Technical Report 453

THE USE OF TACTICAL COMPUTERS

TO PROVIDE WEAPONS AND TACTICS TRAINING

-TO COMBAT NCOs: RESULTS OF A FIELD TEST

William G. Hoyt

System Development Corporation and

1'* James D. Baker

Army Research Institute

MANPOWER AND EDUCATIONAL TECHNOLOGY TECHNICAL AREA

U.S. Army

Q~) Research Institute for the Behavioral and Social Sciences

- .July 1980

Approved for public release; distribution unlimited.

82 04 21 042
U. S. ARMY RESEARCH INSTITUTE

FOR THE BEHAVIORAL AND SOCIAL SCIENCES

A Field Operating Agency under the Jurisdiction of the

Deputy Chief of Staff for Personnel

L. NEALE COSBY

JOSEPH ZEIDNER Colonel, IN

Technical Director Commander

'1

Research accomplished under contract

to the Department of the Army

System Development Corporation

NOTICES

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Please address correspondence concerning distribution of reports to: U.S.

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THE USE OF TACTICAL COMPUTERS TO PROVIDE WEAPONS Interim - Dec. 72 to Dec. 73 AND TACTICS TRAINING TO COMBAT NCOs: RESULTS 6. PERFORMING ORG. REPORT NUMBER

OF A FIELD TEST

7. AUTHOR(s) 8. CONTRACT OR GRANT NUMBER(a)

William G. Hoyt (SDC) and James D. Baker (ARI) Contract DAHC19-73-C-0029

9. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELEMENT. PROJECT, TASK

System Development Corporation, Santa Monica, CA AREA & WORK UNIT NUMBERS

Army Research Institute for the Behavioral and 2Q062106A721

Social Sciences, Alexandria, VA 22333

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July 1980 U.S. Army Deputy Chief of Staff for Personnel 13. NUMBER OF PAGES

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IS. SUPPLEMENTARY NOTES

*

1IS. KEY WORDS (Continue an reverse side If neceary and Identify by block number)

Automated instruction Army Tactical Data Systems

Computer-assisted instruction Field test and evaluation

Computerized testing Military training

PLANIT author language Embedded training

20A9LTNACT (rmw s evr s e nagew.mp. awl dseity by block nhmbet)

'. A pilot field test in 1974 demonstrated the effectiveness of using an Army tactical data system for computer-assisted instruction (CAI) of soldiers when

the system was not being used for tactical operations.

Courseware was written in the existing author language PLANIT to run on the Army's Developmental Tactical Operations System (DEVTOS), at Fort Hood, Tex.

Lessons were developed in two proficiency test areas, Crew Served Weapons and

(Continued)

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Item 20 (Continued)

Tactics, and the 120 participating noncommissioned officers (NCOs) were chosen

for their previous low scores in these subjects. After a pretest, half the

group studied with CAI, a fourth studied from Army manuals, and a fourth

served as a control group by studying unrelated material. A posttest measured

learning, and interviews with the CAI group recorded their enthusiastic

comments.

The CAI group learned significantly more than the others did. The automated instruction also worked equally well for fast and slow learners and

across differences in age, education, and paygrade. Slow learners seemed to

use the same strategy as fast learners but to take more time. The results,

therefore, demonstrated the feasibility and benefits of the project.,_

c.051.O11 

For

VTIS~ ORA&I DTIC TA

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Availlbity COdO4. o.

A,,,1 i. 1d)..r copy

isOECsE

I ii Unclassified

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I-
Technical Report 453

THE USE OF TACTICAL COMPUTERS

TO PROVIDE WEAPONS AND TACTICS TRAINING

TO COMBAT NCOs: RESULTS OF A FIELD TEST

William G. Hoyt

System Development Corporation

and

James D. Baker Army Research Institute

Submitted by: James D. Baker, Chief

MANPOWER AND EDUCATIONAL TECHNOLOGY TECHNICAL AREA

Approved by:

Edgar M. Johnson, Director

ORGANIZATIONS AND SYSTEMS

RESEARCH LABORATORY

U.S. ARMY RESEARCH INSTITUTE FOR THE BEHAVIORAL AND SOCIAL SCIENCES

5001 Eisenhower Avenue, Alexandria, Virginia 22333

Office, Deputy Chief of Staff for Personnel

Department of the Army

July 1980

Army Project Number Command Systems

20062106A721

Aprv for public releams; distribution unlimiud.

Mii
ARI Research Reports and Technical Reports are intended for sponsors of

R&D tasks and for other research and military agencies. Any findings ready

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

of the Brief. Upon completion of a major phase of the task, formal recommendations for official action normally are conveyed to appropriate military

agencies by briefing or Disposition Form.

i i

j=
rT FOREWORD

otmThe Manpower and Educational Technology Technical Area of the Army Research Institute for the Behavioral and Social Sciences (ARI) conducts ongoing research on computer-based educational systems (Army Project

2Q162722A791, FY 80) and training simulation (Army Project 2Qh63744A795,

FY 80). The forerunner of the current research is described in this report.

An effort in the Command Systems Work Unit of ARI had been designed to

optimize cormanders' use of Army tactical data systems for command and staff

information processing and decisionaking, by developing software packages

that would use the actual system as the instructional vehicle for training

users and maintaining their proficiency. The research reported here stemmed

from that effort and from earlier in-house laboratory independent research

(ILIR) efforts.

This research used tactical data systems in acomputer-assisted instruction (CAI) mode to support MOS 11B40 infantry training at the combat unit

level. The particular problem area was selected when the training of 1IB40

soldiers came up as a critical item in the report of the Board for Dynamic

Training in 1972 and in the Continental Army Command (CONARC) Task Group Report on Computer-Assisted Instruction in 1972.

ARI programs are conducted as in-house research augmented by contracts

with organizations having unique capabilities in the area. Much of this

experiment was conducted by personnel of the System Development Corporation

(SDC) under contract DAHCl9-73-C-0029. The entire effort responded to requirements of Project 2Q062106A721, Human Performance in Military Systems,

FY 1973 Work Program, and to special requirements levied by what were then

the Assistant Chief of Staff for Force Development and the Director of Army

Research, Office of the Chief of Research and Development. Current programs

are responsive to requirements of the Army Training and Doctrine Command

(TRADOC), the successor to CONARC.

The essential contents of this paper were presented at the 16th Annual

Conference of the Military Testing Association (MTA), 21-25 October 1974 at

Oklahoma City, Okla. and appear in the Proceedings of that meeting.

0 EPH ZEIDER

hnical Director

v
THE USE OF TACTICAL COMPUTERS TO PROVIDE WEAPONS AND TACTICS

TRAINING TO COMBAT NCOs: RESULTS OF A FIELD TEST

BRIEF

Requirement:

To determine in a field test whether Army tactical data systems can be

used for the secondary purpose of computer-assisted instruction (CAI) to

support unit training requirements when the systems are not needed for tactical operations.

Procedure:

Subject matter was chosen from areas that light-weapons infantry in

military occupational specialty (MOS) 11B40 must know to pass their proficiency tests. Courseware was written in the existing Programming Language

for Interactive Teaching (PLANIT) authoring language, to be run on the Developmental Tactical Operations System (DEVTOS) installed at Fort Hood, Tex.

Participants were 120 NCOs in MOS 1IB40 at Fort Hood, who were chosen because

they had scored low on their last MOS proficiency test.

All participants were given a pretest and assigned to one of three

groups. One group received CAI on either Crew Served Weapons or Tactics;

the second studied the same material from Army publications and manuals in

a structured setting; and the third (control) group studied an unrelated

assignment. After about 4 hours all participants were given a posttest, and the computer-instructed group were interviewed to obtain their reactions.

Findings:

Four major findings came out of this effort:

1. Statistically significant evidence showed that learning did take

place when tactical data systems were used in a secondary role for

automated instruction (AI) and that Al was more effective than

self-study.

2. The AI method of training was equally effective in providing weapons

and tactics training to 1lBO personnel with high or low general

technical (GT) scores across the range of 11B40 GT scores.

3. Automated instruction appeared to be effective across age groups,

across education levels, and across the paygrade of the 1IB40

population.

4. The slower learners used the same strategy and essentially the same

paths through the AI course as the faster learners, but they required

more time to read and comprehend the material.

vii
Utilization of Findings:

The results from the U.S. Army's MASSTER Test FM 122, IBCS: Automated

Instruction demonstrated the feasibility of using these systems in a standalone mode in support of soft-skills (nontechnical) unit training requirements and paved the way for the current variety of systems.

Vii

~viii
THE USE OF TACTICAL COMPUTERS TO PROVIDE WEAPONS AND TACTICS

TRAINING TO COMBAT NCOs: RESULTS OF A FIELD TEST

CONTENTS

Page

INTRODUCTION..................................1

APPROACH..................................

Subject Matter and Courseware Considerations..............1

AI Authoring Considerations.......................2

Subject Selection............................3

Conduct of the Field Test........................3

-1 RESULTS..................................4

Between-Groups Comparison .. ....................... 4

Relationship of Data to GT (General Technical)*Score .. ........ 7

Other Variables Examined........................7

Qualitative Results..........................10

I. SUMMARY AND CONCLUSIONS.........................11

REFERENCES................................13

DISTRIBUTION...............................15

LIST OF TABLES

Table 1. Test design--treatment of participants ................ 3

2. Crew served weapons: group means and standard deviations . 5

LIST OF FIGURES

Figure 1. Relationship of between-groups findings for the LAW . . . . 6

2. Relationship of between-groups findings for tactics . . . . 8

3. Relationship of pretest, posttest, and gain scores

to GT scores..........................9

ix
THE USE OF TACTICAL COMPUTERS TO PROVIDE WEAPONS AND

TACTICS TRAINING TO COMBAT NCOs: RESULTS OF A FIELD TEST

INTRODUCTION

Anticipating the smaller all-volunteer force, the decentralization of

training, and the added emphasis on individual and group instructional activities at the unit level, thq Office of the Chief of Research and Development of

the U.S. Army undertook an investigation in 1973 to determine whether the Army

Tactical Data Systems' (ARTADS) projected network of computerized systems could

be used to support combat unit training activities. The study reported here

supported that effort. The objective of this research was to determine the extext to which Army tactical data systems could be used in a secondary, automated instruction (AI) role directed toward satisfying training requirements in

a tactical unit environment. The systems measurement bed (Uhlaner, 1972) used

for this assessment was the U.S. Army's Developmental Tactical Operations System (DEVTOS), located at Fort Hood, Tex. Detailed descriptions of DEVTOS have

been presented elsewhere (Baker, 1968, 1972, 1973); it is a mobile automatic

data processing (ADP) system intended to aid commanders and their staffs in

conducting tactical operations by collecting, processing, and summarizing the

information they need for command decisions and staff actions. Given the requirement and the system, it was necessary to identify the subject matter to be

taught, select the individuals who would take part in the study, develop the

courseware necessary to teach it, develop a test plan for conducting the field

test (designated as MASSTER Test FM 122, IBCS: Automated Instruction), conduct

the field test, and analyze the results. This report details that undertaking.

APPROACH

Subject Matter and Courseware Considerations

The maintenance of proficiency by noncommissioned officers (NCOs) in military occupational specialty (MOS) 11B40, Light Weapons Infantry, had been identified by the Combat Arms Training Board and the Continental Army Command

(CONARC)i as a significant unit training problem. Among the four subject matter areas that 11B40 personnel were required to know, Tactics and Crew Served

Weapons accounted for most of the proficiency test failures; therefore these

subject matter areas were selected for AI courseware development. Another

area, General Educational Development (GED), was also examined during the

study, but the results of that aspect of the research are reported elsewhere

(Hoyt, Butler, Bennik, & Baker, 1980). The training analysis (and subsequent

recording of results) followed the principles and concepts contained in CONARC

Regulation 350-100-1, Systems Engineering of Training. The data forms used to

structure the AI courseware included training analysis information sheets, criterion and enabling objectives, test items, and course outlines. For crewserved weapons, subject matter experts from the Weapons Committee of the Infantry School reviewed the training analysis results page by page, concurred

1N

INow the U.S. Army Training and Doctrine Command (TRADOC).
(or non-concurred) in the topic selection, and ranked each weapon as to its

relative importance in the Crew Served Weapons Course. Three weapons ranked

highest: LAW (light antitank weapon), 90mm recoilless rifle, and M60 machine

gun. The resultant materials provided a basis for developing 12 hours of Al

Crew Served Weapons courseware, although only 4 hours of the material was subsequently used in MASSTER Test 122. A similar procedure was used to provide

12 hours of Al Tactics courseware.

Al Authoring Considerations

The first task was to analyze the DEVTOS hardware and software to deter- mine whether or not it could support computer-aided instruction (CAI) and to

survey and analyze existing CAI programs and procedures to determine their

availability and feasibility for use within DEVTOS. The results of this phase

are documended elsewhere (Hoyt, Butler, Bennik, & Baker, 1975, 1980). Essentially it was found that the PLANIT CAI system (PLANIT stands for Programming

Language for Interactive Teaching) would best satisfy the requirements (Frye,

1968; Feingold, 1968; Frederick, 1974; Atkinson & Wilson, 1969). Consequently,

the courseware was written to run on the PLANIT/DEVTOS system.

In the development of course materials, every attempt was made to capitalize on PLANIT capabilities that assist the author preparing the instructional

material and the student receiving it. The most elementary of the presentation strategies was a straight instructional path. In addition, as the complexity of the topic warranted, an accelerated or a remedial instructional path

was included. Courseware was written at a level designed to reduce reading

difficulties. Military terms were included, and if it appeared that these

terms might introduce comprehension problems, synonyms or examples were added.

To the extent possible, on-line representations (on the computer) of situations

or examples were used (basically, if you can put it on a typewriter, you can

put it on the scope). When this was impossible or infeasible, pictures and

diagrams were prepared for student use.

Answer-matching in frames requiring constructed responses (PLANIT accepts

certain misspellings), and the alternative selected in multiple-choice questions constituted the student response data-base. Correct as well as incorrect

responses provided a basis for selective remedial material and for making decisions for subsequent actions. When an 1IB40 answered incorrectly, he was

told not only that he was wrong, but why he was wrong. The student received

feedback for each response entered. The feedback took a positive, negative, or neutral form. Additional prompts and cues were also included. Another PLANIT

feature provides a course reentry capability to restart students where they

stopped for breaks.

Materials for preassessment and postassessment of student performance were

prepared to balance the assignment of students among three treatment groups and

to assess extent of learning (gain score) among students. From an Infantry

School-approved list of criterion and enabling test items, two test versions

for each courseware module were prepared (LAW, M90, M60, tactics, patrolling).

Each version contained a camparable number of items, about 25 to 30. Versions

were created by scrambling the order of multiple-choice alternatives, while

leaving content the same; selectinq different steps for items consisting of a

2
- ,..J- - . - "......--- - - - - - - - -- *

series of steps; and adjusting the relative position in the test of constructedresponse items which did not lend themselves to alteration.

The PLANIT structured AI courseware content was reviewed by the Infantry

School in July 1973. The subject matter experts comments ranged from "Can go

into practical application with little additional training (LAW)," to "This

program looked good, the exercises on sighting and engaging targets were quite

good and probably of the most value (90mm Rifle)." After a field tryout with

10 11B40 NCOs, course materials were finalized in August 1973.

Subject Selection

The test plan (experimental design) called for 11B40 NCOs who had relatively low scores on their MOS proficiency test; therefore, they needed the

training. To choose them, a run was made on the personnel tapes at Fort Hood,

Tex., and the 1IB40 files were extracted from it. Card decks containing names

and Social Security numbers of 1IB40 personnel were sent to the Enlisted Evaluation Center, Fort Benjamin Harrison, Ind., and the 1972 MOS Proficiency Test

scores were obtained. A month before MASSTER Test 122, the card decks were

again run against the personnel tapes to determine whether or not the llB40s

were still at Fort Hood. On the basis of this information, listings of the

subject pool of llB40s were prepared and delivered to Headquarters MASSTER.

For each 11B40 on the list the background data included GT (general technical)

score, MOS proficiency test scores, education, age, and paygrade on a student

record form.

Conduct of the Field Test

MASSTER Test FM 122 began in September 1973. An outline of the test design in terms of the treatment of participants is shown in Table 1. Briefly,

120 male participants were tested--60 in the Al Group, 30 in the Study Group,

and 30 in the Control Group. One-half of the participants in each of the Al

and Study Groups participated in one of the two subject-matter areas: Crew

Served Weapons or Tactics.

Table 1

Test Design--Treatment of Participants

AI group Study group Control group

(N = 60) (N = 30) (N = 30)

Pretest Pretest Pretest

Automated instruction Self-study Activity unrelated

to subject matter

Posttest Posttest Posttest

Structured interview

3
The procedure called for a maximum of 12 11B40 participants each day to

fill out an introductory form on arrival and to be briefed on the purpose of

MASSTER Test FM 122. While the briefing was conducted, student records were

pulled and assignments made to either Crew Served Weapons or Tactics, based

upon MOS proficiency test score. For a given participant, his past scores were

compared, and he was assigned to that treatment group which corresponded to his

lowest previous proficiency test score. Participants then took their pretest

(half on Form A and half on Form B) and were assigned at random to the AI,

Study or Control Groups. The AI Group took their course (e.g., Tactics) on the

computer; the Study Group studied the same material from Army publications, and

the Control Group had an unrelated assignment (learning the ALPHA-DOT Systen of

communication) (Sidorsky, 1974a, 1974b). Each group averaged approximately

4 hours on their activity. The posttest (the alternate form of the pretest)

followed, and then the AI Group was interviewed.

RESULTS

In discussing results, there is always a question about favoritism of one

or more groups (bias) in regard to background variables or pretest scores and

*1 pretest time. Table 2 shows the means and standard deviations for the Al,

Study, and Control Groups for the LAW (light antitank weapon). The data show

that the three groups are about the same, which indicates that the effects of

these variables were virtually canceled out by the random assignment of test

subjects to the AI, Study, and Control Groups. The one variable that shows the

greatest difference is GT score, in which the Control Group has a mean of 108

and the AI and Study Groups have means near 100. This difference is not regarded as having a serious effect, since the correlation of GT score and pretest score was low (.26), and the difference was in favor of the Control Group.

Table 2 also shows a fairly wide range of 11B40 personnel. In the AI Group,

for example, ages were from 21 to 45, education from 8th to 13th grade, GT

scores from 88 to 119, and paygrade from E5 to E7.

Although the mean pretest times for the three groups were within one-half

a minute of each other, the mean posttest time differed considerably (Table 2).

The Study Group took 8 minutes or 53% longer to complete the posttest than did

the Al Group. [This ability of the Al Group to answer the test questions more

quickly than the Study Group was highly significant at the .01 level of confidence, as evidenced by a t-test (t = 4.70) with 44 degrees of freedom) computed

from an F ration.] Feedback obtained during the interview indicates that the

AI Group was more confident of their knowledge and skills than the Study Group

and could answer the test questions more quickly and more accurately.

Between-Groups Comparison

The two critical comparisons for the study are between (a) the AI and Control Groups and (b) the AI and Study Groups. The AI Group had a mean gain

score of 8.394, an 82% increase over their pretest score. The Control Group

had a mean gain score of 2.154, a 21% increase. The difference between the two

mean gain scores is 6.240, which, using the t-test of statistical significance,

produces a t ratio of 6.23--highly significant at the .01 level (.01 - 2.69).

The t ratio shows that the possibility of a mean difference 6.240 occurring by

4

MILL.-.- - - ---- --
Table 2

Crew Served Weapons: Group Means and Standard Deviations

AI group Study group Control group

Variable name (N 33) (N = 13) (N = 13)

M SD M SD M SD

GT score 100.2 8.9 100.7 11.0 108.3 12.8

Education 12.3 1.6 12.1 1.8 12.1 2.1

Age 28.4 5.6 30.9 6.0 29.6 5.3

Paygrade 5.7 0.6 5.9 0.5 5.8 0.6

MOS Test 2 13.5 2.8 14.2 2.6 12.7 2.9

MOS Test total 65.9 8.6 66.1 8.4 64.2 10.5

.- Pretest score 10.3 2.7 10.1 2.5 10.4 3.1

Pretest time 25.6 6.3 26.2 4.9 25.6 7.6

Posttest score 18.7 3.5 15.6 3.1 12.6 3.1

Posttest times 15.0 5.2 22.9 4.9 20.5 8.3

Gain score 8.4 3.1 5.5 3.8 2.2 3.0

chance is remote. Consequently, this difference can be attributed to the automated instruction given the Al Group. The significant t ratio and the 82% increase in proficiency are positive statistical and practical evidence that

learning takes place when tactical data systems are employed in a secondary

(automated instruction) role.

The same comparisons were made between the Al and Study Groups. The difference was statistically significant at the .05 level, producing a t ratio of

2.66 in favor of the AI Group. The significant t ratio and a 52% inczease in

proficiency over the Study Group (82% - 54% = 52%) are positive statistical and 54%

practical evidence that--within the limits of this study--learning by means of

automated instruction is more effective than study group methods of training.

For purposes of completeness, note that the differences between the Study Group

and Control Group produced a t ratio of 2.54, which is significant at the .05

level. Thus the Study Group also had a significant gain in learning when compared with the Control Group, although not as great as that of the AI Group.

Figure 1 shows the relationships of these between group findings.

The Study Group situation was better in this experiment than the 1B40

NCOs normally encounter. They were given a pretest, and although they did not

know their pretest scores, they had some idea of how well they did on the test.

They had field manuals to work with, plus an instruction sheet telling them

what topics to cover and in what order, and on what pages and paragraphs the

topics were located. Finally, they had supplemental material available in this

structured-study situation.

5
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Figure 1. Relationship of between-groups findings for the LAW.

6
The results for Tactics were quite similar to those for LAW, as evidenced

by the data presented in Figure 2. There is some difference in the slope of

the control group curve from that obtained for the LAW group. But this pretest

and posttest difference shown for the LAW control group was not statistically

significant and appears to reflect the variability between the alternative forms

of the test. Also, the "idealized" equal-cell entries sought in the original

test plan design (Table 1) were not fully obtained in reality. These differences in "idealized" and "actual" may be obtained by comparing the Ns of Figures 1 and 2 with those in Table 1. Such deviations from plan (or how the field

test differs from the laboratory) are to be expected in this type of setting

(Johnson & Baker, 1974; "Field Testing," 1974).

Relationship of Data to GT (General Technical) Score

Data were analyzed on 77 variables for the 33 NCOs in the Crew Served Weapons AI Group, and for 34 NCOs in the Tactics AI Group. The statistics included

the frequency distributions, means, standard deviations, and range of scores for

each variable, and the intercorrelation matrix for all 77. The relationships of

three specific variables to GT score are shown in Figure 3.

of particular interest to the training community is GT score, which is considered a measure of general aptitude or ability to learn. Combat arms personnel, many of whom are in the lower ranges of GT score, present speciar problems

in training for the military services. As shown here, GT scores for the 33

llB40s in the Crew Served Weapons Al Group ranged from 88 to 119, with 55% of

them below 100. These results show that the automated instruction method of

training applies equally well to 11B40 personnel with high or low GT scores.

The 10 Al subjects with the lowest GT scores had an average posttest score of

18.7 and the 10 highest, an average of 19.5, less than a point difference.

Similar results were obtained for the Tactics Al Group.

Even when the entire N of 33 is considered, a distinct observational trend

shows that students at lower GT levels improved more than those at higher GT

levels. The Pearson r between GT and pretest score was .38, while the Pearson

r for GT and posttest score was .17. This shows a trend for all GT levels to

learn and do better on the posttest, with those in the lower GT band showing

the most improvement. It appears that the posttest had a ceiling sufficient to

accommodate both the higher and lower GT levels; however, an exhaustive psycho- metric analysis of posttest ceilings was not made. Because of the practical

significance of this conclusion, further corroboration of this phenomenon in AI

field experiments is warranted.

Taken overall, these results indicate that automated instruction is an effective method of providing weapons and tactics training across the range of

11B40 GT scores. Automated instruction apparently has the effect of reducing

or overcoming the verbal handicaps usually associated with lower GT scores.

Other Variables Examined

Three other variables--Paygrade, Education, and Age--were examined and

showed a low negative correlation with gain score. Thus, automated instruction

7
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Figure 2. Relationship of between-groups findings f or tactics.

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(n - 33) (correlation between gain and GT is -.14)

9
appears to be effective across age groups, across education level, and across

the paygrade of the 11B40 population.

The total course time for the 33 subjects in the LAW course averaged 224

minutes and ranged from 158 minutes to 300 minutes. There were four lessons

in the LAW course. Of the 33 participants, 24 completed the entire LAW course,

1 was in lesson 4 at the time the test period ended, and 8 had completed or

were in lesson 3.

A ratio score was obtained to determine whether slow learners, in taking

the AI course, used a different strategy than the faster learners did. Total

entries that each slow learner made were divided by total FL Frames (the minimum path to the point the slow learner reached in the course). This ratio is

the number of entries made for each FL frame. The results showed that the

slowest 16 made only one additional entry more than the fastest 16 per every

nine FL frames reached. This relatively small difference would indicate that

the slow learners went through the AI course in the same way as the fast learners and simply required more time to read and comprehend the material.

qualitative Results

After the posttest, AI Group participants were interviewed in depth about

their experience with automated instruction. The interviewer filled out a form

as the participant responded. Some questions were open-ended, and others required a specific answer. The interviews were also taped, with participants'

permission. The response to and acceptance of automated instruction by llB40s

was striking: combat infantry NCOs, who usually start fidgeting after an hour

in the classroom, sat down at a scope for 4 or 5 hours and became completely

absorbed in the learning process. As one qualified NCO drill instructor aptly

put it: "It covered the whole weapon. When you come out of there, you knew a

hell of a lot more than you did when you went in." Another NCO expressed it

this way: "Makes you confident because when you walk out, you know the

subject."

Responses to the interview questions were virtually unanimous. In terms

of the expected, and widely touted, positive features of CAI, these participants also remarked on the quiet atmosphere, the self-pacing features, immediate feedback, individual attention, and nonthreatening interactions between the

student and the computer. The last point was a key feature, since these NCOs

repeatedly remarked that they were reluctant to ask questions or make mistakes

in a classroom for fear of "looking stupid" in the eyes of those they command.

Regarding the course materials and technique of presentation, the participants

commented that the items were easy to understand, were obviously built on each

preceding item, were accurate, and "got at the facts without the B.S." The

students found the situation to be challenging (but nonthreatening) and rewarding, "because you always know immediately where you stand." They believed

that new methods of training such as AI would make Army instruction better and

more interesting.

10
SUMMARY AND CONCLUSIONS

With the efforts in the Army to develop and field tactical data systems,

there will be a considerable data processing capability at the tactical unit

level. A potential secondary role for these systems, when they are not re- p

quired for tactical operations, is that of supporting unit and individual

training. The results from the U.S. Army's MASSTER Test FM 122, IBCS: Automated Instruction, have demonstrated the feasibility of using these systems in

a stand-alone mode in support of unit training requirements--in this case MOS

and GED training of infantry personnel.

This project has validated one of the objectives of the National Science

Foundation's effort in the development of PLANIT--maximum portability. A

DEVTOS tactical data system using PLANIT is installed on a next-generation,

militarized, more rugged tactical data system. Several diverse courseware

packages have been developed and successfully executed on a tactical computer,

using this CAI language.

We may conclude that automated instruction in a field setting is effective,

is enthusiastically accepted by NCOs, and is more efficient than the traditional

study method of training. Efficiency refers here to time spent in taking the

posttest and observed concentration and focus on the learning task (attentiveness) when comparing AI and Study Groups. Also, using a computer for training

during scheduled periods of inactivity may indeed be more efficient (productive)

than letting it sit idle, in terms of amortizing the dollars already spent on

the computer, software, peripherals, and assigned Army manpower. AI training

has the advantage of reducing or overcoming the verbal handicaps usually associated with lower GT scores. For these participants, automated instruction held

their attention, required them to think about what they were doing, and provided

them time in which to ponder and learn. Further, AI provided a positive learning experience in a nonthreatening environment.

Further research seems warranted. The LAW students believed they could

have left the console and gone out and fired the weapon with accuracy. Thus

there is considerable interest in restructuring the criteria against which tests

are designed and constructed, that is developing criterion-referenced tests

(Carver, 1974). Criterion-referenced tests are designed "to yield measurements

that are directly interpretable in terms of specified performance standards"

(Glaser & Nitko, 1971). Traditionally developed tests (such as achievement

scored, norm-referenced proficiency tests) are usually the methods used for assessing such things as the efficacy of using Al as the instructional media.

Such was the case in the present study. But the real experimental question remains: Does CAI teach effectively enough in the abstract to affect performance

measured in the real world? The U.S. Army has a major stake in the answer.

I . . . . . .. . .. . . . . .. . .I I I " " "
REFERENCES

Atkinson, R., & Wilson, H. A. (Eds.). Computer-Assisted Instruction: A book

of readings. New York: Academic Press, 1969.

Baker, J. D. Human factors experimentation within a tactical operations systern (TOS) environment. ARI Research Study 68-4, October 1968.

Baker, J. D. Acorns in flower pots/psychologists in the field. JSAS Catalog

of Selected Documents in Psychology, 1972, 2, 88. (MS No. 191).

Baker, J. D. Human performance research in military information systems: Quo

fuimus et quo vadimus? Proceedings: NATO Defense Research Group Seminar

on Man-Machine Relations, May 1973.

Carver, R. P. Two dimensions of tests: Psychometric and edumetric. American

Psychologist, 7, July 1974.

Feingold, S. L. PLANIT--A language for CAl. Datamation, September 1968.

Field testing: Special issue. Human Factors, 3, June 1974.

Frederick, F. J. PLANIT: A "machine-independent" CAI language and system.

Viewpoints, 50(4), Indiana University, July 1974.

Frye, C. H. CAI languages: Their capabilities and applications. Datamation,

September 1968.

Glaser, R., & Nitko, A. J. Measurement in learning and instruction. In

R. L. Thorndike (Ed.) Educational measurement. Washington, D.C.: American Council on Education, 1971.

Hoyt, W. G., Butler, A. K., Bennik, F. D., & Baker, J. D. Application of

tactical data systems for training: DEVTOS feasibility determination and

selection of an instructional operating system. ARI Technical Paper 267,

October 1975. (AD A017 436)

Hoyt, W. G., Butler, A. K., Bennik, F. D., & Baker, J. D. Application of

tactical data systems for training: Summary of early development. ARI

Technical Report 450, June 1980.

Johnson, E. M., & Baker, J. D. Field testing: The delicate compromise.

Human Factors, 3, June 1974.

Sidorsky, R. C. Alpha-Dot: A new approach to direct computer entry of battlefield data. ARI Technical Paper 249, January 1974a.

Sidorsky, R. C. Source data automation via the alpha-dot tablet: A feasibility study. Presented at 82d Annual Convention, American Psychological

Association, September 1974.

Uhlaner, J. E. Human performance effectiveness and the systems measurement

bed. Journal of Applied Psychology, 1972, 56(3), 202-210.

13

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