COMPUTER ATTITUDE, SELF EFFICACY, AND USAGE:
PERSPECTIVES FROM SCIENCE AND ENGINEERING STUDENTS
Felipe B. Martinez
Physical Sciences Department, College of Science, De La Salle University, Dasmarinas, Cavite, Philippines
ABSTRACT
This study attempts to describe and compare the computer attitude, computer
self efficacy, and computer usage between Science and Engineering students of De La
Salle University-Dasmarinas. A survey questionnaire was administered to a total of 60
student respondents. The result shows that there is a statistically reliable difference
between the mean score of science and engineering students in the usage of computer
for inquiry purposes with an F value of 0.85 and p value of 0.36 respectively. In the
usage of computer for communication, t test results failed to reveal a statistically reliable
difference between the mean scores of science and engineering students with an F
value of 0.01 and p value of 0.93 respectively. Likewise, t test results did not reveal a
statistically significant difference between the mean scores of science and engineering
students for both the computer attitude (F value = 5.05, p value = 0.02) and computer
efficacy (F value = 2.19, p value = 0.14). One - way ANOVA of the means of four
components of computer attitude scale of science and engineering students shows that
there is a statistically significant difference between the means of the components with
an F value of 65.86, a p value of 0.00 and F value of 20.32, a p value of 0.00
respectively. The results of one way ANOVA for science and engineering students both
failed to reveal a statistically significant difference between the means of the three
levels of computer efficacy with an F value of 2.55, a p value of 0.08 and F value of
1.43, p value of 0.24 respectively. Pearson r correlation coefficient of attitude towards
computer and computer efficacy of science and engineering students shows no
correlation at all. The result shows no significant relationship between affective and
perceived control and usefulness and behavioral intention and perceived control and
usefulness of science students. There was also no significant relationship between
perceived control and perceived usefulness. However, the result shows a statistically
significant relationship between affective and behavioral intention. For engineering
students, the result also shows a statistically significant relationship between affective
and behavioral intention and perceived control. There was also a significant relationship
between perceived control and perceived usefulness and behavioral intention. On the
other hand, the result failed to reveal a statistically significant relationship between
affective and perceived usefulness and behavioral intention and perceived usefulness.
Technology training is a great factor for the participant to possess positive
attitudes towards computer and in gaining confidence in different levels of computer
efficacy. The usage of computer for inquiry and communication can be maximized if
participant will be exposed in more technology training. A further study can be
conducted considering large sample size for the two groups of respondents.
Relationship of age, gender, and participant with completed technology training and
different variables can also be considered.
INTRODUCTION
The 21st century education is facing issues and challenges in different areas of
learning. Learners should learn the 21st century skills like complex communication
skills, adaptability, and the ability to solve non routine problems (Levy and Murnane,
2004) which is one of the issues that need to be addressed. Digital technology was also
introduced into our lives which created a diverse learner (Withrow, 2011). One
challenge now to educators is the application of educational technology in the
classroom because the advancement in technology was no longer measured in the past
due to rapid development. The discoveries of computer in different fields were
unending. Computer nowadays is inevitable as evidence by the number of users that’s
why a suitable technology in the classroom should be applied.
Teaching with excellence and school leadership are two aspects that have
gained renewed emphasis in different fields of research (Ambrose and Wilson, 2008).
These two areas should be addressed to deal with the issues and challenges in
different areas of learning in the 21st century education. To gain excellence in teaching,
an educator must undergo continuous training related to field of teaching, in educational
technology for example. An educator should also be resourceful in pedagogical
applications because current research shows that there are reliable differences among
attitude towards computers of males and females and between students and teachers
(Robertson, 1995).
On the other hand, school leaders must address issues and challenges of the
21st century education. They must do systematic transformation of the school curriculum
aims to attend current issues and designed to develop the 21st century skills. Leaders
should integrate information and communication technologies into education in all levels
employing all 21st century tools (computers, telecommunications, videos, etc.) which
recognizes that technology is, and will go on to be, a motivating force in workplaces,
society, and personal lives in the 21st century (Salpeter, 2003).
In the Philippines computer is widely used in many fields all over the country. But
in education sector especially in the public, use of computers are very limited for both
educators and the learners. This is the reason why many individuals hold positive
attitudes about computers; many persons express grave fears and have been tagged
“technophobic” (North and Noyes, 2001) Computer education must be improved to offer
students convenient access to computers (Lamont,Liu,and Maddux, 2004). Assessment
must be done first before the transformation of curriculum and incorporation of 21 st
century tools. Attitude towards technology must be identified to determine where the
transformation of curriculum will start and what 21st century tools will be employed.
METHODOLOGY
A total of 60 students were used as samples of the study. These samples came
from college of science and college of engineering of De La Salle UniversityDasmarinas during the second semester of SY 2011-2012.
A computer usage scale adopted from the work of Bruce and Levin (1997) was
used to determine the purpose of using computers of science and engineering students.
It has two major categories namely; inquiry and communication. The inquiry component
is further subdivided into four categories namely; technology for thinking, data access,
extending the senses, and data analysis. The communication component has also four
subdivisions namely; document preparation, communicating with others, collaborative
media, and teaching. A Likert – type response scale was used to determine the
frequency of use of computer of the samples. The students were asked to tick the
appropriate option and encircle the corresponding frequency 1, 2, 3, and 4 which
pertains to never, rarely, sometimes, and often respectively.
Another instruments used were computer attitude scale developed by Selwyn
(1997) and a computer self efficacy scale developed by Murphy et. al (1989). The
computer attitude scale has four components namely; component 1, affective,
component 2, behavioral intention, component 3, perceived control, and component 4,
perceived usefulness. The computer self efficacy scale is composed of thirty two
statements with three computing levels namely; beginner’s level, advance level, and a
level related with mainframe computers. A Likert – type response scale was used in
these two instruments to determine the scale that best describes the confidence in
using the computer of the samples. The students were asked to encircle the
corresponding scale using 1, 2, 3, 4, and 5 which pertains to strongly disagree,
disagree, neutral, agree, and strongly agree respectively.
To study the properties of the measurement scales and the items that make them
up, reliability analysis was used. The reliability of the survey questionnaire was found to
be 0.95 and 0.97 for computer usage and computer self efficacy respectively.
T test was used to determine if there is a significant difference between the
means of scores of the two groups of samples in different components of the survey
questionnaire while Anova was used to determine if there is a significant difference
between the mean scores of science and engineering students in different components
of attitude towards computer and computer efficacy. To find out which of the mean
scores in the components of computer attitude and computer efficacy scale differ
significantly, the Tukey method was used
Pearson r correlation coefficient was also used to determine if there is a
significant relationship between the scores of science and engineering students in
computer attitude and computer self efficacy. It was also used to determine if there is a
significant relationship between the four components of attitude towards computer.
RESULTS AND DISCUSSION
The gathered data were clustered per categories and were grouped according to
course. The mean scores were compared and tabulated.
Figure 1 shows the distribution of samples according to course and gender. The
figure shows that there are 9 science and 24 engineering male students. The female
comprise of 21 science and 6 engineering students.
Science Students
Engineering Students
6, 20%
9, 30%
Male
21, 70%
Male
24, 80%
Female
Female
Figure1. Distribution of samples according to course and gender
Figure 2 shows the distribution of samples of science and engineering students
who completed and currently enrolled in a technology training course. The data shows
that 35% and 58% of science and engineering students respectively are currently
enrolled in a technology training course while 65% and 42% of science and engineering
were already completed the training. 10 out of 30 science students are neither enrolled
nor completed a technology training course while only 12 engineering students is either
enrolled or completed a training course in technology.
Science Students
Engineering Students
5, 42%
7, 35%
13, 65%
Completed
Training
Cuurently
enrolled
7, 58%
Completed
Training
Cuurently
enrolled
Figure2. Distribution of samples of science and engineering students who
completed and currently enrolled in a training course in technology
Figure 3 shows the distribution of samples of science and engineering students
based on the frequency of use of computers. The figure shows that science students
use computer more frequent than engineering students. 80% of science students use
computer 5 or more times a week while only 24% of science students
Science Students
1, 3%
1,
3%
4,
14
%
Engineering Students
Less than once
a week
1-2 times a
week
15,
50%
3, 10%
Less than once
a week
3, 10%
3-4 times a
week
3-4 times a
week
5 or more
times a week
24, 80%
1-2 times a
week
9, 30%
5 or more
times a week
Figure3. Distribution of samples of science and engineering students based on
the frequency of use of computers
Figure 4 presents the average mean of science and engineering students in
computer usage for inquiry purposes. The data shows that the engineering students
scored significantly higher in data access component with a mean score of 2.33. The
science students got a mean of 1.55 in technology for thinking component being the
lowest in all categories. The over all mean of 2.08 of engineering students shows that
they are more inclined in using different features of the computer included in the survey
questionnaire. It also shows that they use the features of the computers more frequent
than the science students.
2.33
2.09
1.9
1.95
1.96
2.14
1.74
2.08
1.83
1.55
Science Students
SD of SS
0.74 0.67
0.67 0.73
0.51
0.62
0.77 0.72
0.67 0.69
Engineering Students
SD of ES
Technology for
thinking
Data Access
Extending the
senses
Data Analysis
Average
mean/SD
Figure4. Mean scores of science and engineering students in the usage of
computer for inquiry purposes
The data analysis component of science students has the widest spread (0.77)
which is higher compared to the overall standard deviation of 0.67. Extending the
senses component of engineering students has the narrowest spread with an SD of
0.51.The data analysis component of science students has the widest spread a little bit
lower to the SD of technology for thinking.
Figure 5 presents the mean scores of science and engineering students in the
usage of computer for inquiry purposes. The data shows that the engineering students
scored significantly higher in document preparation component with a mean score of
2.52. The engineering students also got a mean of 1.99 in teaching component being
the lowest in all categories. Still, the results shows that the over all mean of 2.24 of
engineering students shows that they are more inclined in using different features of the
computer in communication component included in the survey questionnaire. The result
also shows that they use the features of the computers more frequent than the science
students.
Teaching component of both science and engineering students have the widest
spread with a standard deviation of 1.11 and 1.02 respectively while communicating
with others component have the narrowest spread with an SD of 0.72 and 0.71
respectively.
2.43 2.52
2.22
2.35
2.14
2.35
2.13
1.99
2.11
2.24
Science Students
0.83 0.83
1.11 1.02
0.96
0.72 0.71
0.73
SD of SS
0.91 0.82
Engineering Students
SD of ES
Document
preparation
Communicating
with others
Collaborative
media
Teaching
Average
mean/SD
Figure5. Mean scores of science and engineering students in the usage of
computer for communication purposes
Table 1 shows the independent sample test for inquiry and communication
purposes of science and engineering students. The t test results revealed that there is a
statistically reliable difference between the mean score of science and engineering
students in the usage of computer for inquiry purposes. In communication, the t test
result failed to reveal a statistically reliable difference between the mean scores of
science and engineering students.
Table1. Independent sample test for inquiry and communication purposes of
usage in computer of science and engineering students
t Test for Equality of Means
Levene's Test for Equality of Variances
Sig. (2-
MEAN
Equal variances assumed
INQUIRY
MEAN
Sig.
t
df
tailed)
.851
.360
-3.651
58
.001
-3.651
57.380
.001
-1.426
58
.159
-1.426
58.000
.159
Equal variances not assumed
Equal variances assumed
COMMUNI
F
.008
.930
Equal variances not assumed
CATION
Figure 6 shows the mean scores of science and engineering students in attitude
towards computer. The data shows that the science students scored a mean of 4.35 in
perceived usefulness component. This is significantly higher compared to the average
mean score of 3.16. The engineering students got the lowest mean of 2.52 in affective
component. The mean suggests that the participant perceptions on the usefulness of
computer is more positive than they believed it was helpful and has been liked, their
control of computer, and intention to use computer. The overall mean of science and
engineering students in computer attitude component only shows an average attitude
towards the use of computer with a mean of 3.16 and 3.11 respectively.
The standard deviation of engineering students in perceived usefulness
component has the widest spread while perceived control component has the narrowest
spread with an SD of 1.14 and 0.42 respectively.
4.35 4.19
3.16 3.14
2.57 2.52
3.16 3.11
Science Students
2.58 2.59
SD of SS
0.57 0.64
Affective
0.53
0.8
Behavioral
intention
Engineering Students
1.14
0.42
0.72
Perceived
control
0.71
Perceived
usefulness
0.56
0.83
SD of ES
Average
mean/SD
Figure6. Mean scores of science and engineering students in attitude towards
computer
Table 2 shows the independent sample test for attitude towards computer of
science and engineering students. The t test result failed to reveal a statistically reliable
difference between the mean scores of science and engineering students.
Table2. Independent sample test for attitude towards computer of science and
engineering students
t Test for Equality of Means
Levene's Test for Equality of Variances
Sig. (2-
MEAN
Equal variances assumed
COMPUTER
F
Sig.
t
df
tailed)
5.493
.023
.819
58
.416
.819
40.702
.418
Equal variances not assumed
ATTITUDE
Figure 7 shows the mean scores of science and engineering students in different
levels of computer efficacy. The data shows that science students are more confident in
using the computer with an average mean of 4.14. This is a higher compared with the
average mean score of 3.83 of engineering students.
The standard deviation of engineering students has a wide spread of 0.85
compared with the standard deviation of science students with a value of 0.67.
4.35
4.02
3.97
3.82
4.14
4.09
3.64
3.83
Science Students
SD of SS
Engineering Students
0.54
0.82
Beginner
0.65
0.77
Advanced
0.82
0.97
Mainframe
0.67
0.85
SD of ES
Average mean/SD
Figure7. Mean scores of science and engineering students in different levels of
computer efficacy
Table 4 shows the independent sample test for computer efficacy of science and
engineering students. The t test result failed to reveal a statistically reliable difference
between the mean scores of science and engineering students in computer efficacy. It
means that there confidence in using the computer is the same.
Table4. Independent sample test for computer efficacy of science and
engineering students
Levene's Test for Equality of Variances
t Test for Equality of Means
Sig. (2-
MEAN
Equal variances assumed
COMPUTER
EFFICACY
F
Sig.
T
df
tailed)
2.191
.144
1.517
58
.135
1.517
52.378
.135
Equal variances not
assumed
Table 5 shows the one - way ANOVA of the means of four components of
computer attitude scale of science students. The result shows that there is a statistically
significant difference between the means of the components with an F value of 65.86
and a p value of 0.00. This shows that science students scored significantly higher in at
least one of the components.
Table5. One - way ANOVA of the means of four components of computer attitude
scale of science students.
Sum of Squares
Df
Mean Square
F
Sig.
Between Groups
63.310
3
21.103
65.858
.000
Within Groups
37.170
116
.320
Total
100.480
119
To find out which of the mean scores in the four components of computer attitude
scale differ significantly, the Tukey method was used. A Tukey post – hoc analysis
(Table 6) revealed that there were statistically significant differences between all the
components except for component 1 and 2. The result also shows that the mean score
of component 4 is significantly higher than the mean scores of component 1, 2, and 3.
Table6. Multiple comparisons of the means of four components of computer
attitude scale of science students.
(I)
95% Confidence Interval
Compo (J)
nent
Mean Difference (I-
Component
J)
Std. Error
Sig.
Lower Bound
Upper Bound
2
-.00833
.14616
1.000
-.3893
.3727
3
-.59500*
.14616
.000
-.9760
-.2140
4
-1.78333*
.14616
.000
-2.1643
-1.4023
1
.00833
.14616
1.000
-.3727
.3893
3
-.58667*
.14616
.001
-.9677
-.2057
4
-1.77500*
.14616
.000
-2.1560
-1.3940
1
.59500*
.14616
.000
.2140
.9760
2
.58667*
.14616
.001
.2057
.9677
4
-1.18833*
.14616
.000
-1.5693
-.8073
1
1.78333*
.14616
.000
1.4023
2.1643
2
1.77500*
.14616
.000
1.3940
2.1560
3
1.18833*
.14616
.000
.8073
1.5693
1
2
3
4
*. The mean difference is significant at the 0.05 level.
Table 7 shows the one - way ANOVA of the means of four components of
computer attitude scale of engineering students. The result shows that there is a
statistically significant difference between the means of the four components with an F
value of 20.32 and a p value of 0.00. This shows that engineering students scored
significantly higher in at least one of the components.
Table7. One - way ANOVA of the means of four components of computer attitude
scale of engineering students.
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
43.880
3
14.627
20.317
.000
Within Groups
83.511
116
.720
Total
127.391
119
A Tukey post – hoc analysis (Table 8) revealed that there were no statistically
significant differences between component 1 (affective) and component 2 (behavioral
intention) and between component 2 and component 3 (perceived control). The result
also shows that there were significant differences between the means of component 1
and component 4 (perceived usefulness), between the means of component 2 and 4,
between component 3 and 1 and 2, and between component 4 and 1, 2, and 3.
Table8. Multiple comparisons of the means of four components of computer
attitude scale of engineering students.
(I)
95% Confidence Interval
Compo
nents
1
2
3
4
Mean
(J) Components Difference (I-J)
Std. Error
Sig.
Lower Bound
Upper Bound
2
-.13100
.21908
.932
-.7021
.4401
3
-.64600*
.21908
.020
-1.2171
-.0749
4
-1.53933*
.21908
.000
-2.1104
-.9683
1
.13100
.21908
.932
-.4401
.7021
3
-.51500
.21908
.093
-1.0861
.0561
4
-1.40833*
.21908
.000
-1.9794
-.8373
1
.64600*
.21908
.020
.0749
1.2171
2
.51500
.21908
.093
-.0561
1.0861
4
-.89333*
.21908
.000
-1.4644
-.3223
1
1.53933*
.21908
.000
.9683
2.1104
2
1.40833*
.21908
.000
.8373
1.9794
3
.89333*
.21908
.000
.3223
1.4644
*. The mean difference is significant at the 0.05 level.
Table 9 and 10 shows the one - way ANOVA of the means of three levels of
computer efficacy of science and engineering students. The results both failed to reveal
a statistically significant difference between the means of the three levels of computer
efficacy with an F value of 2.55, a p value of 0.08 and F value of 1.43, p value of 0.24
respectively. This shows that science and engineering students have the same
confidence in the three levels of computer efficacy.
Table 9. One - way ANOVA of the means of three levels of computer efficacy of
science students
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
2.357
2
1.178
2.545
.084
Within Groups
40.284
87
.463
Total
42.641
89
Table10. One - way ANOVA of the means of three levels of computer efficacy of
engineering students.
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
2.107
2
1.054
1.435
.244
Within Groups
63.873
87
.734
Total
65.980
89
Table 11 shows the summary of Pearson r correlation coefficient of attitude
towards computer and computer efficacy of science and engineering students. The
result shows no correlation at all in attitude towards computer and confidence in using
computer.
Table11. Summary of Pearson r correlation coefficient of attitude towards
computer and computer efficacy of science and engineering students
Science Students
Engineering Students
*significant correlation
R
Sig. (2 tailed)
-0.04
0.83
0.31
0.09
Table12 shows the summary of Pearson r correlation coefficient of four
components of computer efficacy of science students. The result shows no significant
relationship between affective and perceived control and usefulness and behavioral
intention and perceived control and usefulness. There was also no significant
relationship between perceived control and perceived usefulness. However, the result
shows a statistically significant relationship between affective and behavioral intention.
Table12. Summary of Pearson r correlation coefficient of four components of
computer efficacy of science students
Affective
Pearson Correlation
Behavioral
Perceived
Perceived
Affective
intention
control
usefulness
1
.672**
-.006
-.249
.000
.977
.185
1
.127
-.314
.504
.091
1
.195
Sig. (2-tailed)
Behavioral intention
Perceived control
Perceived usefulness
Pearson Correlation
.672**
Sig. (2-tailed)
.000
Pearson Correlation
-.006
.127
Sig. (2-tailed)
.977
.504
Pearson Correlation
-.249
-.314
.195
Sig. (2-tailed)
.185
.091
.302
.302
1
**. Correlation is significant at the 0.01 level (2-tailed).
Table13 shows the summary of Pearson r correlation coefficient of four
components of computer efficacy of engineering students. The result shows a
statistically significant relationship between affective and behavioral intention and
perceived control. There was also a significant relationship between perceived control
and perceived usefulness and behavioral intention. On the other hand, the result failed
to reveal a statistically significant relationship between affective and perceived
usefulness and behavioral intention and perceived usefulness.
Table13. Summary of Pearson r correlation coefficient of four components of
computer efficacy of engineering students
Affective
Affective
Pearson Correlation
1
Sig. (2-tailed)
Behavioral intention
Pearson Correlation
Sig. (2-tailed)
Perceived control
Perceived usefulness
.691**
Behavioral
Perceived
Perceived
intention
control
usefulness
.691**
.518**
.213
.000
.003
.258
1
.582**
.313
.001
.093
1
.671**
.000
.518**
.582**
Sig. (2-tailed)
.003
.001
Pearson Correlation
.213
.313
.671**
Sig. (2-tailed)
.258
.093
.000
Pearson Correlation
**. Correlation is significant at the 0.01 level (2-tailed).
.000
1
The overall result shows a positive attitude and high level of confidence in using
the computer of science and engineering students. The positive result could be
attributed to the completed and ongoing technology training of the participants. The
result shows that science students scored significantly higher in the average mean of
computer attitude and computer efficacy compared with engineering students. It means
that science students are more positive and confident in using computer which is
reflected in the survey that 80% of them use computer 5 times or more per week. On
the other hand, engineering students scored significantly higher in usage of computer
for inquiry and communication purposes compared with science students. It means that
engineering students use computers more frequent for inquiry and communication
purposes.
The result of the study for computer attitude and computer efficacy confirms the
findings of Arani (2001) which states that the level of self-confidence in using the
computer and attitude towards computer was high. However, Arani’s findings in the
relationship of attitude towards computer and computer efficacy contradict with the
outcome of this study which shows no correlation at all. The study also confirms the
findings of Sam (2005) which shows that undergraduate students of Universiti Malaysia
Sarawak have high computer self-efficacy.
The findings of Teo (2008) that shows a significant correlation in all components
of computer attitude conforms with the findings of this study for engineering students
except for the affective and perceived usefulness and behavioral intention and
perceived usefulness which shows no correlation. On the other hand, Teo’s findings
contradict the outcome of the study for science student which shows no correlation in all
of the components except affective and behavioral intention which shows a significant
relationship.
Technology training is a great factor for the participant to possess positive
attitudes towards computer and in gaining confidence in different levels of computer
efficacy. The usage of computer for inquiry and communication can be maximized if
participant will be exposed in more technology training. The study of Abbitt (2007)
shows that there is a significant increase in self-efficacy beliefs while enrolled in a
technology course.
A further study can be conducted considering large sample size for the two
groups of respondents. Relationship of age, gender, and participant with completed
technology training and different variables can also be considered.
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