SQUINTS 5/14/2012 – K-12 & TECHNOLOGY: PART TWO

 Foreword

After last week’s launch of “K-12 & Technology” a process of critique and self-evaluation was executed, because the topic is complex, and because the trail of insights from prior work ends quickly.  The question posed was, is all of this detail and complication really necessary?  Can’t we simply by common sense and a little trial and error observe which hardware, and what applications work in the classroom? 

A fair question but the answer isn’t simple:  One answer is that the rate of development of technologies, devices, and software is now so great that some mechanism or screening logic is demanded to sort capabilities against needs.   A second answer is public education paranoia about taking any risk that might impact presently demanded standardized test scores, shooting down most creative and ad hoc experimental approaches, though the concept has a history in market-based innovation.

Part Two, while starting down the trail of how to match technology and needs also begins with an overview of how technology is conceived to assist public K-12 performance.

Part Two

The Technology Mission

In the course of researching this installment, two existing sources popped out.  The first is new and notable, and frames this whole series; how dependent on technology should K-12 change be?  The second is notable but not new, just widely overlooked in the technology debates.

All linked, the first was a press report of two Washington, DC schools at opposite ends of the technological spectrum, one super-technological, one that might be billed, for Luddites-only. Illustrating the challenge of prescribing technology for K-12, both schools are outputting students who score in the 600 range on SATs, strongly suggesting that technology per se is not necessarily the determinant of solid learning.  But simultaneously, the two systems are placing material bets on the state of the world, and its trajectories a decade or two into the future.  Will the world retreat to the 20^th century, or will the 21^st continue its seemingly inexorable path to higher order technology requiring that literacy to cope?

The second source is a primer on technology in K-12, and should have been required reading for every American K-12 educator then and now: The “National Education Technology Plan 2010,” authored by the Office of Education Technology, of the U.S. Department of Education.  Creative and eloquent, the Plan details how technology might be effectively employed to advance American K-12 education.  That document is the de facto platform for the point of this series, so an unusual recommendation; take a time-out and read that document.  Paraphrasing its contents simply wastes words and would suffer by comparison, but there is one paragraph pointedly addressing the issues of Part Two:

"What education can learn from the experience of business is that we need to make the fundamental structural changes that technology enables if we are to see dramatic improvements in productivity. As we do so, we should recognize that although the fundamental purpose of our public education system is the same, the roles and processes of schools, educators, and the system itself should change to reflect the times we live in and our goals as a world leader. Such rethinking applies to learning, assessment, and teaching processes and to the infrastructure and operational and financial sides of running schools and school systems."

Parenthetically, and the only departure dwelling on present standardized testing, pp. 25-38 of the Plan are instructive.  If you read the citation, you will be left with a major question.

Addressing the Issue of Complication

For the literal-minded, “what you sees is what you gets,” and the issue may seem transparent; for example, a pad is a device based on a processor with graphics orientation, WiFi/Internet access, and with a whole private sector creating apps.  What’s so complicated?  For openers, the hardware has been dedicated to a different mix of allowable operations versus a desktop or comparable laptop computer.  Less processor capacity and speed, and less memory are available.  The operating systems are from present mobile operating systems, positioning a pad to be adept at communication rather than traditional computing, capable of being a reader, a videoconferencing tool, a word processor, an audio source, but neither designed for nor supplied with software allowing the problem solving capacities of the traditional desktop.  The answer to the question, where do you position a pad as a learning technology, requires an answer to the learning-based question; for what stages of learning and in what ways is it the applicable classroom tool?

Apply the same logic to all complex hardware being advocated for K-12.  The issue is that alternative hardware can serve comparable classroom uses, e.g., a pad, or laptop, or a smart phone, or a whiteboard, or a digital projector, can display information, be accessed interactively by a teacher, and supports communication and even animation, but with nuances in how each excels.  So, flip a coin, or run the cost numbers?  If every display opportunity equivalently served all learning factors in play, just that simple.  But there are:  Multiple dimensions of thinking; theories of learning; learning processes; actions that constitute the contents of any learning sequence; assessment options; learning styles; links between substantively what is to be learned and theories, processes, and actions; organizational and classroom arrangements; teacher capabilities; models; algorithms; paradigms; and rubrics.  It is not intuitively obvious where to invest.

Apparent above, the hardware also interacts with the software.  For example, there are somewhere in excess of 750K apps for pads and smart phones, with some unknown number applicable to K-12 learning.  A property of those apps is that they are bite-sized applications, not broadly designed for data base work, heavy research or problem solving calculations, complex simulations, or statistical analysis. For example, the ubiquitous Mathematica, major simulations, and most education statistical packages are not designed for pads and would be constrained.

Lastly, a large issue is the combinatorial effects above; multiple hardware types x learning modes x multiple learning assists x multiple software pros and cons x multiple classroom environments x multiple cost/investment scenarios = intuition fails.  Consider the analog with another training- and experience-rich professional skill set – the professional pilot.  Intensive training, both theory and practice, testing both concept-based and practical, at the high-order end of the function thousands of hours of drill and performance; pilots with all of those qualifications still use checklists, and pilots with all of those hours can still, failing that use, land with gear retracted.  Embarrassing, and the grinding noise is unnerving.  The matrices implied above, that shape technology elections, are the equivalent of the checklist for pushing the right technology buttons.

A Focus to Link Learning and Technology

An irony of the present reform movement is that it is based on a distortion of reasoning that has dominated public education since its development, the concept of reductionism.  Reductionism is defined as “…an approach to understanding the nature of complex things by reducing them to the interactions of their parts, or to simpler or more fundamental things, or a philosophical position that a complex system is nothing but the sum of its parts, and that an account of it can be reduced to accounts of individual constituents.”  Long the determining model for education methods, the standardized testing movement is no more than a glib expression of the same reasoning, that perfection of lower-order learning will energize all learning.  The temptation is to cite the old saw, “you gets what you pays for.”

Improving learning, from recognizing to designing, touching all aspects of the processes between, therefore requires some viable formulations that first identify what is in those processes, then reassembles the components to facilitate genuine learning, those higher-order thinking skills that when practiced in context germinate knowledge.  The technologies are metaphorically the servos that activate or orient information and thinking constructs that emerge as human learning.

Some to many learning processes have not been sufficiently developed or formalized, partially because of weak technology levels within public K-12, partially because Bloom’s taxonomy of learning and a 21^st century version have become one standard bearer for addressing K-12 instruction.  Education psychologist Benjamin Bloom’s 1956 publication of Bloom’s Taxonomy (of learning stages) captivated public education.  Despite critics, it has endured and with numerous amendments to become Bloom’s 21^st century digital taxonomy, and dominates learning stage citations numbering in the dozens of flavors.  In the process it may have also become a barrier to breaking through to another level of thought that might have smoothed introduction of technology.  

Figure A is one updated depiction of Bloom’s taxonomy, amended to illustrate the tools, techniques, methods, and materials that align with Bloom’s learning action steps.  The materiality is that the “products" shown are another rung on a ladder to technologies that fit.

Figure A

BLOOM’S  TAXONOMY

Higher-order thinking	Bloom	Actions	Products
	Creating (Putting together ideas or elements to develop an original idea or engage in creative thinking).	Designing Constructing Planning Producing Inventing Devising Making	Film Story Project Plan New game Song Media product Advertisement Painting
	Evaluating (Judging the value of ideas, materials and methods by developing and applying standards and criteria).	Checking Hypothesising Critiquing Experimenting Judging Testing Detecting Monitoring	Debate Panel Report Evaluation Investigation Verdict Conclusion Persuasive speech
	Analyzing (Breaking information down into its component elements).	Comparing Organising Deconstructing Attributing Outlining Structuring Integrating	Survey Database Mobile Abstract Report Graph Spreadsheet Checklist Chart Outline
Lower-order thinking	Applying (Using strategies, concepts, principles and theories in new situations).	Implementing Carrying out Using Executing	Illustration Simulation Sculpture Demonstration Presentation Interview Performance Diary Journal
	Understanding (Understanding of given information).	Interpreting Exemplifying Summarising Inferring Paraphrasing Classifying Comparing Explaining	Recitation Summary Collection Explanation Show and tell Example Quiz List Label Outline
	Remembering (Recall or recognition of specific information).	Recognising Listing Describing Identifying Retrieving Naming Locating Finding	Quiz Definition Fact Worksheet Test Label List Workbook Reproduction

Source:  Iowa State University, Center for Excellence in Learning and Teaching.

Figure B adds an additional factor, by suggesting a communications spectrum as a potential overlay for Bloom’s stages and verbs connoting action processes.

Figure B

BLOOM’S AMENDED TAXONOMY

Source:  Andrew Churches, Digital Bloom’s Taxonomy (NZ).

For illustration, this depiction of Bloom shows the concept of a gradient from lower level learning to higher order thinking skills.  “Finding” at the lowest level of alleged learning implies the ability to simply identify and decode information that conveys meaning, or in a digital world find information perhaps via the Internet or other search modality.  At the apex of Bloom’s “creating,” “designing” might invoke creative development processes, computer-assisted design, or something as exotic as creating an artificially intelligent avatar or creating state-of-art modeling.  All of Bloom’s stages infer possible technology fits.

Getting to Technologies

A private sector research firm, Ambient Insight, has attempted to connect up learning stages and technology, proposing the following types of learning products that infer hardware/software technologies:

• Self-paced eLearning Courseware
• Digital, video, Text & Audio Reference
• Collaboration-Based Learning
• Social Learning
• Simulation-based Learning
• Game-based Learning
• Cognitive Learning
• Mobile Learning

Interesting addition, but it still begs the core question.  Cognitive learning is a universe; every mathematical, statistical, biological, STEM, economic, environmental, space, social, and organizational concept amenable to quantification could swim in that pond.

Hence, a complete model of technologies applicable to K-12 is the combination of something like Bloom’s taxonomy x hardware types (computing, video, audio, tactile, discrete analytical, sensing, replicating) x matched logic operators (sorting, classifying, computational solutions, statistical analysis, simulation, gaming, optimization, allocation, sequencing and scheduling, data mining) x the classroom environment x discrete proprietary software that provides execution of these processes.

Figure C is an attempt to portray all of the basic factor categories that might dictate a technology election.  Each category breaks into multiple factors, and in this combinatorial form the task appears daunting.

Figure C

MATCHING LEARNING NEED AND TECHNOLOGY

         Factor Categories                                                             Outcome

         Learning theories

         Learning stages                                     

         Learning actions

         Learning instrumentation                       

         Multi-purpose computer                        

         Communications hardware

         IP/WiFi/cloud                                       

         Mobile systems

         Social media                                                                            Unique

                                                                 FACTOR                         Need  &

                                                         COMBINATIONS    X      Technology

                                                                                                          Solution         

         School/classroom environment              

         Teacher status

         Student learning style                            

         Seat time

         Student attributes                                   

         Prior knowledge

         Special education requirements              

         Assessments -- formative/high order

              

         Subject matter taxonomy

         Subject matter structure/                        

             knowledge types

         Specific algorithms/paradigms/               

             models/simulation/gaming

         Expert systems                                        

         Artificial intelligence

However, before resorting to one of the time-honored mechanisms for choice in the face of many options – squat and squint, or the dart, or the ultimate decision-maker, delegate it – some perspective is in order. 

The same conceptual problem occurs daily in commercial sourcing, where a buyer need is posed against an assortment of materials, sizes, physical specifications, performance specifications, prices, etc.  A sophisticated but common form of customer solution – and applicable to education technology choice – takes two branches:  One, the lookup table, where the work to match need to product or service has been exhaustively researched and the best options are defined for all key needs; or the development of an algorithm or model that takes as input the buyer’s needs, then solves for a best match between buyer specifications and available products or services.  The latter modeling, for example, can accommodate in addition to physical specifications, buyer or user preferences expressed quantitatively.

Applying the same logic to education technology matching means two extra courses of development, not seemingly off-the-shelf:  One, characterizing all of the hardware that fits in terms of a series of properties that discriminate how they fit classroom learning; and two, promoting the process of documenting, preferably by controlled experiment, a specific technology’s actual delivery of performance in the learning setting.  The software side of learning technology is a quicker study, because to an extent use can be experienced and evaluated by a user using the same rubrics that guide classroom practice and choice of methods.

A Midway Point

Because of the failure to exploit classroom digital technologies even at the level of higher education versus the private sector, that might have beneficially trickled down to effect K-12, present attempts to play catch-up place an exceptional burden on K-12 administrators and teachers.  To add to the shortfalls, the IT (information technology) function in K-12 education in particular has been low level; changing that is a major need and challenge, because applying IT in K-12 now entails dual skills to accelerate technology adoption, both the technology understanding and extensive awareness of learning theory and practice.

Revisiting opening arguments, real integration of digital technologies with present classroom practice means going back to the drawing board and rewriting K-12 education thinking on what works in both classroom, and increasingly in those learning venues outside the classroom that either reinforce or diminish classroom success.  That, in turn, means an effort to attract technological expertise to the classroom that goes beyond even the aspirations for Teach for America.  That means reform of schools of education.

What can be applied here and now in the public K-12 education trenches obviously won’t benefit even from competent strategic planning at a USDOE, or at state levels, and may not receive any help at all in the present testing-dominated environment.  If there are to be better technology inputs in K-12 they will have to happen at the local level, or secure some converts in our technology companies to invest in education.  Aside from Apple early out of the starting gate, now belatedly Microsoft, there has been precious little targeted private sector effort to specifically understand and tailor technology for education.

Part Three

Part Three of this series will attempt a street-level walk-through of a process for selecting hardware and methods that matches one or more of the Bloom learning stages in action, and a HOTS model, and in some assumed learning settings.  Early times, but a byproduct of that probing may be some concepts for heuristic modeling the matching of need and specific technologies, helping bypass the combinatorial challenge.