Research Report 1412

"Smart Technology for Training:

Promise and Current Status

Wayne D. Gray, Daniel B. Pliske, and Joseph Psotka

DTIC S .LECTE

SEP 0 3 190D

YD

Instructional Technology Systems Technical Area

Training Research Laboratory

U. S. Army

Research Institute for the Behavioral and Social Sciences

March 1985

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Technical review by -cTi'* ", . -

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ARI Research Report 1412 r ).

4, TITLE (and SubmidI.) r T PC OF REPOr4T A PERIOD COVEMED

SMART TECHNOLOGY FOR TRAINING: PROMISE AND September 1983-June 1985

CURRENT STATUS a. PrRFORMING ORG. REPORT NUMBER

7. AUTHOR(@) 4. CONTRACT OR GRANT NUMBER(*)

Wayne D. Gray, Daniel B. Pliske, & Joseph Psotka

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18. SUPPLEMENTARY NOTES

IS. KEY WORDS (Conlinue ont reverse @ids It naceemary aid identify by block number)

Artificial intelligence Mental models Intelligent tutors

AI Computer assisted instruction Smart Technology

Cognitive science CAI Smart courseware

Cognitive psychology Computer based instruction HAWK

Problem solving CBI PLATO Training

"20. A rRACT (rc•mlmie s,.ewV-se sl fl recwaew7 and Idewiity by block number)

SSmart Technoloqy represents the application of cognitive and computer science to Army training. A major component of this technology is "intelligent

tutors." The authors discuss differences between intelligent and conventional

CAI and emphasize the "current status" of intelligent tutors. Also discussed

is ARI's effort to produce Smart Technology for CAI developers. The authors

contend that most conventional CAI is "urldursophisticated" and that this problem can be corrected by producing Smart Technology aid!; for CAI (Continued)

DO I "., 1473 EDITION OF I NOV GS IS O.*OLF ,LNI JcM UCN CA AI ,O;F THIT EAD- _LS=I-TO4C) SE URT HSP G W - )f ý.. _
UNCLASSIFIED

SCURtTY Y LASSIFICATION OF TH•S PAGE(Wh' D0.O. r-r'md)

ARI Research Report 1412

20. (Continued)

... developers. A project for the Army Engineer School, which is applying research on problem-solving to engineer training, is discussed.

L.

1I

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* 'A'.
Research Report 1412

Smart Technology for Training:

Promise and Current Status

Wayne D. Gray, Daniel B. Pliske, and Joseph Psotka

Instructional Technology Systems Technical Area

Zita M. Simutis, Chief

Training Research Laboratory

Haro!d F. O'Neil, Jr., Director

U.S. A-MY RESEARCH INSTITUTL FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Aventi e, Alexiodri.;r, VirunliiI 22333-5(600

Office, Deptuty Chief of Staff for Potwvionnl~

0)epinn tnliqon of the Army

March 1985

Army Projoct Numbor Munpowor. Porsonnel. and Training

2Q162722A79 1

Am .. -vea ..for lic lA .1vw' >,Ž;; ( 't itt l )* 'lffl3⁄4 %& .hl,
ARI Research Reports and( TI eci cI HnwQl )vo ,tr, tilntended for sponsors of

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for implementation at the time of publication are presented in the last part

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mendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form.

m

NC

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FOREWORD

One of ARI's roles as an Army research and ue'.nlopment Lgency is to identify important new technologies and encoui'age thel, &pplication to Army problems. Smart Technolc,,y fits into this category.

Smart Technology represents the application of cognitive and computer

science to Army trairi.ag,, A major component of this technology is the creation

of "intelligent" tutors (or intelligent computer-assisted instruction (CAI)).

Intelligent tutors have long been heralded as the next wave of computer-assisted

instruntion. The authors discuss differences between intelligent and conventional CAT and emphasize the current status of intelligent tutors. Further,

the authors conclude that intelligent tutoring systems are no longer just the

"toys" of academia, but are beginning to play a vital role in technical training. Examples of systems being developed by ARI are provided.

Also discussed is ARI's effort to produce Smart Technology for CAI developers. The authors contend that most conventional CAI is "undersophisticated"

and that this problem can be corrected by producing Smart Technology aids for

CAI developers. By way of illustration, a project at the U.S. Army Engineer

School is discussed. This project is an attempt to apply cognitive science

findings on problem solving to PLATO CAI for technical courses for engineers.

Smart Technology may be the best way to meet the training challenge of

the late 1980s and 1990s without a great and unrealistic increase in money and

personnel. We at ARI expect that Smart Technology applications will be welcomed

by training developers as these applications becoqie available.

EDGAI A. JOHNS N

Techiical Director

IA V
SMtRT TECHNOLOGY FOR TRAIAING: PROMISE AND CUHRENT STATUS

EXECUTIVE SUMMARY

Requirement:

The Army's training system is straining under greatly increased demands.

Maintenance and repair of complex, high-technology weapons systems require an

increasing flow of highly skilled and very specialized technicie.ns. Mobilization requires better training for Reserve Components (training that must be

delivered at the home station one weekend a month and be available in 2-week

increments during summer duty). Force Readiness requires qualitatively better

schoolhouse training in less time for the regular Army. Present methods of

training development and delivery are unlikely to meet these demands without

an unrealistic increase in resources.

Product:

This paper provides an introduction to a new technology. "Smart Technology" is defined as the application of cognitive and computer science to Army

training problems. Abstract concepts from cognitive and computer science are

discussed in the context of specific projects that represent both the longterm promise and the current status of Smart Technology. (Note that the projects discussed emphasize two types of Smart Technology: lntelligent Tutors

(intelligent computer-assisted instruction (CAI)), and cognitive science approaches to teaching problem solving in technical domains.)

Use:

The report is intended for managers and developers of Army training. In

recent years cognitive science has grown into a large and fruitful field and

has reached the point where theories are ripe for training applications. Unfortunately, most of the training development community comes from an intellectual tradition and moves in professional circles that do not include cognitive

science. It is our intent to bring cognitive science and the applications of

Smart Technology to the attention of the training community. We expect that

such approaches will shed new light on old training problems and that putting

the ideas of cognitive science to the test of training applications will help

define the nature and limits of those ideas.

-,-
SMAIT TECHNOLOGY FOR TRAINING: PROMISE AND CURRENT STATUS

CONTENTS

Page

O V E R V I E W . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . .

Definitions . . . . . . . . . . . . . . .

ProJects . . . . . . .. . . . . ... . . . . . . . . . 2

LONG-TERM' DEVELOPMENTS . . 9 . . . . . . . . . . *. . ................ 2

EXPLORATORY DEVELOPMENT ..... . ............................... 4

The HAWK MACH-III . . . . . . . . . . . . . . .a. . . . . . . 5

APPLICATIONS RESEARCH: SMART TECHNOLOGY FOR CAI DEVELOPERS . . .0. . . . 6

"Obstacles to Learning . . . . . . . . . . 0 # # * . a . 0 . . . 0 * . 0 6 . Facilitating Learning * % . * # * * @ * e a * * * * . . . . * . . . 8 

SUMMARY: CONTINUING EFFORTS . . . . . . * . . a * * * * s e 9 a . 1 i 

REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . 3

LIST OF FIGURlES

Figure 1. "Naive physics" . . * 0 . . . . 0 . . & . 0 . . . . . . . . 4

2. Linear (traditionitl) two-column proof for geometry problems . 7

3. Hierarchical proof structure for geometry problems ..... ..... 9

4. An expert's search through a subset of' the geometry

"problem space . .O............................................10

.ix9 

IL1A' 1 9

W '9Pr,,..."
SMART TECHNOLOGY FOR TRAINING: PROMISE AND CURRENT STATUS

OVERVIEW

The Army's training system is straining under greatly increased demands.

"* Maintenance and repair of complex, high-technology weapons systems

require an increasing flow of highly skilled and very specialized

technicians.

"* Mobilization require3 better training for Reserve Components (training that must be delivered at the home station one weekend a month

and be available in 2-week increments during summer duty).

"* Force Readiness requires qualitatively better schoolhouse training

in less time for the regular Army.

To meet these demands, present methods of training development and delivery

would require an unrealistic increase in resources. However, an alternative

exists: Smart Technology.

In this paper we attempt to explain the jargon and to provide an introduction to this new technology. Abstract concepts are discussed in the context

of specific projects that represent both the long-term promise and the current

status of Smart Technology.

Definitions

Smart Technology represents the application of cognitive and computer

science to Army training problems. Cognitive science takes an information

processing approach to the study of human cognitive processes. Of particular

importance to trainers is the recent focus on the nature of expertise including expert versus novice, "naive" theories, mental models, and problem solving in technical domains. (For a recent review of the importance of cognitive

science to the national interest, see the National Academy of Sciences, 1983.)

SISmart Technology uses three aspects of the new computer technology. The

first aspect is the well-publicized availability of sophisticated technology

at a low cost. The availability of special symbol-manipulating, or LISP, machines is the second aspect; these machines were developed specifically as

tools for research in cognitive science and applications of artificial intelligence (Al). The third aspect is the AI technology of expert systems and

intelligent tutors (or computer-assisted instruction--CAI).

In many cases, the cognitive science side of' Smart Technology can be applied via "chalk and talk," paper-based, or standard CAl techniques. However,

in other cases, the application of Smart Technology requires a delivery vehicle

that can interactively model a soldier's current knowledge and probtem-solvilng

strategies, compare this with what an expert would do, and in real time, design

and deliver instruction. The new computer technology 1)rovi(!es a vehicle caapable otf meeting these needs. (For a "popular" disciiiision o' this new coriputer

1
technology, see Feigenbaum & McCorduck's book Japan and the Fifth-Generation

Challenge, 1983.)

Proj ects

For the past 2 yearB the Army Research Institute's (ARI) Smart Technology

for Training team has monitored research in cognitive and computer science to

determine its applicability to Army training. First, the team has identified

areas that if nurtured will help meet the training needs of the Army in the

1990s. At present ARI is supporting long-term development efforts by some of

the nation's best cognitive and computer scientists.

Second, there is a criticpl gap between the skills soldiers must have to

maintain and repair high-tech systems and the capability of the Army school

system to train these skills. (Currently, some 80% of these skills are taught

on the job, not in the school.) Clearly, bold solutions are required to bridge

this gap. Pai't of the solution lies in the development of "intelligent" or "smart" maintenance tutors capable of delivering high-quality technical training in less time and at less cost than current training. ARI is supporting an

exploratory development effort to build a prototype and is working on a smart

maintenance tutor for the HAWK Air Defense System.

Third, there are many findings from cognitive science research that can

be app' ied now to improve training developments. Research to apply this knowledge to improve conventional CAI is currently underway.

LONG-TERM DEVELOPMENTS

Long-term development projects have been funded through joint ARI/ONR

(Office of Naval Research) contracts. Several of these projects relate to the

development and use of mental models of physical phenomena and devices.

People use mental models as a basis for predicting outcomes and for planning and reasoning. An example of a simple but erroneous mental. model is the

"stove model" of thermostat control. Anyone who has walked into a cold house

at the end of the day and set the thermostat at 90" is using the stove model.

This model treats the central heating system as analogous to a gas stove: the

higher the setting, the higher the flame- the higher the flame, the greater

the heat; the greater the heat, the faster the pot boils; hence, the higher

the thermostat setting, the faster the 1iouse heats. In the correct model the

thermostat is viewed merely as an on/off switch. Setting the thermostat at

90' will not cause the house temperature to ,each 68' any faster than setting

it at 68'. (See Gentner & Stevens, 1983, for in-depth discussions of mental

models.)

One of the projects is examining how mental models of electric circuits

influence the learning of troubleshooting strategies *nd, conversely, how

training in troubleshooting influences the mentai mo'lels that are developed.

For example, people ofteni view the flow of' electric current as analogous to

either "flowing water" or "teeming crowds" (Gentner & Gentneý', 1983). These

models produce different patterns of errors in reasoning about electric circuits. Does the effective use of different troubleshooting strategies require
the support of different models of current flow? Can one model of current flow

be found that supports the application of different troubleshooting strategies

to different problems? The goal is to find some combination of models and

strategies that results in flexible and effective troubleshooting behavior and

that can also be easily learned.

Forming mental models of physical phenomena is so pervasive that people

will form models based upon incomplete and erroneous information. A wrong

model may be just incomplete, that is, a preliminary stage in the development

of more accurate and complete understanding. In contrast, however, misconceived

models must be unlearned before the correct model can be acquired. For example,

when given the coiled tube shown in Figure 1 and asked to predict the path of a

ball after it exits the tube, about half of the college students tested said

that the ball would continue to curve. A few students said that the ball would

circle the coil (McClosky, 1984). Apparently, these individuals have a preNewtonian "impetus" model of this phenomenon. (Passing through the coil imparts a "circular impetus" to the ball, which is gradually lost after the ball

exits.) McClosky found that even students who had taken a college physics

course still held some form of impetus theory. Rather than supplanting "naive"

models of physics, college instruction coexisted or was incorporated into the

students' preexisting mental models.

There is a growing appreciation that much of what a good human tutor does

is to discover students' misconceptions about a domain and provide examples or

instructions that discredit these intuitive models and lead to the adoption of

accurate ones. ARI is supporting two projects that examine these aspects of

mental models and tutoring. The first project supports the development of an

AX-based medical consultation and explanation system. By interacting with the

student, the system constructs a dynamic model of the student's knowledge and

diagnostic reasoning abilities. The system is able to tailor instruction to

support the student's development of accurate mental models and to help discredit misconceived models. The second project supports an Al-based system

that identifies a student-programmer's errors and relates these to the set of

plans and subp).ans the student used to write the program. The goal is to use

errors to diagnose the student's underlying misconceptions about programing

and then target instruction to correct these misconceptions.

Another ARI-supported effort is investigating how experts in a domain go

from a problem statement to making inferences about the problem. The goal is

to implement these patterns of inference as a computer model and to compare

the inferences experts make to those that students make.

The last ARI/ONR project described here is an attempt to develop Al textgeneration techniques that can be used to teach reading skills to adults. In

the current prototype, text generation is embedded in a gaming situation that

requires the student to advance in a fictional organization (for example, an

Armor Company). The system will generate appropriate written responses to the

student's moves in the game, administer diagnostic tests for isolating problems

in reading comprehension, and adaptively introduce novel materials based on the

results of the diagnosis. Once the text-generation techniques are developed,

the content of the gaming situation can be easily altered. The long-term application is to build a family of intelligernt tutors in which soldiers can acquire job knowledge as they improve their reading skills.

3

L%- `
II

Figure 1. "Naive physics." In accord with medieval impetus theory, half of

the college students tested predicted that a ball shot through a

coiled tube would continue to curve after exiting.

EXPLORATORY DEVELOPMENT

ARI is supporting Xerox Palo Alto Research Center (PARC) in the exploratory development of a prototype smart tutor to train high-level diagnostic and

troubleshooting skills for repair of a complex reprographics system. The

chosen system represents a stable, high-technology testbed (with electronic,

mechanical, chemical, and electro-optic components). These complex, interacting technologies require the sophisticated diagnostic and troubleshooting skills

vital for Army systems in the late 1980s and 1990s. Also important is the fact

that the system is not clas3ified and has an existing training program with

which to compa' Ž the smart tutor. Measures of on-the-.job perform.-nce of smarttutored versus conventionally trained technicians will be obtained. The ultimate proof" of the tutor will be whether Xerox uses it for in-house training.

The mnin goal of this effort is not the prototype tutor per se, but the

sof tware tools, progrwning environments,, and modeling techniques that such a

tutor' requires. Once these tools, environment, and techniques are developed

p.
and tested, they will be used to develop a, family of smart tutors for training

maintenance and troubleshooting for Army weapons systems.

Tnose researchers who have watched the development of CAI systems such as

PLATO and TICCIT will understand the importance of building specialized software tools and programing environments to expand the access to new technology.

However, the role of mental models and the importance of developing techniques

to construct such models may not be obvious.

As an example, a smart tutor for teaching radar maintenance requires four

different models: first, a straightforward computer model of the radar system

itself--such device models are becoming standard in many conventional computerbased training systems; second, a model of how experts think about the device

while troubleshooting.--an experts' mental model of the device; third, a theorybased model of a,, expert instructor--this model includes pedagogical strategies

such as when to let a student puraue a wrong answer and when to provide feedback; fourth, the capability to interactively create a model of the student's

knowledge--including their understanding and misconceptions. (For a discussion

of smart tutors, see Sleeman & Brown, 1982, and Bregar, 1983.)

In a smart tutor, these models interact so that at all times the feedback

provided and problems presented are appropriate to the student's current state

of knowledge. This technology peomises smart tutors that will generate feedback and problems to meet each student's unique needs. This generation is in

contrast to the best conventional CAI in which students' responses result in

their being branched down one of a limited number of predetermined paths.

The HAWK MACH-III

The Maintenance Computer for HAWK--Intelligent Institutional Instructor

(MACH-III) represents the first attempt to build a smart tutor for Army training. The HAWK provides over 50% of the air defense for Army units. Current

training costs run from $10,000 to $50,000 per radar maintenance trainee, yet

a recent study identified training as a major problem area that has plagued

HAWK from the outset. Even a small increase in training effectiveness should

more than pay back the cost of developing and fielding the MACH-III.

Our approach at. ARI is to pinpoint areas where the MACH-III has the most

to contribute. We will determine what the major radar maintenance problems

are and how these problems are addressed by the current training system. Generally, we will be interested in how Smart Technology can be used to improve

existing training methods, and specifically, how a smart tutor can be used

with greatest leverage.

The application of smart tutors to HAWK training is ideal. Training problems for the HAWK have been recognized as large and costly. Good conventional

training strategies (that is, chalk and talk, CAI, and videodisk) have been

tried, but still a large deficit remains. The proponents for the HAWK recognize this problem and are willing t' try a new approach.

The MACH-Ill represents a target of opportunity. The high cost of' development is more than justified by the potential to improve maintenance training.

The experience gained in developing the MACH-Ill will be on important test of
newly developed software tools, programing environment, and modeling techniques.

Experience gained in using the MACH-hI, will provide guidelines for the most

effective use of smart tutors. Finally, the experience gained in building the

MACH-III should cut down the time and costs involved in applying smart tutors

to other training problems and help transfer smart tutor technology to the Army's

training development community.

APPLICATIONS RESEARCH: SMART TECHNOLOGY FOR CAI DEVELOPERS

It is our contention that most conventional CAI is undersophisticated,

that is, (a) does not take advantage of what we know about human learning, and

(b) does not come close to exploiting the power of the computer. We believe

that the best way for the Army to increase the return on its investment in CAI

is to put Smart Technology in the hands of CAI developers.

Toward this end, we are working with the U.S. Arvy Engineer School (USAES)

to develop and implement CAI in the Engineer Officer Advanced Course (EOAC).

First, as advisors, we have helped organize and train a CAI courseware cell and

are now assisting in the formative evaluation of courseware. Second, as researchers, we have taken a hard look at the nature of the technical courses

being taught and the traditional ways (both chalk and talk and CAI) of teaching them. The Engineer School teaches its Captains a variety of technical

courses in Civil Engineering (for example, bridge design, soil analysls, flexible pavements, and so on). The goal of these courses is to teach Engineer

Captains how to solve problems in each of these subdomains.

The simple observation that Engineer Captains are taught how to solve

problems in various technical domains led us to compare the nature of EOAC

instruction with findings of cognitive science on problem solving in other

technical domains (such as physics, chemistry, geometry, and programing). As

a result of these comparisons, we have begun research designed to apply cognitive science theories to the design of instruction delivered on a conventional

PLATO CAI system.

Obstacles to Learning

One research goal is to eliminate obstacles to learning embedded in conventional technical instruction. Thuse obstacles are not unique to the Engineer School, but are found in all such instruction.

Identifying the Goal Structure, The goal structure of a problem is the

"path" taken from the problem statement to its solution. Instruction should

teach students how to traverse this path but usually does not. In fact, most

instruction on goal structures contains obstacles to learning. For example,

Figure 2 presents a traditional two-column proof for a geometry problem. After

learning a few postulates and theorems, the student is shown one or two of

these proofs and then is asked to solve problems (Anderson, Boyle, Farrell, &

Reiser, 1984).

This "linear" proof' structure is very misleading. First, by definition,

the proof' provides no sense of' hierarchical relationship among the steps.

6
GIVEN: ? -- X-Z

T LWMY = ZTMZI

M midpoint of YZ

PROVE: T =- ZW

Y Mz

STATEMENT REASON

M is midpoint of T2 Given

Y M M Z Definition of midpoint

x Y Given

ZXYZ - LXZY base angles of

isoceles triangle

ZWMY Z LTMZ Given

L•WMY - LTMZ Angle-side-angle (ASA)

W'Y - TZ Corresponding parts

L/WYZ Z STZY Side-Angle-side (SAS)

Y T Z W Corresponding parts

I,'igure 2. Linear (traditional) two-coLumn proof tCor geometry probLems.

(Adapted frori Anderson, BoyLe, Iarrell , & HeiBer, t084.)

-I• y
Second, it is not clear to the student whether, the order of the steps is accidental or essential. These failings are easily overcome by teaching a hierarchical proof structure as shown in Figure 3.

Searching a Problem Space. The problem space includes an individual's representation of the objects in the problem situation, the goal of the problem,

and the actions that can be performed and strategies that can be used in working

on the problem. It also includes knowledge of constraints in the problem situation: restrictions on what can be done, as well as limits on the ways in which

objects or features of objects can be combined (Greeno & Simon, 1964, p. 4).

The search for a problem solution involves a search through some subset of this

space. For example, Figure 4 shows the search through a subset of the geometry

problem space that an expert made while solving the problem given in Figure P.

The numbers indicate the sequence of the search.

The linear proof given in Figure 2 provides no inkling of the fa&se starts

that the expert went through. The novice is led to believe that all such problems are solved in a strictly linear fauhion--starting with the givens, and

generating the next step, until with clock-like precision an answer is found.

Experts do not solve problems by this process; unfortunately, however, experts

do teach this way.

The consequence of these obstacles is that the problem-solving process is

never explicitly taught but is )eft to the student to discover alone. This

"discovery" learning often leads to frustration or failure and can actively

retard the development of expertise.

Facilitating Learning

We plan to replace embedded obstacles with embedded facilitators. First,

for selected engineering subdomains, we plan to discover the goal structure

underlying successful problem-solving and to communicate that goal structure

to the student.

Second, typical instruction separates information about the task (usually

presented by lecture and text.) from performance of the task. At best, performing the task is regarded as a chance to practice (not acquire) knowledge and

skillB. We plan to revise this procedure and to create problem-solving con-

"* texts in which students acquire knowledre and skills as they perform the task.

S

*'• Creating problem-solving contexts should farn.ilitate learning for- several

re&.sons (Anderson, et al., 1984). First, many studies have shown that human

memory is partially context dependent. If performing a task requires a problemsolving activity, then instruction in the facts and theories of t:e task domain

should take place in a problem-solving context. Additionally, mary concepts are

hard to understand when presented in the abstract. Teaching these concepts iin a

problem-solving context provides a concrete example of the concept and how it is

used, Finally, there is the problem of the applicability of int'ormation. When

facts are taught in the abstract, students may be perfectly capable of demnonstrating• that they know these facts (by tests of recall or recognition) but rnot recognize that the fact is relevwnt in a particular context. Btudents can access mereory, but they do not know when to 9pply it. Teaching Vacts in a problem-solving

*. conteift assists students in learning the "goal relevance' of' knowledge.

"'' "41 "% '2• '"rA • f• & " ' "".k...... """•... . 8... .. ... .. • " ., [.'' "'."@"q". . . .. '. . . ..
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Parts (2) Parts (3) Parts (11)

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W-X XT

SAS (10) - - Segment

WM 21 UT Subtraction (9)

WY=-T Z

Corresponding At

Parts (7) Corresponding

x/ Y T Parts (8)

M z ASA (6)

/..WMZ /-YMT" IYM i MZ LXYZ •-LXZY

Supplementary (4) Midpoint (1) Isoceles (5)

LWMY = ZTMZ I M midpoint of YZ XY xz

Gi.ven Given Given

Figuire )4. Al exp-,rt 's searth through a. ilbuet of' the , ieomnetry prob [ ini )c.

AdaptI)Led rrom AntIersoi I, Bo.y •e, ,arre I t L , Ho i wer , 1961,).

1()
4

The third way we intend to facilitate learning is by reducing working nemory failures. Anderson, et al. (1984) argue that working memory failures are

(a) the major source of errors during learning, (b) an additional limitation on

the learning rate, (c) a barrier to certain effective types of problem solving

(such as backwards reasoning in geometry), and (d) a cause of incorrect retrieval

from long-term memory. Since it is generally acaepted that working memory expands with expertise, the problem is how to minimize the working memory load of

novices while they acquire the long-term memory structures prerequisite to a

larger working memory. (Working memory is viewed as domain dependent. An expert

with a large working memory in a domain of expertise would not have a large working memory available in an unfamiliar domain.)

Rather than yielding one large product, our applications should produce a

stream of' smart facilitators that can be embedded in PIATO (or any conventional)

CAI. Many ideas will be rapidly developed and tested using LISP machines. Then

the most successful ideas will be translated into TUTOII (the language of PLATO)

as prototype lessons and compared with existing instruction. The prototype lessons that prove effective will be immediately made available to the Engineer

School. More important, those ideas that prove successful will be immediately

transferred to the Engineer School's courseware developers to incorporate into

their own lessons. In this way we will watch closely the process of technology

transfer to assist the movement of Smart Technology aids from the laboratory to

the user. Our ultimate goal is to make all validated Smart Technology aids accessible to the Army training community.

SUMMARY: CONTINUING EFFORTS

One of AmI's roles as an Army research and development agency is to identify important new technologies and encourage their application to Army problems. We plan a concerted effort to make Smart Technology a standard tool of

the Army training community.

Spreading the word is the keystone of our effort. In recent years cognitive science has grown into a large and fruitful field and has reached the

point where theories are ripe for training applications. Unfortunately, most

of the training development community comes from an intellectual tradition and

moves in professional circles that do not include cognitive science. Our intent is to bring cognitive science and its Smart Technology applications to the

attention of the training community. We expect that Smart Technology will shed

new light on old training problems and that putting cognitive science ideas to

the test of training applications will help define the nature and limits of'

those ideas. (For a discussion of the application of artificial intelligence

to training, see Psotka,, 1963. For a discussion of how cognitive science is

changing the nature of learning theory, see Gray & laniza, 1983.)

There are an increasIng number of successful training programs based on a

"Smart T'echnology approach. These programs include such long-staniding conundrums

as literacy training (Wisher, 1983; Wisher & O'Hlara, 1981) and technical writing

(Hedish, Felker, & Hose, 1981). While these programs demonstrate the effectiveness of' Smart Technology, their developers tend to come from and pubLi-,h outs•idu

the traditional training development community.

11

* *. - C - b . I.tlt . -. * ,- ..* ..... k ..... & - .... .. .. - . • A°' '. <' . ./' ' ."/ ,." '- ,.
Demonstrating the effectiveness of Smart Technology is also part of our

continuing effort. The smart tutor for a complex reprographics system is intended as a demonstration. Arrangements have been made with Xerox to compare

the effectiveness of the smart tutor with conventional instruction. Students

will be Xerox field technicians. Measures of effectiveness will be based upon

on-the-job performance.

Also, we expect that our work with the Engineer School and on HAWK MACH III

will result in impressive demonstrations of Smart Technology's effectiveness.

Handbooks and guidelines for the application of Smart Technology to train- 4ng do not exist. This lack severely limits the widespread application of Smart

Technology to training. At present, our efforts at creating guidelines are focused on evaluating software tools for building smart tutors. As these tools

are tested, guidelines will be developed and made available to the training

community.

In summary, we see the current examples of Smart Technology as just the

beginning. Other teams at ARI and at Air Force and Navy laboratories are moving

quickly to exploit this emerging technology. Smart Technology is the only way

to meet the training challenge of the late 1980s and 1990s without a great and

and unrealistic increase in money and personnel.

1

I •,
REFERENCES

Anderson, J. R,, Boyle, C. F., Farrell, R., & Reiser, B. S. (1984). Cognitive

principles in the design of computer tutors. Pittsburgh, PA: CarnegieMelon University, Advanced Tutoring Project.

Bregar, W. S. (Ed.). (1983). Intelligent computer-assisted instruction (Special

issue). Journal of Computer-Based Instruction, iO(l&2).

Feigenbaum, E. A., & McCorduck, P. (1983). Japan and the fifth-generation challenge. Reading, MA: Addison-Wesley.

Gentner, D., & Gentner. D. R. (1983). Flowing waters or teeming crowds: Mental models of electricity. In D. Gcntner & A. L. Stpvens (Eds.), Mental

models. Hillsdale, NJ: Iawrence Erlbaum Associates,

Gray, W. D., & Hamza, A. N. (1983). Cognitive task analysis and the new learning theory. Proceedings of the Technology of Training Evaluation & Productivity Assessment Conference (pp. 66-68). Warrenton, VA: Society for

Applied Learning Technology.

Greeno, J. G., & Simon, H. A. (1984, February). Problem solving and reasoning

(Tech. Report UPITT/LRDC/ONR/APS-14). Pittsburgh, PA: Learning Research

Development Center. To appear in R. C. Atkinson, R. Herrnstein, G. Lindzey, & R. D. Luce (Eds.), Stevens' handbook of experimental psychology

(Revised Edition). New York: John Wiley & Sons.

McClosky, M. (1984). Cartoon physics. Psychology Today, 18, 52-58.

National Academy of Sciences. (1983). Report of the research briefing panel

on cognitive science and artificial intelligence. Research Briefing 1983.

Washington, DC: National Academy Press.

Psotka, J. (1983). Artificial intelligence contributions to training and productivity. Proceedings of the Technology of Training Evaluation & Productivitl Assessment Conference (pp. 24-30). Warrenton, VA: Society for

Applied Learning Technology.

Redish, J. C., Felker, D. B., & Rose, A. M. (1981). Evaluating the effects of

document design principles. Information Design Journal, 2, 236-243.

Sleeman, D., & Brown, J. S. (1982). Intelligent tutoring systems. New York:

Academic Press.

Wisher, R. A. (1983). A generative approach to technical reading improvement

by couputer. Proceedings of the Technology of Training Evaluation & Productivity Assessment Conference (pp. 45-49). Warrenton, VA: Society for

Applied Learning Technology.

Wisher, R. A., & O'Hara, J. W. (1981). Computer-baged approach to the Navy'

academic remedial training, pro ect PREST: A cost effectiveness evaluation. (Special Report 81-18). San Diego, CA: Navy Personnel Research &

Develtopmient Center.

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