This is the first of an n-part series on technology integration into U.S. K-12 learning systems. The “n” is still an unknown because of the explosion of narratives – and hundreds of pages of articles and citations -- that issued from asking the question.
But the reader may quickly discern a difference; this treatment doesn’t start with proprietary hardware, or even software. No Internet, whiteboards, professional videoconferencing, iPads, other pads, desktops, laptops, smartphones, clickers, or even more futuristic technology applications in this introduction. The proposition of this series is that those are the last topics and questions to be posed. The objects typically identified as present technologies are the dependent variables in the K-12 technology equation, not its explanatory variables.
Another caveat is, that if one is looking for a vending machine for mass distribution of pre-packaged learning technologies, be disappointed. The machine is still mostly empty. Indeed, the core challenge is that, metaphorically, the prior decisions to design the product, machines to blend and extrude the product, as well as the machines to package it, haven’t been adequately put in place for U.S. K-12.
State of Technology Integration
Volumes have been spoken on whether American K-12 schools and programs have sufficiently embraced digital applications; then, even when those efforts have been executed, whether present technologies are assets to learning, or distractions, or marginally useful, or too costly for a learning ROI?
In reality, there has been enough field research conducted over the last few decades, though not at the necessary level of specificity, to suggest some higher order answers. There is great risk of over-simplifying their outcomes, but for brevity, an assessment is that most studies found some positive contributions from overall technology adoption, but not that “knock your socks off” finding that routinely changes beliefs and causes brand-switching.
Partially explaining that temperate assessment, it has also been rare to find throughout public K-12 systems instances where digital technologies were embraced normatively as they should have been; too frequently pasted on top of traditional classroom rubrics, rarely the foundation for rewrite of learning models, and even more rarely to date put into the hands of teachers properly prepared to use the tools. Education’s desultory approach to its own reform has been some drag in using even extant technologies proven effective in other venues.
U.S. K-12 education is being flogged for its past failure to extend the classroom delivery of learning, knowledge, and creativity to meet dual challenges; growth of information now allegedly doubling every couple of days, and need to create a learning environment and performances with both greater reach and depth, but simultaneously, with less variability regardless of the variances among its human subjects. But operating K-12 has not been principally complicit in failing to pioneer needed technology adaptation for K-12 that could help produce such delivery.
That failure has some mainstream contributors: U.S. schools of education, more deeply embedded in the last century than public K-12 schools, resistant to cross-discipline research; the sources of our various digital technologies, either indifferent to applications of their creativity, or lacking incentives to tackle applications in education versus financial, business, research, industrial, or even public systems’ markets; and the USDOE that dropped the ball when NCLB became the reform theme.
Critical in this view of the challenge, years of digging have revealed no well- organized, definitive models relating digital concepts to the learning chores, nor conceptual frameworks for unfolding what should link the learning environments with those tools. There have been no more than a handful of attempts to go beyond just a favorable technology environment to treat experimentally propositions that specific tools create specific learning gains. In short, public K-12 schools have been handed “stuff” they barely comprehend, subsequently beat on for failure to turn it into learning innovation.
Start by imagining a continuum or thread, at one end the billions of switches capable of indicating 0 or 1, forming via binary arithmetic the capacity to express and store numbers and letters, embedded in hardware that can manipulate and store the results as information, that can be employed to carry out operations using Boolean logic and mathematical expressions, to work on algorithms, formulas, graphic manipulation, transmission of recoverable images, links to the Internet, and creation of output in language and formats amenable to human interface and understanding.
Next, imagine that these capabilities can be applied via computer programming and language rules to address all forms of communications processing and expressions that can be transformed digitally, and via specific models (many existing before the digital revolution) solve equations, maintain databases, execute algorithms, transform and store both numbers and language content, all targeting specific common sense and invented applications, many that were generally part of our knowledge base before processors and computers. Digital processing along with the humanly derived protocols for getting solutions both massively changed the time to get solutions and created a sea change in conceiving what could be solved.
A simple example: Appearing every day in our business, science, or even general press, someone presents a relationship of two variables – your family income (x) and your savings rate (y) – and asserts an association shown graphically and with fit measured by a number (0 – 1) called a correlation coefficient. That coefficient is actually the product of one specific form of associative calculation (in this case called a Pearson product moment r) that is derived by using differential calculus to fit a least squares linear line (minimizing the squared deviations from the line when y is regressed on x) to that x-y data. The points unexplained (not fitted by the line) and their distance from the fitted line become the basis for deriving the r coefficient. Complicate the game a bit; add multiple explanatory variables to predict y. Sixty years ago solving for that (multiple) R with some material number of observations was the product of roughly 12-15 hours of time on a mechanical calculator – in 2012 the solution, with all attendant statistics for the fit, a few seconds. The game’s still pretty much the same, but the game has also been kicked to a new level of explanatory capability.
With all of the latter power the issue of meaning is still more than the statistical model and its assumptions, but that the model is applicable with proper assumptions to literally any human enterprise and knowledge where quantification is possible. In some domains of knowledge, in some environmental setting, populated by some assumed human recipient, the model will suffice and deliver useful information. Critically, it is the latter specification that carries meaning. In the case of technology applied to K-12 education, the game is no different: What basic digital technology, delivering what capacity for explanation, fitting what needs by it recipients, tailored to deliver in what environments, keyed to fit what capabilities and knowledge needs of its human targets, effected by what goal for expression and use of results, delivered at what cost, producing what differentials in performance, permits a given technology to become a challenger to a present methodology for discovery and learning?
Expressed another way, the K-12 technology problem is: To specify technology – along with the logical model or algorithm or rubric – in the form of usable software – fitting the concepts of learning being employed by the system and teacher – fitting the specific needs for functional delivery to a classroom’s students – to be melded with other specific learning methods – that can be executed within the school’s structure, support environment, and cost constraints – to meet expressed learning and assessment goals.
The problem is not one of selecting hardware from an electronics buffet, but of carefully matching the specific utilities of that hardware and software to equally specific learning plans.
Getting a Handle
Clearly there is more than one way to address the K-12 technology issue.
One strategy, that became managerially popular, is just do it – by small experiments and trial and error simply try various digital tools until the better performers are identified. The concept has worked in markets where it may cost more to research needs than can be returned by the segmented product markets, or where products have no link to past customer experiences and they can’t define what they want. Apple under Jobs didn’t do market research on new products, because the market could not have delivered answers. Apple’s present valuation speaks volumes about the question.
If learning needs that could be augmented by technology were universal, with minimal variance, with lead-time and funding to burn, the above is not an unacceptable strategy. American public K-12 does not have either luxury.
Proposed below are just the chapter titles of one approach for methodically sorting technologies for fit to the K-12 classroom:
Need for categorization of digital technologies, extant and on the horizon, based on their differential capacity to deliver an educational service. For example, assess the comparative utility of whiteboard, versus laptop, versus pad, versus smartphone, versus digital projector, versus other even traditional visual display options, in communicating a class of information. For contrast, change the learning task from communicating by telling, to creating an interactive learning situation; the utility of each technology changes.
The above has roots in defining the sensory deliveries that need to accompany the various classroom-learning needs. There is well-defined theory for multi-sensory inputs effecting the speed and quality of learning. Self-evidently, every K-12-relevant technology delivers a different potential for sensory delivery: Auditory, visual, tactile, propriopception, even olfactory and gustatory. A remedial side to this factor is the difficult to diagnose presence of student sensory integration dysfunction; understanding of technologies that can serve as remedies are also part of this need for technology matching.
Need for categorization of digital models and techniques based on their fit to common K-12 models of learning, for example, matching belief in behaviorism, cognitivism, constructivism, or some amalgam. Another is matching Bloom’s Revised Taxonomy. This well-known framework for staging learning in K-12 posits learning stages of remembering, understanding, applying, analyzing, evaluating, and creating. Different technology hardware and digital models or techniques will differentially serve the extended active components of these stages.
Another need, categorization of technologies based on whether lower order memorization and recall, or HOTS (higher order thinking skills) are being targeted. For example, creating HOTS may engage developing taxonomies, knowledge search techniques, database understanding, use of semantic networks, expert systems, collaborative knowledge construction, microworlds and simulation, artificial intelligence, constructivist learning methods, hands-on research, group and socially-networked creativity exercises, and even computer programming proper as a cognitive learning modality.
The specific K-12 environment – school organization, administration, culture, financial model – all become conditions permitting, enhancing, or retarding technology adoption and application; and similarly, specific operating or style properties enforced by the above are bases for the features of adopted technology. These can be anticipated and linked to the specific versions of technology best fitting a specific system.
Lastly, three features of technology effectiveness frequently slip under the radar: One, the significant distinction between the traditional view that the function of the K-12 classroom is communication to, informing students, rather than creating an interactive culture; two, at this time in technology’s and K-12 education’s joint evolution, the client is as much the teacher as the student; and three, germane to debate about the efficacy of standardized testing, the role that technology can play in development of equally (or better and more honest) “standardized tests” that can assess HOTS versus the lowest common denominator of K-12 delivery.
Present reform and standardized testing are working against HOTS development, and it is uncertain whether and how interactive models of real learning can exist coextensively with the drill the former dictates. One hypothesis is that there may be technology combinations that can sufficiently increase the productivity of the low level stages of Bloom’s Taxonomy, and that slack can be created for installing genuine interactive learning in many systems. There is growing evidence based on neural biological and fMRI work that the productivity of low-level performance can be increased, but an empirical experimental platform and organized ways to do school trials don’t yet appear to exist.
The second factor, our K-12 teachers, may dwarf the first. Some empirical experiments with teachers suggest that, not unexpectedly, there are currently more negative beliefs about the efficacy of technology in K-12 than positive ones. Accompanying analysis suggests – also behaviorally not unexpected – that those beliefs are difficult to change, requiring actual successful use of technology to precede and induce attitude and belief change. This puts a premium on the leadership and perspicacity of present public K-12 school administrations, currently not always an auspicious bet, because of both their training and the heavy pressure to meet current mechanical reform objectives.
The technology contribution to assessment is closer to reality than many believe, because of increasing work on expert systems, breakthroughs in artificial intelligence, and the diffusion of knowledge of how to create simulations and serious gaming. If your only connection to gaming is “Angry Birds,” you are missing a large swath of computer experience, with effective education simulation stretching back over a half-century, although limited in application then to available mainframe computer support. A measure of how the game has changed, an eighth-grade grandson is writing game simulations on today’s desktop, which could just as easily be a laptop. The challenge, instead of fixating on present low level standardized testing being isolated by a corporate cabal, is to academically take back the function of test development, make it part of the educational commons, and inject the necessary creativity to develop standardized, technology-assisted testing that covers the full range of learning outcomes.
If it was Easy...
The above broad categories of both education theory and practical factors, impacting which digital technologies are sought along with related models and techniques, are for this post just chapter titles. Each factor needs to be expanded and linked to a comparable expanded classification of digital tools, that linking differentially matched to classroom need. Further, there is the need to factor in availability of complementary learning modes that operate extra-classroom, for example, online learning, third-party capabilities (Harvard’s and MIT’s free curricula, the Kahn Academy learning modules), and parental contributions to formal learning.
The process described may seem complex. Just adopt the KISS principle? For example, simply vend a couple million dollars of iPads to distribute to students in the belief that their use is obvious. In fact, their effective use is conditioned by everything above, plus several hundred education-specific third-party apps, and literally hundreds of thousands of apps (the Apple Store alone vends 500,000 apps) that can range from dysfunctional for K-12 education through brilliant but not intended nor tailored to K-12 use. Connected in specific ways those pads become search, social, and problem-solving networking devices rather than “laptop light.” This still excludes additional custom apps that may need to be developed to serve a particular school’s application.
The issue is no different than the function served by Bloom’s Taxonomy and a generation of thinking it prompted to structure K-12 learning stages and processes. It is arguable that the technology issue, equivalently, has no magic answer – it will take the same level of developmental and HOTS efforts to refine tailored classroom-learning-technology solutions.
Is there a return on the investment in technology and the student engagement and learning it can produce? One seer of renown thought so long before the world became littered with our ubiquitous 0's and 1's:
“I hear and I forget. I see and I remember. I do and I understand.” (Confucius)
This continuing series of SQUINTS will attempt to unfold the above categories of determinants of technology that can work, with a similar attempt to align the specific technologies with their fit in classroom functions and in learning assessment.