An Incomplete CompendiumSomehow music, art, writing, architecture, and teaching have led me down a road to a profession concerned with educational technologies. I am not exactly sure why this is so but it does kind of make sense in the practical application. I am currently the IT Director for an educational agency and teach graduate courses in web technologies and educational application at a university. I believe these are powerful tools but also that what is made with the tools is far more important than the technology or the many are various fantasies that are often associated with them and that pertain to solving problems. Faculty Page - University of Hartford
Here are some essays and other items pertaining to my earlier understanding and analysis of technology and education. I have some very different ideas now after years of immersion in the practical day to day implementation of technology in support of teaching and learning. The first essay (unfinished) below is more in keeping with my present understanding and interpretations. The subsequent articles were written at various times in the past. John Dewey advocated the reform of the American education system, positing that existing traditional education methods were too concerned with delivering knowledge, and not enough with understanding the learning process and the ability to develop such capabilities. Dewey believed that elementary and secondary schools were generally repressive institutions that did not promote exploration and growth. His ideas included reforms that would change schools to be "major agencies for the development of free personalities." Public education slowly but increasingly embraced this concept and began restructuring education as less of the "factory model" where knowledge was systematically poured into students and moved towards a more constructivist approach. It is difficult to accurately measure the impact on learning and society resulting from the infusion of the new ideas in education but I can comment from my personal experience as a student in public schools from the mid 1950's to 1970. I characterize this period as the time when Dewey's ideas had become well integrated and had evolved to connect very well with contemporary culture. It was the highest codified form of the concepts he envisioned. My perspective on this period is tempered by years as a teacher and administrator in public education. Yet I believe my personal educational experience during this heyday of Dewey's philosophy taught me the skills of life long learning and provided me with a sense of joy and satisfaction in questioning, discovery and experimentation. These are precisely the core values and synthesis skills Dewey believed would result from his constructivist model.
Since that time a new player has arrived on the scene and garnered the lion's share of attention and resources of public education. The new glamor icon of public education today is computer technology. Computer based instruction, data warehousing and analysis, assisted learning, multiple learning style support, and a myriad of other educational buzzwords and slogans have created an enormous strain on educational resources as well as unraveling much of the progress that Dewey's ideas had spawned. Added to this new re-construction of educational methodology is the awkward and poorly conceived NCLB that resurrects some of the worst "factory model" characteristics of education from the late 19th and early 20th century.
The link above is to an educational game for children aged 8 to 10 that I created. The game really does not work as designed in this web based version but should be download and used with the player component to permit full functionality. This publication is by the software vendor that published the underlying application and uses certain examples to demonstrate their product. The game is a model for projects to be developed by small cooperative groups of middle school age learners. The students, working in collaborative fashion and with guidance and assistance from a teacher, are asked to express fundamental mathematical and/or scientific concepts in the form of an entertaining game. This model game is specifically designed so as not to be an exercise in hand-eye coordination or to promote random wandering about the interface. Instead, it features a mental challenge, based on observation and controls, in order to encourage the game player to predict outcomes and logical sequences. The game player participates by carefully observing the constructs and by controlling the speed at which the activities are presented. The educational strategy proposes that by engaging the participant in focusing on analysis and prediction, they will be required to understand the underlying concepts presented. This game is centered in the cognitive domain and involves knowledge building and the development of intellectual skills and attitudes. The game attempts to build into the hierarchy of educational objectives commonly associated with Bloom's Taxonomy, and by design, endeavors to divide the cognitive objectives into a granular state thus permitting the game player to move from simple knowledge to comprehension through synthesis stages. Knowledge is built through the gaming dialogue and is used here in the sense of the participant remembering previously learned concepts. Examples of the learning objectives at this level for this game include: knowing common terms, knowing specific facts, knowing methods or procedures and knowing basic concepts and principles.
At the next level the game initiates players to grasp the meaning of the introductory passages. Translations of concepts from one form to another are integral to the game design. Prediction by interpretation of process is on the next order of learning outcomes. At this level comprehension is supported by the understanding of facts and principles, the interpretation of sequential processes and the prediction of future results implied by data and procedures. This game does target the application level of cognitive abilities by directing players to use learned material in tangible new situations. The game requires participants to apply rules and principles to increasingly complex environments. This type of activity requires a higher level of understanding than those under listed as comprehension based outcomes.
At the highest level of this game, the participants are asked to analyze the component parts of presented situations so that inherent organizational structure may be understood. These activities require the identification of parts, analysis of the relationship between parts, and recognition of the logical principles involved. At this level the game players must demonstrate an understanding of both the content and the structural form of the material. Future development of the game could involve a design to support the next highest level of cognition. The application would permit gamers to assemble new constructs from a parts bin to form complete new logical structures. At this level the highest level, evaluation, would be closely connected to the synthesis as gamers would be compelled to evaluate their designs for accuracy and logical continuity.
An Epagogic Generative Model of Instruction In a time of drastic change it is the learners who inherit the future. The learned usually find themselves equipped to live in a world that no longer exists. Eric Hoffer Reflections on the Human Condition (1973)
It is obvious that the Industrial Age has given way to the Information Age. Lamentably, much of public education still uses Industrial Age models of instruction that encourage passive learning and stress knowledge mastery. In the media-rich culture in which we now live it would seem useful to shift to instructional models that put learners at the core of both the curriculum and delivery systems and encourage them to become actively engaged in the acquisition of knowledge. The model for instruction presented here centers on the idea that effective teaching demands that the learner be actively involved in creating new and relevant schema by integrating existing and newly acquired knowledge and skills. The teacher’s role in this model is to encourage and support the learner in analyzing and organizing processes, strategies and content of the lessons. A fundamental precept of this model is to minimize the occurrence of learners passively receiving teacher-delivered bodies of knowledge. Operating on constructivist principles, the model attempts to recognize multiple representations of reality. The world we live in is dynamic and highly complex and learners need to focus on knowledge construction, not reproduction. Tasks and activities, as presented in this model, contextualize rather than abstract the acquisition of knowledge and skills. The model aims at leading learners to a metacognitive approach to problem solving and a construction of knowledge through social negotiation.
Generative Models of Instruction
The term generative model of instruction refers to a model which is multiparous in that the inherent structure of the model gives birth to a prolific set of vehicles or conduits to lesson objectives. This aspect of the model was specifically designed to permit learners to take full advantage of the multi-access, multi-channel nature of a computer-based learning environment, given the diversity of learning styles. According to Eggen and Kauchak 1, “teaching models are prescriptive teaching strategies designed to accomplish particular instructional goals.” The descriptor prescriptive, is used to emphasize that the teacher’s responsibilities are clearly defined in planning, implementation and assessment stages of instruction. Models are constructed to achieve specific goals and different models are selected to match particular goals.
Active teaching is not a new concept. In 1983, Thomas Good coined the term to describe both a genera of teaching behaviors and a philosophical approach to teaching. According to Good, active teachers identify goals for their students and find effective strategies to meet them. Active teachers provide examples and illustrations which aid learners in gaining a deep understanding of the topic. Active teachers demand that students take an active role in the learning process. The active teacher serves as a guide for the learner’s construction of new schema and carefully monitors the process for evidence of learning.
Information Processing
In creating a model for instruction which is relevant to a computer-based learning environment it is necessary to examine how information processing is enhanced and otherwise affected by integrating the artificial intelligence and multi-access delivery systems of the computer with the learning process. The way that people gather and organize information from the environment in order to form useful patterns is can be modified in many ways by the processing power now available to learners with ready access to personal computers.
Sensory Focus and The Affective Domain
The sensory focus of learners in a computer-based learning environment is generally greatly enhanced and the level of engagement is often near optimal. Considering this, the model reflects a design strategy which strives to access deep levels within the affective domain as an integral component of the learning process.
The model presented here incorporates aspects of several models including some of the fundamental concepts presented by Robert G. Main in Integrating the Affective Domain into the Instructional Design Process.2 The key relevant factor in Main’s research points out that attention to the affective domain is viewed as being particularly important for technology-based instruction which removes much of teacher/student interaction from the lesson delivery.
A technologically literate person uses systems and processes in an informed, ethical, and responsible way. Ethics and responsibility are centered in the affective domain and, ideally, are addressed at the core of all models for instruction, particularly that which relies on computer technologies. The model presented here views technological fluency as not only knowing how to use new technological tools, but also recognizing ethical, responsible use, and knowing how to make things of significance with those tools while developing new ways of thinking based on new experiences. Learners must also concurrently acquire skill in adaptation in order to continue to succeed and must be fearless to analyze, control and take advantage of evolving technologies.
The design of model presented here synthesizes some aspects of J. Keller's ARCS (Attention, Relevance, Confidence, Satisfaction) model of motivation for learning with other instructional design model components (analysis, design, development, implementation, and evaluation). The model provides a structure for organizing instructional principles, strategies, and techniques concerning both the lesson content and the affective domain. The primary characteristic incorporated from Main’s model, cited above, is to help in providing for consideration of the affective domain in every aspect of the instructional design process, from planning through lesson implementation and evaluation of learning results.
Social and Cultural Context: Art vs. Technical Procedures
Major findings3 of research on teacher thinking and instructional systems design illustrate that teachers and their teaching and learning processes can only be studied within their social and cultural context. Teachers' knowledge is a complex blend of personal, practical, and theoretical knowledge. Teaching, learning, and instruction has to shift its emphasis from cognition to the social construction of knowing, and teachers as designers of their own instruction needs to be emphasized. The model of instruction presented here is built to take advantage of a multi-access framework that remains responsive to evolving cultural and social sub-systems of the learners. Multi-access delivery and opportunities for generative teaching are intrinsic to the computer-based educational environment. Each delivery system may be content independent and able to have its own unique instructional design, all of which is incorporated in a learner-centered philosophy. The concept of instructional design as an artistic, social, and cooperative act replaces the procedural and technical concept of instructional design. The focus here is on goals instead of procedures and models and principles comprises an approach in which the objectives and strategies evolve as the teacher integrates the social and cultural subsystems of the learners.
Linear Instructional Process Problems
Teachers who are trained in a linear instructional process may be slow to recognize and, often incapable of reacting to unexpected opportunities for learning which frequently occur with the integration of computer-based artificial intelligence in the classroom. Additionally, teachers often maintain that there is very little classroom time available for student inquiry which leads to the in-depth questioning of subject matter which improves understanding and hence, performance levels. One study4 which examined these problems indicated that teachers felt that it was easier to teach using the traditional instructional process model largely because of the added skills and attitudes that must be considered in generative teaching. These challenges, which are often perceived as problems, are further exacerbated by a exiguity of technological literacy among teachers.
Changes in Assessing Learning Outcomes
An important point to consider in this discourse of theory is that learners do not automatically understand the concepts targeted by instruction just because they are actively involved. Even though there be a shift in emphasis from passive traditional models to active generative models we need to understand that there results an interdependent change in what we can accept as understanding or knowing. Assessment of learning outcomes can become a much more exacting process.
Educators face the task of guiding learners in developing skills, attitudes, and thought processes necessary to sustain them in a world of accelerating change. The difficulty of this task is magnified by the reality that learners often do not understand the complexity of the structures within which to place the experiences they have. A vital part in enhancing critical thought processes involves helping learners to develop mental structures which support further learning. In a truly generative model, educators continually question their teaching methods and models so they can construct and practice a pedagogy that lives and responds to learners’ continuing cultural change.
Learning as a Living Process
Each spring, the gardener removes mulch and the accumulated debris of winter from the garden bed to prepare the soil in order to promote new growth. Similarly, educators must dislodge from learners, archaic, entrenched notions of teaching and learning, in order to promote the growth of new learning opportunities. The concept that learning is a living process, a procreative, self-sustaining animate thing, lies at the core of this model. In the past, learners were viewed as vessels into which knowledge and skills might be poured. It was accepted that some would spill out and some might stay, and thus was education served. It is self-evident that we now live in a culture rich in powerful, tireless information vessels that exist primarily to extend our cognitive capabilities. The personal computer, as an extension of a learner, places encyclopedic knowledge at one’s fingertips. From these facts, the rationale and the tools for a generative, constructivist approach are obvious. Teachers’ interactions with learners should reflect the information rich culture we live in and this symmetry can well meet the diverse goals we have for K-12 education. Learners need to think about their thinking. This metacognitive activity plays a central role in reconstructing one's understanding of the world. Constructivism applies not only to teaching, but also to teacher education in a general sense. Changes in archaic notions of learning and teaching will require experiences that challenge the paradigm from within.
The Epagogic Generative Model Overview
The model presented here is described as an epagogic generative model because it attempts to shape the learning process as a living heuristic exploration. The model presented here is synthesized from several, constructivist-oriented, prescriptive teaching strategies. It is specifically designed to lead learners to specific goals within a computer-based learning environment. Technological fluency skills underpinned by ethical, responsible use of communications technologies are interleaved as interdependent entities in a constructivist approach to learning. The model strives to emulate real-world environments that furnish contexts in which learning is relevant. Focusing on realistic approaches to solving problems, the teacher is the analyst of strategies used to reach solutions. The model stresses the inter-relatedness of concepts and processes, providing multiple representations or perspectives on the content. In the model, learning is viewed as being internally controlled and mediated by the learner. The teacher begins implementation of lessons by presenting learners with first-tier information and subsequently with problems which require resolution through development of derivative information and cooperative analysis.
Physical and Social Structure of the Model
This model is designed for a computer-based learning environment, more specifically, a fully networked facility with on-line access to global information sources and a wide range of applications and local information resources. The model necessitates a classroom or lab environment in which the learners feel compelled to offer ideas, take risks and feel comfortable with initial negative results. The learners need to perceive negative results as additional first-tier information and use it in re-processing or re-formulating solutions.
The teacher’s role includes exposing students to new technical information and a question and problem that will require that they learn to integrate the new information with existing knowledge and skills to synthesize solutions. The teacher must encourage learners to ask questions and guide them through negative results so they recognize them as routes to finding original solutions to the problem. The teacher should model questioning behaviors and how to accept and re-process negative results as new information. Learners throughout the activity are analyzing first-tier and derivative information with cooperative questioning strategies to define fundamental concepts. The concepts are needed in order to construct a meaningful new schema leading to successful resolution. Learners are required, by the nature of the activity, to refine and demonstrate skills in the affective domain in order to succeed.
Theoretical Perspectives of the Epagogic Generative Model
This model is based on a constructivist axiom that represents learners as deriving an autistic comprehension of the world around them, as opposed to having understanding delivered to them by others. This model views the learner at the core of the learning process and contributes tools and facilitation channels for developing meaning. It is essential that the teacher maintain an active role in guiding learners to the tools and channels that will help them create new schema.
Goals, Concepts and Principles in the Epagogic Generative Model
An essential component of this model is the idea that true technological literacy requires a seamless interleaving of skills in the cognitive and affective domains. The skills humans need in today’s society are different from those of the recent past and relate intrinsically to the phenomenon of change. 5 The communication devices that extend human capabilities have extended ethical implications. Tools which extend our vision and our ability to send ideas, sounds and images around the require correlative responsibility. This lies at the heart of the fully integrated model. It is also important to the success of technology-based learner outcomes that they not be developed in isolation from other subject areas. Technology centered learner outcomes must be included in every subject's curriculum revision cycle.6
Specific goals for the model include:
The concepts which learners need to comprehend in order to attain the goals range from the similarity in characteristics of computer applications to functional revisions of self-concept. In this model, learners derive important components of cooperative problem-solving from self-assessment of interactions with others. The teacher must carefully monitor and guide these self-assessments and occasionally mediate interactions which are counter-productive. The concepts in the affective domain generally have numerous, intangible characteristics, and thus demand the intensive monitoring and guidance from the teacher. In the epagogic generative model, the teacher is responsible for guiding the integration of subordinate affective domain concepts into revised self-images which support adaptability and a recognition that learning is an essential, positive, on-going component of the individual. The revised self-images are sustained by the learners’ acceptance of new principles which they can then apply to future experiences.
Lesson Planning with the Epagogic Generative Model
Planning lessons for the epagogic generative model requires that the teacher map multiple trunk routes to objectives and content areas. Trunk routes may be thought of as primary channels or approaches to content areas and objectives which permit multiple direct routes to the destination. They may be regarded as frameworks for thoughtful outcomes. In the computer technology-based learning environment there exist myriad channels for learners to construct meaning from experience. This multi-access potential of the environment is the flywheel for a truly generative learning experience.
Trunk routes may be built on topical or process structures. Topics and processes may be inverted or combined in various ways. A process based trunk route may define itself in terms of a topic or the inverse may be true. The focus throughout is for learners to develop new ways of thinking, about the world and about themselves.
Specifying Objectives
Having identified the trunk routes for the lesson activity the teacher must then define the objectives, i.e., precisely what content and/or processes we want learners to know. In the epagogic generative model an underlying, generic objective is that learners will be able to map and re-navigate their unique sub-routes to specified destinations. The destinations are the detailed objectives for the activity. The objectives should be clearly stated and expressed in measurable terms. These objectives may or may not reflect criterion-referenced performance standards. Rapid obsolescence is the norm in a technology-based learning environment and so assessments, evaluation, reporting, and student placement concerning these technology-based learning will depend heavily on the professional judgment of teachers who must be technologically literate.
Modeling Situations and Challenges
In the epagogic generative model, and perhaps in all learning, self-assessment by learners is of great importance. The teacher must thoughtfully model skills which are centered in the affective domain. Because of the number of concepts in the affective domain, and their intangible nature, modeling behaviors, attitudes and self-concepts is an exacting task for teachers. Role play may often be relied on as a useful tool for teachers.
Selection of Examples
Along with modeling affective domain characteristics the teacher must provide learners with examples of the sub-routes to objectives. It is good planning to have examples of diverse sub-routes which illustrate oppositional multi-access channels. Examples that are globally different will help to demonstrate the wide range of sub-routes available and suggest that many choices exist for learners.
Implementing Lessons Using the Model
In the epagogic generative model of instruction there are six phases, illustrated below, which form a framework for implementation of the lesson.
In the lesson initiation phase of implementation the teacher engages the students’ attention by posing a question, or describing circumstances which suggest a specific trunk route as a probable path to resolution. For example, the following initiation illustrates a trunk route to a destination with multiple sub-routes possible: “Today, I have some data projected on the screen. As you can see, these numbers report how much snow has fallen in years past. I’m wondering if there is a way we can use our computer programs to really make clear to students in grades three and four how much snow has fallen in Connecticut each year for the past ten years. Why don’t we quietly talk it over for a few minutes with each other and see if we can come up with some ideas.” Ideas and examples are subsequently shared aloud by the class.
In the derivative question and challenge phase the teacher determines the boundaries of the trunk route to resolution by limiting the applications which may be used by students and by posing the challenge. For example: “Using your spreadsheet program and a graphing program, let’s see what kind of picture we can create for the grade three and four students that will really show them the amount of snow which fell each year for which we have data.”
In the exploration and discovery phase students work independently, and in pairs or small groups, to explore the possibilities of the applications to manipulate and graphically represent the raw data. In this stage students will construct meaning from following sub-routes which produce unsatisfactory results as well as from sub-routes which are promising. It is during this phase of the implementation that the teacher needs to be especially vigilant in monitoring skills which fall in the realm of the affective domain. Students who are able to create successful sub-routes to resolution are encouraged to circulate within the class as advisors/consultants. This microteaching of peers will reinforce learners’ ability to re-map in the next phase.
In the re-mapping phase students are asked to demonstrate the sub-routes to resolution for their classmates. During the demonstrations branches and entirely different sub-routes are identified and become cognitive components in new schema constructed by the learners. During the re-mapping phase learners have opportunity to explore the other students’ sub-routes and compare and contrast their advantages and disadvantages. In re-mapping diverse sub-routes, learners discover and invent new strategies for acquiring meaning and, theoretically, expand their higher order thinking skills by analyzing and evaluating.
In the closure phase the learners identify specific characteristics of their mapping and re-mapping towards successful destinations. The skills being identified in this example could be generically represented as compound process skills, i.e., clusters of micro-skills which enable learners to navigate sub-routes and re-orient during their explorations. One example of a discovered micro-skill cluster might consist of knowing how to use application shortcuts to quickly move back or forward one operation, or to a concurrently running application. Another example stated in the form of an objective might be: Using specified computer-based applications, the learner will locate, copy, paste, and edit specific data in the multi-tasking environment. Collaboration between learners during this phase is a main conduit for acquisition and guided practice of micro-skill clusters.
In the evaluative applications phase students are given a corresponding challenge which requires them translate their new micro-skills clusters and maps for a different data set. In this phase the teacher must again be especially careful in monitoring the affective domain components during the activity. Learners who remain uncertain of sub-routes to resolution may require experimentation with numerous strategies in order to find a way to construct a meaningful new schema. Assessment strategies which focus on group dynamics as well as the development of micro-skill clusters are employed by the teacher throughout the phase.
Summary
The epagogic generative model provides a strategy that is specifically adapted for a computer-based learning environment. The model, founded on constructivist principles, offers learners a myriad of opportunities, as well as dimensional flexibility, in finding meaningful relationships between components of existing and new knowledge. The new relationships and coherence among the constructs provide learners with more highly evolved and responsive schema. The learners are guided, through the structure of the model, to find new ways to think about their thinking and new ways to make sense of their world. The model’s emphasis on meta-cognition reflects Hoffer’s belief that, “In a time of drastic change it is the learners who inherit the future.” Exponentially accelerating change is the essential nature of contemporary human existence. It is the responsibility of teachers to lead their students to develop the skills, tools and knowledge they will need to be successful, lifelong learners.
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Stochastic Simulation Games and Learning for Evolution Will Game Theorists Use Computing Power To Bridge the Great Rift Between Machine And Organic Intelligence and Cultivate Human Evolution?
4.2.2002
In a general sense all humans experience pleasure in the learning of new things. The power of this genetic attribute is apparent in humankind’s physical mastery of the world. In human culture gaming plays a part in building social skills, in the testing and development of intellect, in establishing leadership and in the practice of strategic and tactical conception. Games are systems of ends and means and as such, have always been a supportive cohort to cognition. As human culture has evolved, so has the sophistication of gaming rituals and the complexity and the conveyances of games. The significance of continuing development of sophisticated, computer-engendered virtual-reality gaming should not be discounted as a simple artifice of popular culture but instead be given due consideration as a component in the maintenance of a successful evolutionary path. At what point does the curve of human evolution begin to flatten? As humans meet and master the remaining physical challenges of their world, the ability to increase intellectual capacity might well level off without the benefit of technological utilities designed to cultivate evolution through machine generated challenges and demanding and specifically tailored virtual realities for learning. Advanced machine intelligence may prove to be critical to the continued growth of human intellect or it may be a philosopher’s stone that proves to be as insubstantial as the original. The answer likely lies at a point in the future where there may come to be a convergence of organic and machine intellectual equals and game development is a ready made laboratory for the development of cognitive enhancement systems. Thinking and meta-cognition steer action and comprehension and humans and games serve as rehearsals or simulations for exigencies in a diversity of situations. Specifically, the performance of action reflects the execution of decisions and thus games serve to provide a test bench for strategies to carry out plans or intentions, or as practice in following instructions.1 In this age of digital information technologies games have inherited new powers and abilities that can profoundly affect human cognitive abilities. Prior to the development and diffusion of modern computer and information technologies games were preponderantly based on interactions between human intellects or otherwise based on puzzles or challenges created by others. Advances in computer-based artificial intelligence [AI] are generating new levels and forms of gaming activity that may lead to important educational benefits and advances in human cognitive capacity. The modern era of game theory as a formalized discipline is generally considered to have begun in the year 19442 with the publication of Theory of Games and Economic Behavior by John von Neumann and Oskar Morgenstern. This book is frequently cited as the seminal work establishing game theory as an important branch of learning and many theories from the work were widely applied to the study of economics. This early stage of modern game theory mirrored basic principles of neoclassical economics. The underlying components employed in early models included mathematical agents for maximizing dynamic change, for creating rational agent behaviors and for establishing states of equilibrium in the model environments. Subsequent models of game theory attempted to emulate evolutionary biology and complex intelligent-agent-based simulations. Game theory based on biological models is commonly referred to as Evolutionary Game Theory or EGT. The power of computer technology has thus far made EGT quite successful in the analysis of complex biological systems and in other multifaceted systems such as macro-economics and stress modeling. Further development and integration of EGT with the ever-expanding capabilities of digitally mastered virtual realities, literary power and stochastic simulations hint at the possibility of radical new educational capabilities. Expanding abilities to create increasingly sophisticated and realistic gaming environments are nourished by the growing integration of intelligent agent techniques in both gaming software and in testing environments for human stress handling capabilities. In experiments, these agents have been successfully used to accurately build virtual entities that have the processing and reaction power of lower intellect mammals such as rats and mice. These new technologies fall short when attempting to emulate human intelligence because the inherent architecture, conventions and tools are currently incapable of re-creating the vital characteristics that distinguish persons from the lower animals. The discord between organic and machine intelligence is the core problem for future game development.
Advances in computing power and software engineering are approaching the capability of emulating the some basic language based communicative powers and it may be possible in the future to create an artificial intellect that can perform relatively robust abstract reasoning. It does not however seem possible that true creativity could be replicated by any intelligent agent because the paradigm for such instruments is not inter-subjective but fundamentally a third-person, configuration. Intelligent agents never invent, they simply perform pre-programmed actions and follow the channels implicit in their design. Incapacity to initiate new decisions and actions eliminates the possibility of free will and true creativity. Machine intelligence and its ability to emulate human thought may be limited by its very nature. Several philosophers [e.g. Nagel3] have argued that phenomenal consciousness can never be encapsulated in a design that is based on a third person scheme. John R. Searle states that, “The brain, as far as its intrinsic operations are concerned, does no information processing. It is a specific biological organ and its specific neurobiological processes cause specific forms of intentionality.”4 This observation summarizes the concept that the chasm between machine intelligence and human consciousness is enormous. Perhaps there can never be convergence but instead, as machine intelligence evolves, there may be diplomatic sharing of specific, translatable attributes and ideas. At the extreme edge of experimentation, intelligent agents are being explored at the NASA Cognition Lab5 where computer programs have been developed to help predict excessive memory requirements for human air traffic controllers. The design of this multiple intelligent agent system is perhaps the most advanced and complex processing game yet developed by humans. The system includes components for simulating a physical operating environment, a human operator, and an experimental observer. At the core of the model are mechanisms that permit the intelligent agents to carry out complex tasks closely approximating the human propensity for developing habits, for execution of tasks and related imprecision. The goal for simulation designers, and by extrapolation for educational application engineers, is fundamentally the same; that is, to transform the imagination of humans towards a state where the difference between reality and virtual reality is indistinguishable. This cannot simply be accomplished through more and more sophisticated and powerful technologies but will also require intensive development towards a functional analogy of ancient human narrative magic. Clark Dodsworth Jr.6 illustrates some of the challenges in using these new digital tools to expand on the primordial human story-telling arts that strive to create new, virtual experiences in the minds of others. In the book, Digital Illusion: Entertaining the Future with High Technology, Dodsworth explores how multimedia content designers use the integration of ancient story-telling techniques with state-of-the-art methods to create magical simulations and effects. Dodsworth attempts to show how users, engaged by rich narrative, are otherwise affected by the shape and conveyances of new, technologically rich experiences. The implicit question is; how closely can multimedia/multi-modal simulation be integrated with human reactions and feeling? Dodsworth’s emphasis on the importance of narrative is strongly echoed by Markku Eskelinen7 and Selmer Bringsjord8. Bringsjord states that in order for games to continue to evolve there is a, “…need to instantiate immemorial themes”. According to Bringsjord, other key areas that beg for attention in gaming development are story mastery, personalization and the building of, “robust autonomous characters.” Personalization may be among the most challenging attributes to truly emulate because organic intelligent reactions are founded upon mutual understanding between parties. The nature of machine intelligence, unlike human intellect, is not centered on complex and inclusive beliefs about others and the world. Instead machine intelligence is rule driven and exists as a Planiverse9; a dimensionally challenged shadow universe. Because computers have inherent systemic restrictions when replicating structure at the nexus of human identity , true literary power remains an underdeveloped and required component to push virtual reality gaming to new levels. In 1950 Alan Turing10 published a seminal paper on machine intelligence that spawned the Turing Test (TT). The Turing Test for machine intelligence has been widely debated and many attempts to improve it have been made. Numerous thought experiments have expanded the battery of trials for examining machine intelligence and a famous argument that supports the idea that machines are incapable of ever truly knowing is identified as the Chinese Room Argument11. In this thought experiment devised by John Searle, one is asked to envision being in a sealed library full of rule books, blank books, and writing tools. From time to time papers with Chinese characters come into the library through a slot in the wall marked “input". The functional task associated with each piece submitted is to locate the rule that matches the pattern on the piece of paper. From this rule will be an analogous rule dictating a specific reply pattern of characters to write on a blank and send out through another slot marked “output”. The assumption is that the room’s occupant does not understand Chinese, and is not cognizant that the symbols are any specific language beyond the encoded rules. According to the argument the input received are questions about a story and the output generated by the rules are correct and astute responses to the questions. Following the argument, the questioners outside perceive the output as a fluent speaker of Chinese. For Searle, the kernel of the argument is that there is no internal understanding of Chinese. Critics of his position predominantly make the point that the individual in the Chinese Room is merely a component of a system and that the entirety of the system does understand. While it may seem to be somewhat of a semantic argument, when comparing gaming simulations and virtual reality to human relations and reality it is apparent that there is a replacement of user and system events for actions and proceedings. The contrast becomes less ambiguous when considering computer game events (both user and system) as being either independent or dependent of the game player. Organic intelligence internalizes both components thus creating a rich narrative base for literary expression. Conversely, machine intelligence is restricted to simplistic evaluation of events as successful or unsuccessful and thus to simple constative statements. Additionally the dimensional richness of digitally engineered media and illusion is adversely affected by the exponential rate of change in the associated toolset. Many, if not most, digital tools are obsolete so quickly that the refinements and virtuosity associated with traditional narrative based on long-established techniques is atypical or non-existent. In the book, Trigger Happy: Videogames and the Entertainment Revolution, Steven Poole downplays the distinctions between classical arts such as painting, film and literature and contemporary sophisticated videogames. Poole asserts that if videogame interfaces exist on an assortment of informational scales they become more than merely two-dimensional illusions. I believe that Poole inadvertently reprises the idea presented by the Chinese Room Argument in his description of types of incoherence. As semiotic power and memory increase we can infer how machine intelligence might evolve ostensibly more sophisticated psychological strategies but still lack true understanding. Poole’s general aesthetic arguments include the notion that certain videogames are works of art. Poole states that videogames have many of the characteristics of improvisational theatre and that an authentic gameworld scenario is "absolutely possible." He proposes that these creations are art in a different sense than usually imagined and that in time they will be accepted as sophisticated and rich narrative forms. I would agree with Poole that gaming deserves genuine academic and cultural study and is a natural branch of the games theory tree. Should his belief that completely immersive virtual reality be realized then education, and quite possibly human evolution, stand to benefit to a degree that was previously impossible. For education and human development, truly immersive virtual reality may be a philosopher’s stone but Poole maintains that extreme realism makes gaming difficult and boring. A central tenet of constructivist theory is that learning requires the student to draw upon social, cultural, and material resources available in the world in which we live. The roots of constructivism are deep in soil of authenticity. As difficult as identifying the authenticity of educational components might be for educators in the real world, the task becomes enormously more complex when evaluating virtual realities. As much as each person’s world has a different shape and form to their perception, virtual worlds will always bring external contrivances to the union. Just as educators cannot pre-authenticate learning because they cannot predetermine the learner’s representation of the task, virtual realities and stochastic simulation gaming will always lack an inherent understanding of the human participant. Human language, because of its propensity for change and re-interpretation, in itself will always be an obstruction to machine-to-human understanding. Ludwig vonWittgenstein12 maintained that the rules for usage in ordinary language are neither right nor wrong. He stated that the rules are not true nor false, but that they are merely useful for the specific applications and contexts to which they are applied. This vagueness of ordinary usage will always be a problem for machine intelligence but for humans, it is source of linguistic riches. Meaning lies at the heart of understanding and the plastic rules for human language reflect the spontaeneous use of expression by organic life. Multimedia computer gaming technology may hold enormous power to educate humans by providing authentic and personalized experiences that would otherwise be unattainable. There are many complex challenges to reaching this ulimate stage of potency and it may prove to be beyond the realm of human creative endeavor. The intrinsic differences between machine and higher-order organic intelligence are many and vast. It is clear that there is the value of meta-learning in integrating stochastic simulations and virtual realities with the educational process. The jury may be out for a long time before its effect on human performance and intellectual development is clear. Bringsjord, Selmer, Is It Possible to Build Dramatically Compelling Interactive Digital Entertainment?, Game Studies, The International Journal of Computer Game Research, Issue 1, July 2001 - http://www.gamestudies.org/0101/bringsjord/index.html Davis, Martin, The Universal Computer: The Road from Leibniz to Turing, W.W. Norton & Company; 2000, ISBN: 0393047857 Dewdney, A.K., The Planiverse : Computer Contact With a Two-Dimensional World, Simon & Schuster, 1984, ISBN: 0671463632 Dodsworth, Clark, Digital Illusion : Entertaining the Future With High Technology, ACM Press Siggraph Series, August 1997) Eskelinen, Markku, The Gaming Situation, Game Studies, The International Journal of Computer Game Research, Issue 1, July 2001 - http://www.gamestudies.org/0101/eskelinen/ Freed, Michael, Cognitive Simulation, http://human-factors.arc.nasa.gov/cognition/research/cogsim.html Poole, Steven, Trigger Happy : Videogames and the Entertainment Revolution, Arcade Publishing, September 2000, ISBN: 1559705396 Searle, John, R., Is the Brain a Digital Computer?, http://cogsci.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html Searle, John, R., The Mystery of Consciousness, New York Review of Books; 1997, ISBN: 0940322064 ; Von Neumann, J., Theory of Games and Economic Behavior John Wiley & Sons, 1053; ISBN: 0471911852 Wittgenstein, Ludwig, Philosophical Investigations - The English Text of the Third Edition, Anscombe, Reprinted 1999 Notes: 1 Evidence on Learning in Coordination Games, John B. Van Huyck, Raymond C. Battalio, Frederick W. Rankin, Revised October 2001 - http://econlab10.tamu.edu/JVH_gtee/OS5.HTM 2 An Outline Of The History Of Game Theory, Paul Walker - http://william-king.www.drexel.edu/top/class/histf.html 3 http://www.nyu.edu/gsas/dept/philo/faculty/nagel/ 4 Is the Brain a Digital Computer?, John R. Searle - http://cogsci.soton.ac.uk/~harnad/Papers/Py104/searle.comp.html 5 Cognitive Simulation, Researcher: Michael Freed - http://human-factors.arc.nasa.gov/cognition/research/cogsim.html 6 Digital Illusion: Entertaining the Future with High Technology. Clark Dodsworth - Jr. ACM Press - Siggraph 7 The Gaming Situation, Eskelinen, Markku, Game Studies, The International Journal of Computer Game Research, Issue 1, july 2001 - http://www.gamestudies.org/0101/eskelinen/ 8 Is It Possible to Build Dramatically Compelling Interactive Digital Entertainment , Bringsjord, Selmer, Game Studies, The International Journal of Computer Game Research, Issue 1, July 2001 - http://www.gamestudies.org/0101/bringsjord/index.html 9 http://www.csd.uwo.ca/faculty/akd/PERSONAL/books_and_articles.html#books 10 http://www.turing.org.uk/turing/ 11 http://www.utm.edu/research/iep/c/chineser.htm#The%20Chinese%20Room%20Thought%20Experiment 12 http://www.wittgenstein-portal.com/ Friday, March 30, 2007A Toolkit for Change Humans use complex and robust parallel processing learning techniques that are multi-sensory and multi-layered. This design has not evolved in response to centric models of instruction but instead to accommodate and process myriad asynchronous sensory input channels. We have evolved as tool users, testers, builders, communicators, explorers and as creators of ideas, things, meaning and dialogue. Contemporary culture is built upon a framework of previous learning and technological development that has permitted subsequent generations to reach new levels of complexity and sophistication. Information technologies are relative newcomers to the human toolkit but their potential as learning tools is readily apparent; they perform as powerful and tireless extensions of human, memory, visualization, auditory processing and virtually, experience. These technologies used as cognitive learning tools have been described by Jonassen, Peck and Wilson[1] as Mindtools. Just as early humans used rocks as hardened and forceful extensions of their fists, we now use information technologies as expansions and extensions of our global learning tool sets. If simply overlaid on traditional, knowledge acquisition, instructional models, the technologies cannot reach their potential as integrated extensions of the sensory and intellectual organs of human learners.
Young learners of today have been immersed in an expansive and ceaseless information stream since birth. This immersion produces transformations in information processing responsiveness and receptivity[2] and the technologies have an expanding impact on student motivation to learn. The technologies are serious tools that affect the lives of students with immediacy and with great potential for both good and harm. Thus, a pivotal component for learning with technology tools is that an educated person uses these systems and processes in an informed, ethical, and responsible way. Jonasson, Peck and Wilson define Constructivist Learning Environments as, “technology-based environments in which students can do something meaningful and useful.” Scholarship is not only the application of learning tools to “do something meaningful and useful,” but also recognizing ethical, responsible use, and knowing how to make things of significance with those tools. Ethics and responsibility are centered in the affective domain and they should be addressed at the core of any educational model that relies on embedded information technologies. Education should foster the development of skills and attitudes that can address the social and ethical issues intrinsic to the technological advancements of our society. In concert with a focus on ethics, responsibility and significance is a need for learning power to have precedence over knowledge acquisition. Rand J. Spiro, et.al state that “Various forms of conceptual complexity and case-to-case irregularity in, knowledge domains (referred to collectively as ill-structuredness) pose serious problems for traditional theories of learning and instruction.”[3] An eclectic and generative model of instruction that embeds technologies as part of the global learning toolkit along with a sense of ethics will sustain educational progress. Such a model offers a multifarious approach to learning and the inherent structure of the model gives birth to a prolific set of vehicles or conduits to learning objectives. An epagogic generative model permits learners to take full advantage of the multi-access, multi-channel nature of the digital networked learning environment and particularly well supports a diversity of learning styles. According to Eggen and Kauchak[4], “teaching models are prescriptive teaching strategies designed to accomplish particular instructional goals.” The descriptor, prescriptive, is used to emphasize that the teacher’s responsibilities are clearly defined in planning, implementation and assessment stages of instruction. Traditional models are constructed to achieve specific goals and different models are selected to match particular goals. At the next level of evolution the instructional model itself cultivates new approaches to learning and offers a multitude of connections and channels for the synthesis of ideas. The teacher serves as a guide for the learner’s construction of new schema and monitors the process for evidence of learning. Robert G. Main in Integrating the Affective Domain into the Instructional Design Process[5] states that attention to the affective domain is viewed as being particularly important for technology-based instruction which removes much of the traditional teacher/student interaction from the lesson delivery. New technologies in education require a design strategy that strives to access deep levels within the affective domain as an integral component of the learning process. Educational technologies are well suited to support the concept that learning is a living process; a procreative, self-sustaining, animate thing. Traditionally, learners were viewed as vessels into which knowledge and skills might be poured. It was accepted that some would spill out and some might stay, and thus was education served. It is evident that we now live in a culture rich in powerful, tireless information vessels that exist primarily to extend our cognitive capabilities. Digital networked information systems, as an extension of a learner, place encyclopedic knowledge and powerful representational tools at one’s fingertips. When technologies are fused with an authentic methodology to problem solving and with an emphasis on the inter-relatedness of concepts and processes, learning is internally controlled and mediated by the learner. An epagogic generative model of instruction shapes the learning process as a living heuristic exploration. References: [1] Jonassen, D., Peck, K., & Wilson, B. (1999) Learning With Technology, A Constructivist Perspective. Prentice-Hall, Inc., Upper Saddle River, NJ [2] Strommen, E.F., Constructivism, Technology, and the Future of Classroom Learning, Children's Television Workshop, Bruce Lincoln, Bank Street College of Education, 1992 http://www.ilt.columbia.edu/k12/livetext/docs/construct.html [3] Rand J. Spiro, Paul J. Feltovich, Michael 1. Jacobson and Richard L. Coulson, Cognitive Flexibility, Constructivism, and Hypertext: Random Access Instruction for Advanced Knowledge Acquisition in Ill-Structured Domains, http://www.ilt.columbia.edu/ilt/papers/Spiro.html [4] Eggen, Paul D. and Kauchak, Donald P. (1996) Strategies for Teachers: Teaching Content and Thinking Skills. Needham, MA: Allyn & Bacon [5] Main, Robert (1992) Integrating the Affective Domain into the Instructional Design Process California State Univ., Chico. College of Communication. |
