CALIFORNIA STATE UNIVERSITY, NORTHRIDGE
FACTORS RELATED TO
CHILDREN'S INTEREST IN COMPUTER LEARNING
A thesis submitted in partial statisfaction of the
requirements for the degree of Master of Arts in
Educa tiona! Psychology
by
Eleanor Bernice Coskey
May 1985
The Thesis of Eleanor Coskey is approved:
Daniel W. Kee, Ph.D.
California State University, Northridge
ii
0
Acknowledgment
It is impossible to think about certain people without being
thankful.
My chairperson, Dr. Adele Gottfried, continually offered me
support and encouragement. It was a privilege to be able to work with
her. I have enjoyed her both as a professional and as a personal friend.
Dr. Bernie Nisenholz, my advisor, has been a positive influence in
my education when he introduced me to Gestalt therapy and family
counseling.
It is an honor to have Dr. Dan Kee as an advisor on my
committee and I have appreciated the material that he has made
a,vailable to me on computer learning. I admired his research on young
children's attitudes toward hand-held electronic toys.
My live-in sweetheart and husband of 25 years, Richard, inspired
me to pursue my education. Thanks for your understanding and support.
My three children have given me a beautiful dimension to my
life. I have learned the most from them. Many thanks for your patience
with your student-mother over the last eight years.
Kevin, thanks for your help with statistics. Jay, I appreciate
your critical review of my work. Jill, how would I have managed
without your guidance in self-expression?
To all of you, I sincerely thank you for assisting me in my
growth.
·ill
'
IN MEMORY OF
W. AND W.
iv
TABLE OF CONTENTS
Title
Acknowledgment ..............................•....•....•.•........................... iii
Dedication ....................................•........•...................................... iv
List of Tables •••••••••••••••••••••••••••••••••••••••••• ••• ••• ••• • •• •• •• •• •• ••••••••• ••• •• vi
Abstract ...................................•................................................ vii
I. Introduction
1
II. Literature Review
2
III. Method ••••••••••••••• •••••• ••• •••••••••••••• •••• •••••• ••••••• ••• ••• •••••• •• •••• ••••••••• ••••• 18
IV • ReSUltS •••••••••••••••••••••••••••eeee~oooooeeeeee•••••••••••••••••••••••••••••••••••••••••••• 23
y.
Discussion ••••• •• •••••• ••••••• •• •••• •• •• •••••• ••••••• •• •••• ••••••••••••••••••••••••••••••• 2 9
VI. Notes
33
VII. Bibliography •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 3 4
v
LIST 0 F TABLES
Title
Table 1
CMI and CAIMI Correlations ••••••••••••••••••••••••••••••••••••••••• 26
Table 2
CMI and Attribution Correlations
Table 3
Means and Standard Deviations .................................... 28
Vi
27
ABSTRACT
FACTORS RELATED TO
CHILDREN'S INTEREST IN COMPUTER LEARNING
by
Eleanor Bernice Coskey
Master of Arts in Educational Psychology
This exploratory study of computer use for instruction is concerned with investigating children's computer motivation and how it
relates to their academic intrinsic motivation, intelligence (vocab1;1lary
and block design), Logo learning, and gender.
In this research, data were collected from children ranging from
8 to 13 years old, who volunteered to take a three week summer class
in Logo programming. Most of the subjects had minimal or no prior
contact with computers before this study.
A pre- and post-test on
Logo learning, along with a WISC-R vocabulary and block design test
was individually given to each participant. A reliable and valid computer motivation inventory and an academic intrinsic motivation inventory were administered. During the Logo course, the subjects gained
knowledge of commands involving graphics, logical and conditional
statements, and editing and saving files. Correlations between the
variables proved significant. Children with higher computer motivation
tended to have higher spatial ability scores, greater knowledge of Logo
commands, and higher academic intrinsic academic motivation. Educators, psychologists, and concerned parents might find this research
useful.
viii
Chapter I
Introduction
We are on the verge of a powerful technological revolution that
will enrich all aspects of our lives, including our educational structures
(Papert, 1980; Lepper, 1982). Computers will prove to be as significant
a development as the Industrial Revolution (Lepper, 1982; Howe, 1983).
How we utilize this "dynamic tool" (D. Watt, 1984) will determine our
~estiny
(Lepper, 1983). O.K. Tikhomirov said, "Tools are not just added
to human activity, they transform it" (Greenfield, 1984, page 153).
In five years, 30% of the jobs in America will be computer
related (Bell et al., 1984; Greenfield, 1984).
Our educa tiona! system
has an urgent responsibility to prepare today's generation for this new
era (Licklider, 1983) by emphasizing information-processing skills, such
as how to learn and how to gather information, in order to maintain
the pace with rapidly changing technology (D. Watt, 1984; Megarry,
1983). Problem-solving decomposition~ will be encouraged among learners in a computer environment (Howe, 1983).
Traditional methods of
learning are obsolete and cause disappointment with the quality of
education (Papert, 1980; Suppes, 1968).
1
{l
Chapter II
Literature Review
Computer Motivation
How can we optimize computer motivation to encourage active
and sustained exploration and experimentation? (Lepper, 1982). Thomas
Malone (1981) developed his own theory of intrinsic motivation by
studying the appeal of arcade games.
Video games are the child's first
encounter with computers (Greenfield, 1984).
Important ingredients
that developed motivation were:
Multi-level Challenges.
comfortable level.
player stops.
Learner must operate at a
When the final stage is mastered, the
(Lepper, 198 2; Mandinach, 1984; Greenfield,
1984).
Performance feedback.
Player experiences interaction and
immediately understands if the goal is achieved. (Howe,
1983).
Control.
Learner gains self-esteem with autonomy (Howe,
1983; Mandinach, 1984).
Other factors Malone found enhance learning and lead to intrinsic
motivation are:
Goals, chance (randomness), hidden information, curi-
osity, fantasy, visual motions, sound and novelty (Gottfried, 1981;
Lepper, 1983; Mandinach, 1984).
By understanding computer motivation, educators can distinguish
the variables that positively influence academic learning and improve
2
'
3
patterns of intellectual development (Papert, 1980). Education needs to
solve how to activate the potential of relevant motivation offered
through computer games (Bell et al., 1984). "Computer games are not
as vulnerable to having external rewards undermine intrinsic motivation"
(Kee & Worden, in press).
More learning will take place electronically, which will permit
students to explore image to image, without print or oral words (White,
1983; Greenfield, 1984).
In the electronic age, a child can manipulate
symbolic objects on the computer screen, which is a part of his environment.
The child can "move" streets and homes, and "see" inside
buildings (White, 1983).
thinking 2 (Papert, 1980).
Computers can "concretize" formal operational
Word processors develop formal thinking when
students practice revisions by rewriting essays.
After one year of Logo
instruction, 9 to 11 yeat· olds did better on a word puzzle and "permutation" tasks that r.equired formal operational thinking (Greenfield,
1984).
Piaget believed a child learns naturally, without being taught. If
a child receives support, he will create a meaningful learning structure
of his own (D. Watt, 198 2). Our education is still influenced by Piaget's
theory (Lawler, 1982). Piaget's insights were based on the printed world
of information. Knowledge acquired through computer access may
change Piaget's theory (White, 1983). Reduction of time to grasp basic
skills in early childhood will radically change school curriculum (Lally &
Macleod, 1983; Bell et al., 1984; J. Brown, 1983) and advance cognitive
skills (Mandinach, 1984).
We will have "learning environments" rather than "teaching
environments" which will offer more control to the learner (Gibbon,
4
1983).
Students become their own teachers.
"Only the learner has the
power to internalize new ideas to his personal thinking" (D. Watt,
198 2).
Autonomous learning is developed after the mastery of cyclical
exploratory skills (think, plan, test and debug).
Students who develop
autonomy through computer learning find out who they are and what
they have to contribute (Turkle, 1984), and discover "individual de ficiencies," (Mandinach, 1984) inadequate concepts and "contradictions in
cognitive judgement" (Lally &: Macleod, 1983).
The highest level of
learning is achieved by self-regulated learning3 and the transformation
4
process (Mandinach, 1984).
Interaction between student and machine is highly structured. The
machine expects the student to respond in a specific way which
sometimes curtails creativity (Lally & Macleod, 1983; Boyd, 1983). Boyd
(1983) described the people introducing computers into our society as
~'bureaucratic
managers" who plan to "robotize" people.
"The potential
to enrich human learning will be missed due to the wrong planning
approach" (Boyd, 1983).
McMahon (1983) emphasized that the student
has control of his own construction of knowledge.
The original program
designer, who built in the rules, controls the style of feedback.
This
style of feedback determines how the students learning will be reinforced.
Well constructed programs are flexible and tailored to indi-
vidual differences (Brady&: Hill, 1984; Turkle, 1984; McMahon, '1983).
Program designers and educators need to create an active interchange
of ideas for the development of effective
man, 1969).
comput~r
programs (Silber-
Ike Skelton, a U.S. Representative established a bill to
coordinate the educational system with the computer industry to
produce software (Time, May 17, 1984, page 6).
5
Malone (1983) is optimistic that software will become more
"trendy" in the near future.
1984; Gibbon, 1983).
Educational software is scarce
(C~nnell,
What is available is lamentable. It is com plica ted
(Walker, 1980), poorly documented, gimmicky and unimaginative
(Gibbon, 1983). The most valuable software is when fantasy context and
educational content are endogenous.
"Quality educational software is
the key to success for our young people" (Lepper, 198 2).
Packaged programs facilitate computer instruction, but impede
students' learning to program. Production of program packages takes
from 50 to 500 man-hours per hour of student use. Skills required to
produce this expensive program are good design, layout, dialogue,
graphics and built in prevention against sabotage (Megarry, 1983).
Drill and practice programs are easy to write but are dull,
repetitious and overused (Brady & Hill, 1984; Gibbon, 1983).
Tarrant
<.198 2} indica ted a high level of motivation in a study, using a drill-
and-practice program.
Viable results improved learning of both the
exceptional and slow learner (Tarrant, 198 2}.
Drill and practice pro-
grams are valued less than realistic simulations due to restrictions.
If
drill and practice programs were more "game-like" they would have
more intrinsic interest.
Simulations offer problem solving techniques which are a valued
skill (Schneiderman, 1984).
otherwise not possible.
Hypotheses can be tested safely in ways
The concrete, real world model serves as a
fundamental example to build more abstract knowledge on later (Greenfield, 1984).
When a child learns through his microworld, teachers can
probe by creating a problem to be solved using newly found discoveries
and pointing out procedural misconceptions (Sweeney, 1984).
Learning
6
is facilitated when children make active decisions (Lally & Macleod,
1983).
Programming is a demanding cognitive task that develops precision, strengthens ability to identify sequences, and creates and tests
hypotheses (Brown & Rood, 1984).
through computer simulations.
Metacognitive skills are expanded
(Brady & Hill, 1983; Papert, 1980; J.
Brown, 1983). "Learning by doing" develops metacognitive skills. Personal discovery is remembered for life (J. Brown, 1983; D. Watt, 1982;
Lawler, 198 2). Even when the student is not on the computer, he can
internalize an idea to his personal thinking by relating the idea to his
previous knowledge (D. Watt, 1982).
An intrinsic reward for computer learning is the control of the
environment (Geoffrion, 1983), and the accomplishment of a goal
(Hakensson, 1983; Malone, 1982).
Computers are responsive, powerful
a.nd hold children's attention (White, 1983; Hakensson, 1983).
Critics
suggest "sugar coated" computer motivation will undermine intrinsic
motivation outside of the computer context and information will not be
retained for later application (Lepper, 1982).
A child's exploratory interaction with computers must be studied
by educators (Schwartz et al., 1984). Computer instruments allow us to
study the learning process (White, 1983). A computerized test can
measure memory over various time spans (Sweeney, 1984).
Micro-
computers can be used for multiple "data collection activities"
(Kee &
Worden,, in press).
The "real teachers" are the computers.
"Teachers motivate the
students to use technology; technology motivates the student the rest
of the way" (Roberts, 1983).
"A teacher must know when to intervene
7
and what to say" (J. Brown, 1983).
Papert (1980) called this "de-
liberate" teaching.
Teachers' roles are changing from authoritarians to managers,
facilitators, technologists and coaches (De Vault,
1981; Solomon, 1982;
Bell et al., 1984).
The power of the educational institution will be lost if educators
do not utilize the potential of the computer (Connell, 1983, November).
Hoyle (1983) is not optimistic about computer innovation in schools.
Schools are ultra-conservative institutions.
ment conventional teaching.
Computers will only supple-
Educators need to break from fixed-
conservative formats in order for the computer to be a catalyst for
our educational system (Hoyle, 1983).
New teachers will be computer literate, but established teachers
are reluctant to change their role model.
Computer literacy classes
for teachers must be job-embedded in in-service training to insure
active participation (Hoyle, 1983). Teachers are most effective when
they control the curriculum and are involved in planning and implementing computer instruction (De Vault, 1981).
In the last five years, L.A. County School System has spent 8
million dollars on computer technology and spending will increase in the
future (Smith, 1983, December).
Higher education has been the focus
of computer research, but recently educators realize young students
need computer preparation (Gorman, 1982; Schwartz et al., 1984).
The
average high school in the U.S. owns 10 computers, while the average
grammar school only owns 3.6 computers (Elmer-De Witt, 1984).
What is computer literacy?
have not resolved.
This is a moot question educators
Does literacy apply to computer mechani·cs or
8
computer programming?
literate?
How much does a child need to know to be
These questions represent a few decisions that need to be
determined before a curriculum can be adopted (Brady &: Hill, 1984;
Bell et al., 1984; Schneiderman, 1984).
Stuart Gothold, L.A. Superintendent of Schools suggested that
schools are not making the best instructional use of the computer
(Connell, 1983).
Rudimentary knowledge of computer programming is
essential for all students (Flammer, 1976).
Schneiderman (1984) found few schools are rethinking curriculum
changes.
An improved curriculum would teach children
11
what to do
with computers,11 and exclude "information about them. 11
A minimum program would emphasize:
1.
Text editing for writing
2.
Database programs to collect and analyze data
3.
Laboratory experiments for control and exploration
4. Sound and graphic capabilities for self expression in music
and art (Schneiderman, 1984). Brady and Hill (1984) pointed out that
microcomputers can aid education as a word processor, or for individualized drill and practice or for programming.
What are the prerequisites for a student to learn to program?
Brady's and Hill's research (1984) indicates children in the stage of concrete operations with an understanding of number conservation can
grasp computer programming more effectively, although three and four
year olds without number conservation can and do program (Hiebert,
1981). Birch's study (1983) demonstrated five year olds had no fear or
feelings of inadequacy in using Logo language to program.
Kinder-
gartners accepted the computer as a normal classroom teaching tool.
I
'
9
The advantage of early exposure to programming is that "work grows
out of play," but three and four year olds may not have "visualtactual" skills developed to facilitate the keyboard and screen (Brady &
Hill, 1984; Greenfield, 1984).
How often and how long should computer instruction be taught to
pre-schoolers?
Answers varied from 15 to 20 minutes daily (Lally &
Macleod, 1983; Birch, 1983) to four times a week for 20 to 25 minutes
(Rorenberg, 1983) to every other day for 15 minutes (De Koven, 1984).
Young children's exposure to microelectronic, hand-held learning aids
contributed to computer literacy and provided intrinsically motivating
lessons (Kee and Worden, in press; M. Watt, 1984; Turkle, 1984).
The appeal of microelectronic learning aids over the traditional
books was novelty.
Children without prior exposure to computers are
rapidly disappearing. Other attractions to microelectronic games such as
·"Robot" and "Speak and Spell" were surprise, randomness, and audiocuriosity (Turkle, 1984; M. Watt, 1984). Microelectronic games help
young children to learn to "talk" to a computer (Papert, 1980 ).
Young
children reacted to microelectronic hand-held computers as humanized
toys.
They were frustrated when they lost control but showed affec-
tion with a happy response (Turkle, 1984; M. Watt, 1984).
Although students' attitudes about computers are positive, attitudes alone do not measure effectiveness of computer instruction.
Students viewed computers as more fair, infallible, enduring, helpful and
concise (De Vault, 1981).
Gottfried (1983) suggested that the teacher-
pupil relationship may be influenced by the students' degree of academic intrinsic motivation and the teacher's observation of that motivation. Computers are impersonal, non-judgmental teaching tools which is
~
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10
an advantage in making students comfortable if they do not know the
answers or need material repeated (Suppes, 1968).
Fears that microcomputers would isolate children has not occurred.
(Brady & Hill, 1984; Perry, 1984).
Bank Street School re-
searchers discovered 8 to 11 year olds who were learning Logo on
computers were more spontaneous, cooperative and social, than when
they were learning in a traditional classroom (Silberman, 1969;
Geoffrion, 1983; Greenfield, 1984).
learning (Reid et al., 1972).
television.
Mutual assistance strengthens
Home computers are viewed as replacing
Computers offer more interaction between parents and
children (Bell et al., 1984).
Personal computers, with a price tag of
under $1,000 will be more popular in the future than the accessible
time-sharing computer (Watzman & James, 1984; Golden, 1982, May).
Computer Anxiety
How does anxiety effect computer learning?
Anxiety is associated with resistance and withdrawal (Rohner et
al., 1981; Gottfried, 198 2).
No significant relationship was found
between computer anxiety and gender (Rohner et al., 1981).
Zuk and Anglin (1984) claim anxious students forced to use
computers will learn less than they would with traditional teaching.
Fear of failure, or anxiety, was based on inexperience and lack of
knowledge.
Computer assisted instruction (CAl) improved the students
attitude.
CAl is not the best remedial instruction for every student.
Cognitive and emotional factors are variables teachers need to consider
(Flammer, 1976).
11
Gottfried (1982) pointed out that successful students have a
competent attitude and high intrinsic motivation, whereas students who
experience failure have high anxiety, low motivation and a fear of an
"evaluative situation."
A student can have high anxiety and low
academic motivation in one subject and not in another (Gottfried,
1982).
Spielberger and group (1970) indicated preliminary evidence that
anxiety may influence a students "real life" in the same way as it does
in the CAl laboratory. Blankenship (1983) concluded from a simulated
computer test that anxious people spend less time on their tests by
letting their mind wander.
The result is fewer skills are learned, less
experience and ability is acquired and a low self-esteem is developed.
The effects are cumulative.
This population will avoid math and
computer classes where learning is based on previous knowledge.
Present level of motivation is based on prior learning experience
(Mandinach, 1984).
Intervention can reduce anxiety and build motivation.
motivation fosters learning.
Intrinsic
It is imperative for educators to incor-
porate intrinsic motivation in their instruction (Gottfried, 1984).
Sex Differences
Boys outnumbered girls in computer literacy in 1983 (Miura &
Hess, 1983; Lepper, 1982).
A home-computer ownership survey in
grades five to eight reported 13% of 87 middle to upper class students
owned microcomputers.
Computer owners were all boys, even though
the population was evenly divided between the sexes.
Fathers used the
12
microcomputers for business and games, but no mothers in the survey
used the computer (Miura & Hess, 1983).
Mandinach's research (1984) also found more males (79%} than
females (58%) used a computer.
Twice as many males had computers
at home and knew how to program.
Males did better than females on
a school performance test and were more optimistic, but both sexes
desired to use a computer more often in school work (Mandinach,
1984).
Positive response rate for males of 75% exceeded the 57%
positive response rate for females (Chisholm & Krishnakumar, 1981).
Malone's study (1981) noted no difference among the sexes in achievement or motivation.
Research has demonstrated males have greater spatial skill. How
can we differentiate which came first, the spatial skill, or the interest
in spatial activities?
More research is needed in the field of sex
differences, instead of loose references (Peterson, 1983; Chrisholm &
Krishnakumar, 1981).
Girls'
and boys' interest in computers is equal until third or
fourth grade, when the computer becomes like. an "arcade game" to
girls (Bell et al., 1984).
Boys outnumber girls in crowds around arcade
games (Greenfield, 1984).
More preadolescent boys than girls are
attracted to computers (Golden, 1982, May).
Computer content and
context are more appealing to boys (Lepper, 1982).
Boys like competi-
tive games, while girls select creative, cooperative games (Muira &
Hess, 1983).
Malone's research (1981) showed girls were attracted to
musical context and boys preferred explosive balloon-popping noises
(Kelman, 1983).
13
Parents influence gender accessibility by promoting summer
computer camp for sons more than daughters (Mourat, 1983).
Summer
camps range upwards to $1,500 for a four week stay (Miura & Hess,
1983; Lepper, 1982).
Girls accounted for only one-fourth of the total
campers at Lake Forest College in 1983 (Mourat, 1983).
Although computer assisted instruction (CAl) benefits both sexes,
boys showed a greater improvement over girls after CAl.
The group
that was helped the most was disadvantaged boys (Green & Ross, 1968;
Green, Henderson & Richards, 1968; Fletcher & Atkinson et al., 1971 ).
Intervention strategies must be developed to narrow gender gap
in computer literacy (Miura & Hess, 1983).
In 1995, there will be 48
million computers in the U.S.; half of the users should be women
(Mandinach, 1984).
Seymour Papert, the father of Logo (Schwartz et al., 1984),
worked with Jean Piaget for five years and used the Piagetian learning
framework to create Logo, a computer language that enables a learner
to initiate and construct his own knowledge that is appropriate to his
experience, thoughts and feelings (D. Watt, 1982; Perry, 1984; Carter,
1983; Papert, 1980).
Papert spent over a decade developing Logo
(Papert, 1980).
Logo is a "tool" that places learning power in the hands of the
new programmer (Lawler, 1982; D. Watt, 1984).
Users teach computers
to carry out their commands (Carter, 1983).
"Computers are dumb.
You have to teach them everything," a student from Bank Street
School claimed (Greenfield, 1983).
14
This discovery-based, (Lepper, 198 2) list-processing (Laugh &:
Tipps,
1~83)
language is designed to introduce computer programming to
young people, through Logo
11
turtle graphics" (Papert, 1980; Lepper,
1982; Olds, 1984; Schwartz et al., 1984).
Learners need to know four simple, one-word commands to
actively execute unlimited capabilities: forward, back, left, right. Young
people control the movements of a cybernetic "turtle", a triangular
cursor displayed on the computer screen, by telling the "turtle" to draw
designs (Papert, 1980).
This control encourages the young learner to explore more intricate skills by applying his personal and physical knowledge of the world
to design and debug his own program (Papert, 1980; Lepper, 1982}.
When children explore the "turtle's" behavior, they draw from their
11
intuitive knowledge" of geometry and physics (Solomon, 1982).
Andy
qiSessa developed a special "dynaturtle" for the learner to explore
physics.
(Malone & Levin, 1981; diSessa &: B. White, 1982).
Logo encourages structured procedural thinking, however the
learner can experiment (program and debug) and receive immediate
visual feedback (Schwartz et al., 1984; Olds, 1984; Megarry, 1983).
Complex designs can be developed by repeating and computing programs
(Papert, 1980; Lepper, 1982; Lawler, 1982; D. Watt, 1983).
Logo has proliferated in elementary schools (Schwartz, et a1.,
1984), even though Logo has the weakness of limited memory and
speed.
Logo acts as a tool to initiate abstract thinking by relating new
ideas to familiar, concrete experiences (Carter, 1983). Gorman Bourn's
study noted youngsters who had Logo learning significantly improved
their logical thinking and sequencing (Lepper, 1982; Carter, 1983).
15
The flexibility of the computer allows the learning process to
develop in various directions, yet the end result leads to the same
objective (Sweeney, 1984; D. Watt, 1982; D. Watt, 1984).
can explore his intellectual style of learning.
Each child
One learner can use any,
or all of the three distinct styles of learning; planner, macro learner
5
and micro learner (Goldenberg, 1982). Teachers who introduce Logo
need to know how a student thinks, in order to offer appropriate and
effective intervention (Moore, 1983).
If all students were forced into
the same style of learning, Logo learning would be counterproductive
(Goldenberg, 1982).
Almost any task appropriate for a computer can be accomplished
with Logo because it is a list-processing language that simulates human
thought.
"Words or numbers can be used in any order or combination"
,
(Laugh & Tipps, 1983).
Purpose of the Study
Little evidence was found in the literature that related to
attitudes toward computer learning. The focus of prior studies has been [
on high school and college age students, rather than the eight to
thirteen year old group (Gorman, 1982; Schwartz et al., 1984).
The younger age group did receive some attention from the
following authors. Malone (1981) discussed his own theory, about how to
successfully design computer activities that would have high intrinsic
motivation by studying the appeal of arcade games to elementary
school age people. Brady and Hill (1984) were concerned with the
affective prerequisites necessary for young people to learn a computer
language. Early computer access was advocated by Lepper (1982)cand J.
15
Brown (1983). Seymour Papert (1980) created the computer language,
Logo, to introduce computer programming to elementary school students. The intention of this study is to develop an understanding of the
affective and cognitive attributes that relate to the acquisition of
computer competency.
This research was conducted to analyze the relationship of
computer motivation to:
1.
Academic intrinsic motivation
2.
Attributions
3.
Logo computer learning
4.
Intellectual ability
The role of gender in these relationships was also examined. The
instrument used to measure computer motivation was the Computer
~otivation
Inventory (CMI) (Gottfried & Kee, 1985). Computer Motiva-
tion concerns children's intrinsic interest in learning through the use of
co-mputers. The following hypotheses guided the research:
l.
Computer motivation and academic intrinsic motivation are
positively correlated. Academic intrinsic motivation is measured by the
Children's Academic Intrinsic Motivation Inventory (CAIMI) (Gottfried,
1984) which contains five school subjects: social studies, science, rna th,
reading, and general intrinsic motivation. Results of the CMI were
correlated with the CAIMI to determine the correlation.
2.
Computer motivation and attributions of effort and ability
in computer performance are positively correlated. Attributions refer to
the learner's attitude about his success or failure using the computer.
The results of the CMI and attributions were correlated.
Q
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17
3.
Computer motivation and Logo learning are positively
correlated. The results of the CMI were correlated with a pre- and
post-Logo test that evaluated the learner's motivation before and after
the three week Logo course.
4.
There is a positive correlation between intellectual 1ability
and computer learning. A WISC-R vocabulary and block design test was
administered to each learner. The results of both the CMI and WISC
vocabulary and block design test were correlated. The above correlations were determined by Pearson's Product-Moment Correlation Test.
Chapter III
Method
Subjects
Participants in this study were 34 male and 35 female children
from 8 to 13 years old (with the mean age of 11.1} from a middle to
upper-middle class community in Orange County, California. These
children volunteered to take a computer class in Logo, a computer
programming language, during the summer of 1983 in a computer
laboratory equipped with Apple computers and software, at California
State University, Fullerton.
These 69 participants were randomly selected into six groups,
each consisting of 11 or 12 children. Two groups were conducted at
each of the following times: 8:00 A.M., 9:30 A.M., 11:00 A.M.
The six
groups were given 14 days of computer learning over a three week
period, for one and a half hours.
The instructors had prior training in teaching Logo to elementary
school children. The structure of the classes consisted of two parts.
First, students were given traditional, formal lessons to learn the Logo
commands that would enable them to: design graphics, write logical and
conditional arithmetic procedures, edit their work, save and read their
files, use special characters. A total score for each type of command
was tabulated for each student. Second, the students applied the new
knowledge by independently writing their own programs.
16
19
Procedure
Measures Administered:
1.
Computer Motivation Inventory
2.
Children's Academic Intrinsic Motivation Inventory
3.
Attributions of Computer Motivation Inventory
4.
Logo Pre- and Post-test
5.
Wechsler Intelligence Scale for Children Revised
(WISC-R block design and vocabulary test)
Computer Motivation Inventory (CMI)
CMI consists of 30 items. The CMI was developed by Gottfried
and Kee (1984). This inventory was group administered at the beginning
of the summer session to all children. All items referred to the
learners' intrinsic motivation about using a computer. Feelings used to
describe contact with the computer were: Boredom, interest, curiosity
and enjoyment. The CMI is reliable and valid. The inventory has a
coefficient alpha reliability of .95.
After the administrator read the directions, the administrator
waited for the participants to circle the first two items of the test to
assure understanding. The directions, and test format, using the fivepoint Likert scale was the same as the CAIMI test. A total score was
developed by summing the items.
Pairs of reversal sentences appeared to assure understanding of
content. For example, "I like to do as little work as I can with
20
computers," "I like to do as much work as I can with computers."
Other samples of test items were: "I am not interested in learning new
ideas about using a computer," "I enjoy doing easy activities with a
computer," "I think it is interesting to do work using a computer," "I
give up easily when I do not understand an activity with a computer."
The scoring of these items were approprately reversed. Higher scores
correspond to higher computer motivation.
This test took approximately 30 minutes. There was no time
limit.
Children's Academic Intrinsic Motivation Inventory (CAIMI}
The CAIMI was administered during the first week of the course.
The CAIMI test, developed by A.E. Gottfried (1985}, investigated children's academic intrinsic motivation. The test contains five scales:
Reading, Math, Social Studies, Science, and General Intrinsic Motivation. The first four school subject scales contain 26 items each and the
last, General Intrinsic Motivation, contains 18 items interspersed
throughout the test. Items are scored on a 5-point Likert scale, ranging
from strongly agree to strongly disagree. CAIMI was developed for
children in fourth through eighth grade. Coefficient alpha of the scales
range from •77 to .96, which demonstrates the high internal consistency
of the test. The test-retest reliability ranges from .69 to .75. Items
were scored so that higher scores correspond to higher motivation.
Prior to this 122 item self reported inventory, all students were
informed of the confidentiality of their answers. Instructions were read
aloud by an adult administrator while the students read along silently.
21
Participants responded by circling a number representing their choice. A
five-point Likert scale was used for the answers ranging from strongly
agree (1) to strongly disagree (5).
The CAIMI posesses high reliability, validity and is psychometrically sound (Gottfried, 1985).
Attributions of the CMI Test
An additional 13 items were group administered to tap children's
interest in learning Logo, perception of competence in Logo learning,
and causal attributions regarding computer learning. Attributions are
scored the same as the CMI test, with a five-point Likert scale.
The first three items refer to the learner's attitude toward Logo
programming (enjoyment, interest, ability). Three other items are
c~mprised
of external rewards, such as pleasing parents and teachers
and getting good grades. The remaining seven items refer to reasons
for success on the computer as viewed by the participant (e.g., luck,
hard work, intelligence).
Items included: "It is interesting to learn how to program a
computer using Logo. I do not enjoy programming the computer using
Logo. I do well at programming Logo. I enjoy working with a computer
because I want to please my parents. I enjoy working with a computer
because it helps me get good grades in school. I enjoy working with a
computer because I want to please my school teacher. I do well using a
computer because I work hard at it. I do well using a computer
because I am lucky. I don't do well using a computer because I am not
smart enough. I don't do well using a computer because the activities
22
are hard. I do well using a computer because I am smart. I do well
using a computer because the activities are easy. I don't do well using
a computer because I am unlucky. I don't do well using a computer
because I don't work hard enough at it."
Pre- and Post-Logo Test
Children were asked to recall the Logo commands they knew. A
pretest was administered prior to the beginning of the course and a
post-test was administered at the end of the course. Several measures
were derived from these responses. These included: turtle graphics,
logical/conditional commands, editing commands, filing commands, magic
words, special characters, other commands, and total score of
commands.
Wechsler Intelligence Scale for Children Revised (WISC-R Block
Design and Vocabulary Test)
Children were individually administered the WISC-R block design
and vocabulary subtests to assess both spatial and verbal ability.
Standardized scaled scores were used in the analyses. These subtests
were administered towards the latter portion of the course.
@
•
Chapter IV
Results
The CMI was correlated with the CAIMI, causal attributions of
the CMI, Logo test, and the WISC-R ability test in a Pearson productmoment coefficient test. Mean differences of the sexes were established with a T-test. Control for the effects of age, spatial ability, and
Logo were conducted with partial correlations.
CMI Correlation with CAIMI
Computer motivation (CM) measured by the CMI and academic
intrinsic motivation measured by CAIMI proved to be positively corr~lated.
The four school subjects ranged from r = .43 to r = .47 with
math and social studies having slightly higher scores (see Table 1).
CMI and Causal Attributions
High positive correlations with high probability ranged from
r = .64 to r = .78 when CMI was correlated with enjoyment of Logo
learning, self-esteem, and hard work. Results are presented in Table 2.
Moderate positive correlations, ranging from r = .39 to r ·= .54
were reported when CMI was correlated with the attributions of getting
good grades, and pleasing parents and teachers.
Low negative correlations, ranging from r = -.11 to r = -.14
were evident when CMI was correlated with "easy work" and feeling
lucky.
23
24
CMI Correlations with Logo Pre- and Posttest
Logo pre- and posttests measured the recall knowledge of the
Logo commands at the onset and conclusion of the course.
No significant correlation was found between the CMI measure
and the Logo pretest. However, the Logo posttest indicated a positive
correlation between CMI and Logo learning, with r
= .24,
p
= .03.
The only individual item that correlates with the CMI on the
Logo posttest was graphics. Boys and girls together scored an r
p
= .31,
= .008.
CMI Correlated with the WISC-R Ability Test
The CMI with WISC Vocabulary correlation was found not signific~nt,
but the correlation of C MI and the WISC Block Design test
(spatial ability) was r
= .28,
p<.05 for both boys and girls.
Logo and WISC-R Correlations
Logo post-test scores and WISC block design test revealed a
positive and significant correlation of r
= .32,
p
= .04.
Yet the pretest
Logo scores and the WISC vocabulary test had a non-significant correlation of r
= .0 2.
One item on the Logo post-test, "learning othet· commands," had
a positive and significant correlation with the WISC block design test,
r= .33, p = .03.
25
Mean Difference of the Sexes
Means and standard deviations for all variables, as well as significant sex differences are reported in Table 3.
In general, there were few sex differences. Significant mean
differences between the sexes were established in the areas of reading
and social studies, two sections of the CAIMI test, and "lack of effort," an item from the attribution inventory.
Girls 1 mean scores were higher in reading (girls
boys
= 81.)
and social studies (girls
= 87.,
boys
= 71.)
=
95.,
than boys 1 mean
scores with a T value of 2.14 and 2.64 respectively.
The highest T value was recorded in "lack of effort"(2.65) which
was measured from an item on the causal attributions of the CMI. The
:wording of the item was, "I don1t do well using a computer because I
don 1 t try hard enough." Self-perception of competence, effort, ability
and interest was measured in this variable.
Girls received a higher mean score than the boys, which demonstrates a weaker self-perception (girls
= 4.,
boys
= 3.).
Partial Correlations
Age and spatial ability were partialled from correlations between
the CMI and CAIMI, Attributions, and WISC-R ability test in order to
control for their effects. These correlations continued to be significant
beyond the variables of age and spatial ability.
26
Table 1
Correlations Between the Computer Motivation Inventory (CMI)
and Children's Academic Intrinsic Motivation Inventory (CAIMI)
CMI
with:
Math
CAlM I
Social
Studies
Reading
Science
General
Girls
.44***
.50**
.51**
.48**
.37*
Boys
.38*
.40**
.40*
.40*
.38*
Total
.44***
.47***
.47***
.43***
.38**
Probability
=
*p <.05
**p (.01
***p
<.001
27
Table 2
Computer Motivation InvE:mtory' (CMI) and Attribution Correlations
CMI with Attributions
Girls
Learning Logo is interesting
Logo is uninteresting
.81 ***
-. 73***
r Values
Boys
•75***
-.52**
Totals
•78***
-.64***
I am a good Logo progammer
.78***
.57***
.64***
I learn about computers to
please parents
.37*
.44*
.41***
.45**
.33*
.39***
I learn about computers
to please my teacher
.54***
.55**
.54***
I work hard to be a
good programmer
.74***
.67***
.71***
I am a good programmer
because of luck
-.08
I work with computers for
good grades
-.27
-.14
I am not smart enough to be
a good programmer
-.67***
-.57***
-.61 ***
I am not good at programming
because it is difficult
-.54***
-.69***
-.61 ***
.60***
.46***
.51***
I am a good programmer because
I am smart
I find computer work easy
-.13
-.10
-.11 ***
I am not a good programmer
because I am unlucky
-. 78*
-.44*
-.62***
I don't work hard enough
to be a good programmer
-. 72***
-.40*
-.56***
Note: Positive and negative values have been reversed for clarity.
Probability: *p<.05
**p(.Ol
***p(.OOl
28
TABLE 3. TOTAL MEANS AND STANDARD DEVIATION
Test
Means
GIRLS BOYS
S.D.
TOTAL
T-TEST
VALUE
GIRLS
BOYS
TOTAL
Age
11.31
10.93
11.16
1.07
1.42
1.58
1.40
CMI
123.03
122.36
122.69
.99
17.80
16.13
17.68
93.46
97.19
86.10
89.28
67.09
81.84
93.67
77.78
85.89
67.31
87.66
95.33
82.42
87.67
67.19
2.14*
.98
2.64*
1.06
1.36
16.52.
19.07
17.68
14.65
8.73
21.15
18.15
22.93
22.19
9.54
19.72
18.51
20.39
18.50
9.02
Attributions
Interesting
Uninteresting
Self-esteem
Parents
Grades
Teacher
Hard work
Luck
Not smart
Too difficult
Sn)art
Easy work
Unlucky
Lack of effort
1.76
4.12
2.28
2.64
2.69
2.85
2.15
3.55
4.27
4.03
2.12
2.97
4.06
4.24
1.74
4.16
2.00
2.74
2.71
3.16
2.19
3.42
4.10
3.93
2.16
2.97
4.16
4.00
1.75
4.14
2.14
3.68
2.70
3.00
2.17
3.48
4.19
3.98
2.14
2.97
4.10
4.13
.37
.90
-.22
.22
-.24
1.04
1.20
.27
.39
.47
-,58
.87
.47
2.65*
.83
.99
.99
1.03
.92
.97
.94
1.06
.76
.95
.78
.98
.86
.75
.89
.90
.97
1.34
1.32
1.46
.98
1.36
1.27
1.14
1.07
1.05
.86
1.06
.85
.94
.98
1.18
1.12
1.23
.95
1.21
1.04
1.04
.92
1.01
.86
.91
Logo Pretest
1.30
.32
.81
-1.68
2.97
1.35
2.37
Logo Post-test
Graphics
Logic
Other Commands
Editing
File
Special Commands
15.57
7.27
.52
1.81
3.60
1.49
.88
12.48
6.24
.48
1.26
2.16
1.00
.80
14.08
7.06
.50
1.55
2.91
1.25
.84
-1.86
-.75
-.25
-1.65
-1.71
-1.57
-.30
7.15
2.55
.51
1.40
3.70
1.39
.99
6.06
2.05
.51
1.31
2.97
1.03
.95
6.78
2.31
.51
1.38
3.42
1.24
.96
WISC-R Vocabulary
12.35
12.39
12.33
-.52
2.66
2.93
2.78
WISC-R Block
13.18
13.30
13.24
-.62
2.83
2.83
2.81
CAlM I
Reading
Math
Social Studies
Science
General Attitude
*p <.OS
0
Chapter V
Discussion
Hypotheses
Students with greater intrinsic motivation for computer learning,
academic intrinsic motivation, causal attributions of effort and ability,
spatial ability and positive attitude toward and interest in learning
Logo, achieved greater success in learning Logo.
Hypothesis 1
Computer motivation and academic intrinsic motivation are
positively and significantly correlated. This hypothesis proved to be true
with the correlation of the CAIMI measure and CMI.
These positive and significant correlations indicated that children
with greater academic intrinsic motivation had greater computer
motivation.
Hypothesis 2
Computer motivation and attributions of attitude toward effort
and ability in computer performance are positively and significantly
correlated. This hypothesis was proven by correlating attl.'ibutions and
CMI.
Students with higher computer motivation had higher self-esteem
and felt smarter.
29
•
30
The more highly motivated students accepted learning as a
challenge of hard work. Conversely, participants understood computer
knowledge is not related to luck or easy work.
Pleasing parents, teachers, and getting good grades were also
found to be motivating factors to computer programming.
Hypothesis 3
Computer motivation and Logo learning are significantly and
positively correlated. This hypothesis was supported as evidenced by the
correlation between Logo posttest and the CMI, which indicated that as
knowledge of Logo increased, computer motivation increased. These
correlations remained strong and significant after partialling out age
and ability.
~ypothesis
4
Intellectual ability and computer learning have a positive and
significant correlation. This hypothesis was supported by the positive
correlation between the WISC-R block design test and the C MI.
Computer learning was uniquely and positively related to spatial ability.
Conclusion
We are currently on the edge of a revolution
in technology that may prove more significant
than any other technological advance in 200
years - as powerful microcomputers begin to
infiltrate our lives, in business and industry,
in our homes and in our schools (Lepper,
1985).
Q .
31
This rapid, widespread distribution of modern technology will
enable our children to be educated via computers. Computer-based
instruction encourages the process of discovery-based inductive learning.
The focus of education will move away from cognitive instruction, since
we will have a more information-based society (Lepper, 1985).
This study established important factors that could be useful as a
guiding philosophy for further research in computer literacy. Computer
motivation was identified as an independent and valid form of intrinsic
motivation which related to spatial ability and Logo learning.
The first step in intrinsic computer learning is to have tools
available to the learner. Our schools have been remiss in not providing
educational software (Gibbon, 1983; Walker, 1980).
If schools do not modernize their fixed format and ultraconserva-
tive ways to utilize the potential of the computer to enrich learning
(~oyd,
1983), the powers of educational institutions will be diminished
(Hoyle, 1983). Presently, schools are not making the best use of the
computers they have (Connell, 1983; Hoyle, 1983; Schneiderman, 1984).
It is commonly believed that much will be gained by expanding
our curriculum to include computer science, but will that innovation
become reality? Malone (1982) is optimistic, yet Hoyle (1983) believes
computers will only be used as a supplementary teaching tool. Potential
benefits and costs of early computer access needs further investigation.
Malone's research (1981) was a significant breakthrough because
he identified what the students found intriguing about computers.
Future research in Logo learning using a broader geographically-based
population from a wider socio-economic background with randomly
sampled subjects might facilitate educators in developing computer
32
curriculums,
classroom
interventions
and
computer
learning
environments.
Hopefully, computer learning will become more than a skill, but
a lifelong passion that will inspire and ultimately reward millions of
young people.
O.K. Tikhomirov wrote, "Just as the development of gasoline
engines provided a tool for human physical activity, so the development
of the computer provided a tool for human mental activity" (Greenfield, 1984, page 153).
Chapter VI
Notes
1.
Problem-solving decomposition refers to the method of segmenting the problem into sections. After eac11 section is solved, it is
combined to solve the "whole."
2.
Formal operational thinking, Piaget's highest level of cognitive
development, occurs when mental rearrangement of abstract
propositions takes place.
3.
A student's active acquisition of knowledge through alertness and
monitoring information is self-regulated learning.
4~
Transformation process refers to the discovery of relevant
material, connecting new information with past knowledge and
applying this new information toward a solution.
5.
Goldenberg explains a "planner" as the learner who builds structured programs from top to bottom. A "macro explorer" is the
student who explores the effects of building blocks with openended results. A "micro explorer" describes the learner who is
cautious, conservative and gradual.
33
Chapter VII
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