In the History of Education, as in many other arenas, the Devil is in the Details — not only the Devil, also the opportunity to understand what works under what circumstances for what purposes. Take a look at Audrey Watters’ excellent blog on The Invented History of ‘The Factory Model of Education’. She brings us an informative and long view recap of school development and puts the use of the ‘factory model’ metaphor in context. Audrey’s thesis and critique of expectations that computing can somehow ‘automate’ education are echoed in Marc Guzdial’s May 25th blog.
From these writers and their predecessors we learn that most of the objectives touted by educational reformers today have been part of the discourse for thousands of years — our goals are not new. We are still addressing issues of compulsory vs. free choice schooling, play and games to motivate deep learning, efficiencies offered by presenting the same didactic lessons to masses of students, the role of schooling in promoting ‘industrious’ behavior and reducing idleness, and the concept of matching methods of instruction to the characteristics of the learner (whether age, personality, learning style, sociocultural background, or individual interest).
All of these topics came together and were hotly debated when personal computing burst upon the scene, the period highlighted by HCLE*. Why are we still mired in arguments about whether or not our school systems should engage in ‘mass production’ and whether ‘personalization’ is important in teaching? Ubiquitous computing (which in turn enables mass information storage and telecommunications) has created a paradigm shift that lets us respond “yes” to all choices. We can now provide ‘mass instruction’ via MOOCs, permit learners to ‘play’ in a cafeteria of YouTube lessons and open educational resources. We can convene social groups we call ‘classrooms’ and track the progress of solitary learners studying in remote corners.
So why are these surmountable details keeping us in turmoil over how to educate our children and provide adults with continuous opportunities to acquire new skills and information? I think the answer lies in our desire for a single solution, a ‘universal’, ‘unified’ system of education. The potential of computing to let us offer ‘different strokes to different folks’ brings us face to face with our deep desire for uniformity – our inner wish to be surrounded by people who share the same hopes, goals, values, interests and taboos that each of us holds dear. It’s hard work to discover what turns on an individual child. It’s challenging to build models of a future society and anticipate what our youngsters will need to learn early in their lives in order to thrive in whichever model comes to pass. In this age of labor-saving devices we need to do the mental labor of studying exactly how to craft learning environments which take advantage of the strengths and interests of each individual while providing exercise equipment to strengthen individual weaknesses. Is it our fear of unleashing a Pandora’s Box of diverse people and cultures that keeps us from jumping into the educational revisiting that computing makes possible?
The History of Computing in Learning and Education Project provides a snapshot of an historical period in which many experiments were done and much data was generated about both cognitive and physical abilities of learners. This museum will help us to recapture the lessons of the past. But that isn’t enough. We need to move forward, to exploit the capacity of our present technologies by attending to the details, not the generalizations, of learning. Just as we can now mass produce and distribute clothing and cars, we can now mass produce and distribute the information, facts and basic skills that ‘factory model’ schools were invented to disseminate.
Our new challenge is to harness computing technologies, coupled with excellent teachers filling many new roles, to customize our educational offerings for each individual learner. Assessing the diversity of interests, talents, experience, goals and cultures each learner brings into the educational nexus results in a complex model of the individual student. This is a ‘big data’ problem we can now handle if only we can figure out what factors to track. Understanding how to pair the learner with the most effective stimuli to foster engagement, problem-solving, creativity and emotional well-being will require a huge amount of new and detailed educational research. In the end we may discover that learners themselves are best able to select their own educational experiences.
As observed by our fellow bloggers at the beginning of this essay, the questions we are asking about education today, as well as the criticisms we are making, are not new. Even highly personalized education has been going on in the form of one-to-one teaching and learning dyads for centuries. The change brought about by modern computing is the potential to implement such old solutions on a planetary scale. It won’t be easy. That’s why we’re still struggling with it after 50 years of personal computing. But we can succeed — if — we are willing to address the Devil in the Details.
*History of Computing in Learning and Education, 1960 to 1990