1286 Charles Buabeng-Andoh and Yidana Issifu / Procedia - Social and Behavioral Sciences 191 ( 2015 ) 1282 – 1287
Table 2. Students’ Mean (pedagogical ICT use) score as Function of School Type and School Location
Location
Urban Semi-urban Rural
School Type M SD N M SD N M SD N Overall school
type
Public 2.93* 1.02 658 2.78* 1. 07 716 2.48* 1.04 742 2.73
Private 2.94* 1.04 458 2.49* 1 .07 545 2.58* 1.00 261 2.67
Overall school
location
2.93 1.03 2.64 1.07 2.53 1.02
M = Mean, SD = Standard deviation, N = subsample size, *indicates significance at p < .001
Research Question 3: Relative importance of factors relating to students’ pedagogical use of ICT in learning.
In terms of correlations, there was moderate relationship between students’ CT usage and the variables: access to
computers (Pearson Correlation Value (r) = .46, p < .01), competence (r = .44, p < .01), self-efficacy (r = .38, p <
.01) and leadership support (r = .31, p < .01). Access to computers had the highest correlation with ICT use was
access to computers, followed by competence. In addition, moderate relationship existed between self-efficacy and
competence (r = .48, p < .01). Also, moderate correlation existed between access and competence (r = .48, p < .01).
Finally, moderate correlation exited between self-efficacy and access (r = .39, p < .01). Cohen’s (1988) was adopted
to determine the strength of relationship. Cohen suggested that a coefficient between .30 and .49 indicates moderate
correlation. In the multiple regression analysis, the variables: access to computers, competence, self-efficacy and
administrative support were the predictor variables. The dependent variable was ICT usage. The results found that
competence (β = .24), access (β = .23), leadership support (β = .20) and self-efficacy (β = .14) each made
independent contributions to the equation predicting ICT usage. Competence was the most significant predictor,
followed by access to computers. The variables correctively accounted for 32.4% of the variation in ICT.
7. Discussion
The findings of the study found that the students used ICT to communicate with peers more than any other
activities. The finding of this study confirms Irfan and Noor’s, (2012) study which found that students’ Internet
applications for communication skills are at the proficient level. Generally, this study revealed that students’ use of
ICT to support their learning was low. This may be attributed to students’ low competence level in ICT usage. In
this study, students in public schools used ICT to support their learning more than students in private schools. This
result appears to be unexpected since in Ghana most private schools are better resourced than the public schools in
terms of educational technology. The reason for the finding of this study could be attributed to the fact that students
in public schools were only recently exposed to ICT and are therefore eager to use ICT in their learning resulting in
their high usage. The finding of this study is in contradiction to Asaolu and Fashanu’s, (2012) study which revealed
that private schools students are more proficient in the use of ICT in their learning than their counterparts in public
secondary schools. In addition, this study found that students in public and private urban schools pedagogically used
ICT more than their counterparts in semi-urban and rural schools. Overall, this study revealed that students in urban
schools used ICT to support their learning significantly more than students in semi-urban and rural schools. This
could be attributed to greater urban schools students’ access and exposure to ICT than their counterparts in semiurban
and rural schools. The finding of this study is in contradiction with Pei-Yu, (2013) who found that student
preference and expectation to technology integration did not differ between rural and urban school teachers. The
findings of this study found that competence, leadership support, self efficacy and access to computers significantly
related to technology use of students. This result is in line with findings from previous research on computer
competence (Judi, Amin, Zin & Latih, 2011), administrative support (Dexter, 2008), self-efficacy (Yuen & Ma,
2008) and access to computers (Balanskat, Blamire & Kefalla, 2007). The results of the study found that
Table 2. Students’ Mean (pedagogical ICT use) score as Function of School Type and School Location
Location
Urban Semi-urban Rural
School Type M SD N M SD N M SD N Overall school
type
Public 2.93* 1.02 658 2.78* 1. 07 716 2.48* 1.04 742 2.73
Private 2.94* 1.04 458 2.49* 1 .07 545 2.58* 1.00 261 2.67
Overall school
location
2.93 1.03 2.64 1.07 2.53 1.02
M = Mean, SD = Standard deviation, N = subsample size, *indicates significance at p < .001
Research Question 3: Relative importance of factors relating to students’ pedagogical use of ICT in learning.
In terms of correlations, there was moderate relationship between students’ CT usage and the variables: access to
computers (Pearson Correlation Value (r) = .46, p < .01), competence (r = .44, p < .01), self-efficacy (r = .38, p <
.01) and leadership support (r = .31, p < .01). Access to computers had the highest correlation with ICT use was
access to computers, followed by competence. In addition, moderate relationship existed between self-efficacy and
competence (r = .48, p < .01). Also, moderate correlation existed between access and competence (r = .48, p < .01).
Finally, moderate correlation exited between self-efficacy and access (r = .39, p < .01). Cohen’s (1988) was adopted
to determine the strength of relationship. Cohen suggested that a coefficient between .30 and .49 indicates moderate
correlation. In the multiple regression analysis, the variables: access to computers, competence, self-efficacy and
administrative support were the predictor variables. The dependent variable was ICT usage. The results found that
competence (β = .24), access (β = .23), leadership support (β = .20) and self-efficacy (β = .14) each made
independent contributions to the equation predicting ICT usage. Competence was the most significant predictor,
followed by access to computers. The variables correctively accounted for 32.4% of the variation in ICT.
7. Discussion
The findings of the study found that the students used ICT to communicate with peers more than any other
activities. The finding of this study confirms Irfan and Noor’s, (2012) study which found that students’ Internet
applications for communication skills are at the proficient level. Generally, this study revealed that students’ use of
ICT to support their learning was low. This may be attributed to students’ low competence level in ICT usage. In
this study, students in public schools used ICT to support their learning more than students in private schools. This
result appears to be unexpected since in Ghana most private schools are better resourced than the public schools in
terms of educational technology. The reason for the finding of this study could be attributed to the fact that students
in public schools were only recently exposed to ICT and are therefore eager to use ICT in their learning resulting in
their high usage. The finding of this study is in contradiction to Asaolu and Fashanu’s, (2012) study which revealed
that private schools students are more proficient in the use of ICT in their learning than their counterparts in public
secondary schools. In addition, this study found that students in public and private urban schools pedagogically used
ICT more than their counterparts in semi-urban and rural schools. Overall, this study revealed that students in urban
schools used ICT to support their learning significantly more than students in semi-urban and rural schools. This
could be attributed to greater urban schools students’ access and exposure to ICT than their counterparts in semiurban
and rural schools. The finding of this study is in contradiction with Pei-Yu, (2013) who found that student
preference and expectation to technology integration did not differ between rural and urban school teachers. The
findings of this study found that competence, leadership support, self efficacy and access to computers significantly
related to technology use of students. This result is in line with findings from previous research on computer
competence (Judi, Amin, Zin & Latih, 2011), administrative support (Dexter, 2008), self-efficacy (Yuen & Ma,
2008) and access to computers (Balanskat, Blamire & Kefalla, 2007). The results of the study found that
