into broad categories and constraining each to adhere to a standardized curriculum. Many business, scientific, and artistic geniuses of the past century openly rebelled against such forced grouping, sometimes flunking or dropping out of mainstream educational institutions. Their discoveries often occurred after stepping away from the crowd and connecting dots in ways that defied conventional wisdom.
A methodology we’ve prototyped using McGraw-Hill’s ALEKS system imitates this more fluid process. Using standards such as Common Core, a curriculum is represented as an ontology, or “learning space,” which provides greater flexibility in how topics are organized and expressed. In this way, each student establishes his or her own “sense of location” within a curriculum.
Working together, the system and the instructor create individualized learning pathways through the ontology which the student traverses based on the topics he or she is comfortable with versus those he or she is not comfortable with. The student becomes aware of location and expresses internalized concepts in writing. This reveals not only skill levels, but also learning behaviors not visible in conventional testing. Through the use of a homework platform, the instructor, aided by algorithms, assesses progress and makes recommendations for continuing along or modifying a particular learning pathway.
Assembly-line rigidity, in which learning is compressed into rigid “time boxes,” is replaced by “focus-on/focus-off” exercises that allow a student to shift attention more frequently. This creates a two-sided semantic web, with the individual student in the middle. This builds strong connections between short-term and long-term memory. Learning becomes more deeply internalized.
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