I
declare this as an original document and a creation of my own mind and not a
duplication of another authors work.
Name:
|
|
Signature:
 |
This
project was timely submitted for examination purposes with my approval to my
University Supervisor
Name:
|
Signature:
 |
I
dedicate this research paper primarily for an audience of students, departmental
heads and the entire higher educational community of Kenyatta University and to
my parent school, School of Economics.
I acknowledge the
efforts of the following individuals for their role in making this research a
success. My university supervisor, Dr Manyasa, for being such a wonderful
instructor. He carefully took me through
the entire process from project specification through to estimations and
evaluations. Secondly, my classmates for their support and assistance in areas I
thought I needed a little help. Thirdly, the entire university community for indirectly
seeing to it that this project was a success. Lastly, I thank my mother Mrs.
Martina and my elder brother Mr. Lenin Jumba, two great individuals whose
financial support and encouragement I relied upon to give rise to this final
document.
AB:
Attitude Behavior
TPB: Theory
of Planned Behavior
ICTs:
Information Communication Technology
PBC:
Perceived Behavior Control
SN: Social
Norms
This
project examines the role that information and communications technology can
play in connecting students to the three main pillars of higher education: the
lecture hall, administrative service and scholarly research using the Theory of
Planned Behavior. The thirty undergraduate students sampled students were invited
to complete a questionnaire aimed at getting their intentions, attitudes,
social influences and control on ICTs interaction. The sections in these questionnaire collects
data on each of the components proposed by the theory of planned behavior (TPB)
as designed by Ajzen used to study the influence of attitudes on a wide range
of social interactions.
We uniquely
begin our research with some of the comments and personal experiences we got
from some of our respondents:
“Most of my lecturers now prefer using power
point presentations. They say it is an easy and more efficient way of teaching.
I agree with them to some point because we are able to get our note before-hand
and prepare for a day’s lecture. It has made learning exciting with little
paper work involved.”
“I never thought education would be exciting as
it is over the internet. I always allocate myself an hour or two per day to
recapture what I have learnt in class. Going online means seeking more assistance
on a subject I did not quite understand and it is easier as most of my tutors
are hard to reach.”
“I have accounts with social networking sites
such as Face book, Twitter and MySpace. These have become my social platforms
as I keep in touch with my friends and learn about trending issues with the
touch of a button.”
“Most of my work is now stored online. Keeping
records such as my class notes, curriculum vitae and other educational
documents is easier than it was three years ago. The student registration
process was one of those nightmares we would never want to encounter due to the
amount of work to be done. Given these machines registration has become not
only satisfying but also exiting.”
The above
statements reveal general positive attitudes students have towards ICTs
integration.
The magical touch of
Telecommunication and ICT is evident in every nation and multi national
corporation around the world. This has led to a raging debate on the extent
over which these new technologies are transforming individual economies.
Scholars have been invited to study the relevance of such technologies to the third
world. Positive results have then led the introduction of ICT based courses in
institutions of higher education. However, as irony would have it, universities
are both producers and consumers of the skilled labor that works in this
industry.
The main trigger of this research
was the steady rise in demand for higher education in developing countries. Our
curiosity therefore led us to examine the role of ICTs in three major
functions: improving the quality, widening the access and cutting costs in the
teaching process. This paper follows to highlight with given examples trending
issues in ICTs and the new growth dynamics they are creating in university
education.ict is indeed the driver of intellectual change but without proper
good policies and planning, can create undesirable changes.
When an institution decides to
employ ICTs, its main intention is to improve efficiency in the education
process hereby achieves a growth goal. The institution will be targeting how
best to improve worker motivation, student’s performance and student-instructor
relationship while all together reducing the work load. Notably, ICTs have
promoted newer approaches to working, learning and innovation.
The 21st century is
experiencing the emergence of new technologies resulting from genius
innovations. Key to note of all has been positive growth of ICTs and their
impact on human activities. Recent researches conducted in developed and
developing countries have shown an increasing integration and upgrade of ICT in
functioning economic sectors. The education sector has also been on the fore
front in giving the first priority to ICT literacy through developing a number
of educational programs. In fact, so real is the existence of this technology
that special ministries have been formed. Governments are also investing in ICT
based conferences hence encouraging innovations and foreign investment.
The internet is the main driver of
technology, for instance, the mare presence of it reduces the entire globe into
a tiny electronic village. The East African region has recently taken a major
face lift with the arrival of an under sea cable. Such is a milestone the
community would never have dreamt of more than a decade ago. Few examples of
the benefits here include increased volume of trade, opened up new employment
opportunities, easier transfer of information and more importantly made
research work easier.
Such are just a few examples that
have led to institutions encouraging training in ICTs applications and
utilization. This has become a growing concern around the and for a number of
years, training and further development in the ICT area is evident. Literature
collected on studies that have been conducted on the literacy of technology in
higher education sectors reveal an importance of having ICT integrated in
teaching and learning processes. Top and upcoming universities have been
increasing their intakes by the year. This has in turn increased demand for the
use of virtual and semi-virtual equipment to enhance education. Although these
changes in demand can be sighted in and out of the university, the former is
more active as it is restructuring to meet an ever growing student population.
So as years go by, changes in the
nature of teaching, learning and administrating should be seen. That is,
lecturers shift from using marker boards to projectiles and PowerPoint
presentations and to the use of CDROMs, electronic books and journals.
Similarly, students tend to enjoy online sources of information, social
networking and easier communication with their instructors.
ICTs
came into the world with a promise to transform processes through efficiency
and scope. In the world of higher education, the classroom setting has heeded
to this transformation through teacher-student behavior. This study therefore
suggested that student intention to engage in ICTs interaction was strongly
dependent on their attitudes towards ICTs, social influences, and perceived
behavior control.
This research paper sought answers
for the following questions:
Are attitudes,
peer influences and perceived behavior significant in determining a students’
intention to participate in ICTs interaction?
To what scope do
the above determinate correlate to shape the intention of an individual
student?
This study used TPB to study the
attitude of Kenyatta University undergraduate students towards the use of ICTs
in their learning processes. This is because as K.U continues to expand day in
and out, healthy ICTs application is essential. It is therefore statistically
significant to study these attitudes to ascertain the level of ICT awareness,
competence and influences
In more recent times, studies have
considered the impact of ICT in higher education in terms of the benefits for
tertiary learners; for instance, Goerke & Oliver (2007) studied the use of
mobile learning (M-learning) at the Western Australia Curtin University of
Technology. They have suggested that emerging technologies which are both owned
and used by students, and incorporated wisely into university curricula, can go
some way towards enhancing high quality, face-to- face learning experiences.
This way, articulated knowledge is created as students are presented with
alternative ways of achieving otherwise challenging results are effected (pg.
10).
In yet another study conducted in
the same country at the University of Melbourne found evidence of a significant
positive association between effective use of ICT and success in higher
education (Kennedy et al., 2006). Field researchers have reported an increasing
number of students using ICTs and ICTs based tools in their studies. For
instance, cell phones have been identified as the number one widely accessed
type of technology. They have become, in higher education, an important factor
to capture when studying the shift in ICTs application for learning and
teaching processes.
Lecturers are slowly but surely learning to
model the operation of innovative ICTs, such as students’ mobile devices, in
their courses so that they know how to challenge and change their teaching and
researching methods. Pelliccione (2001) reports that, in a case study at Curtin
University of Technology, the university’s teaching staff had a high commitment
to the adoption of ICT for teaching and learning.
Data in the above study also
suggested that the adoption of ICT in teaching and learning would be promoted
by greater support of the change at the management level of the university.
This is a level consisting of university non teaching staff such as
administration officers, medical and accommodation departments. A huge chunk of
this literature has been devoted to the issues of what factors have been shown
to influence the different perceptions of effectiveness of ICT integration.
It must be asked what can be learnt
from these studies about the relationship between different perceptions and
teaching and learning behavior in terms of effective ICT use? The issue of
students’ ICT uptake is of crucial importance, for it has been argued that use
of ICT during learning practice will lead to competent and confident use in
their learning, while lack of it will mean that students will make little use
of ICT (Stiggins1999).
Universities and other tertiary education
institutions have indicated that ICT has a generally positive effect on the
quality of teaching and learning, although few have been able to offer detailed
evidence. Although many student satisfaction surveys have been conducted on the
use of ICTs, it is still unclear whether or not students fully perceive their
potential and use them effectively (Pachler et al., 1999).
It should be determined whether
refraining from their use is simply through ignorance or some other underlying
concern, and how this may be addressed. Without investigation, it is difficult
for universities to know if they are meeting the needs of students effectively
(OECD, 2005). Galanouli et al., (2001) reported that students perceived three
main barriers to their perceptions of ICT use during learning practice:
lecturers’ attitudes, lack of resources and lack of time. Although lack of
appropriate equipment was considered an important factor when students were
unable to use ICT in their learning, it was also clear that lecturers’ uptake
of ICTs and attitudes towards ICTs’ use played the most crucial role in the
success or failure of their teaching and learning.
Many other researchers (Pelgrum,
2001; Cox & Cox, 2000; Yuen, 2002) in addition to carrying out their own
literature reviews undertook studies to obtain primary evidence of the barriers
to lecturers’ perception of ICT use. They found out that a teacher’s main concern
was their students’ academic success, but lack of evidence and the imposition
of ICT usage are putting pressure on teachers which, in turn, will lead to
building-up more stress and resistance (Schrum 2000). Teachers who are already
fearful of the technology will become more reluctant when the use of the
technology is imposed on them.
Masopha (2005) argues about
imposing on teachers with unclear instructions and directions in implementing ICT
and the lack of professional development are important findings in determining the
possible issues of the teachers’ resistance towards the technological
implementation. Caverly & MacDonald (2004) shared similar views in that
they said there is little focus given to develop positive perceptions for
professional development in technology. It is likely this will force teachers
to create their own standards and understandings which may not produce a
satisfactory result. There has been some optimism amongst teachers in Tasmania
that a thorough understanding of effective ICT use would enhance motivation to
use ICT with their students. If this goal is to be realized, the students also
will be equipped with a variety of ICT skills in order to be meaningfully and
motivationally engaged in their learning.
It states that personal attitude,
subjective norms and perceived behavioral control work together in shaping an
individual’s behavioral intentions and behaviors. This theory provides a link
between human behavior and attitude. It was developed by Icek Ajzen as an
extension of the theory of reasoned action.
Here therefore human behavior is stated to be
in need of three types of belief considerations namely: likely consequences of
the behavior (behavioral beliefs), the normative expectations of others (normative
beliefs), and the presence of factors that may enables and or/ limits the performance
of the behavior (control beliefs). In their respective aggregates, behavioral
beliefs produce a favorable or unfavorable attitude toward the behavior;
normative beliefs result in perceived social pressure or subjective norm;
and control beliefs give rise to perceived behavioral control. In
combination, attitude toward the behavior, subjective norm, and perception of
behavioral control lead to the formation of a behavioral intention. As a
general rule, the more favorable the attitude and subjective norm, and the
greater the perceived control, the stronger should be the person’s intention to
perform the behavior in question.
Finally, given a sufficient degree
of actual control over the behavior, people are expected to carry out
their intentions when the opportunity arises. Intention is thus assumed to be
the immediate antecedent of behavior. However, because many behaviors pose
difficulties of execution that may limit volitional control, it is useful to
consider perceived behavioral control in addition to intention. To the extent
that perceived behavioral control is veridical, it can serve as a proxy for
actual control and contribute to the prediction of the behavior in question.
This theory has been utilized over
the years by a number of researchers in an attempt to understand people’s
intensions to engage and use certain activities. For instance, activities such
as game hunting, weight loss, sports, opinion polls-a quite common survey in
Kenya and gift giving (Hrubes et al,. 2001). These
studies indicate that the application of the Theory of Planned Behavior deals
with the antecedents of attitudes, subjective norms, and perceived behavioral
control.
In
general, the more positive the attitude towards performing the behavior, along
with substantial levels of social pressure to do so and perceived control over
one’s actions, the more likely the individual is to carry out the behavior.
Often behaviors pose difficulties with regard to execution. In this way it is
useful to consider perceived behavioral control in addition to intention.
Depending on how realistic people are in their judgments of the level of
difficulty associated with a behavior, a measure of perceived behavioral
control can serve as a proxy for actual control and as such can contribute to
the prediction of the behavior in question.
When
it was applied to the engagement with ICT, the Theory of Planned Behavior
suggested that intentions to engage and interact with a particular program or
software element is influenced by attitudes towards using ICT, perceived social
pressure to do so and by perceptions of control over the interaction.
The diagram below is a schematic representation of the theory:
 |
Diagram 1: The Theory of Planned Behavior- Icek Ajzen
Alternatively, this theory in
its can be expressed in a mathematical model as below:
![BI {{=}} (W_1)AB[(b) + (e)] + (W_2)SN[(n) +(m)] + (W_3)PBC[(c) + (p)]\,\!](file:///C:/DOCUME%7E1/Punky/LOCALS%7E1/Temp/msohtmlclip1/01/clip_image009.gif){kind=small}
Where: BI=behavior intention, AB=attitude
towards behavior, b=the weight/strength of each belief, e=attribute or outcome
evaluation, SN=social norm, n=strength or weight of each normative belief,
m=motivation complying with each individual under study, PBC=perceived
behavioral control, c=the strength of each control belief, p=perceived power of
the control factor, Wi=empirically derived weight.
Earlier researchers have taken on this problem in different
dimensions. For instance Creswell 2005 brings to book a mixed method approach
that intentionally combines the strengths of both quantitative and qualitative
paradigms to investigate this phenomenon. This mixed approach majors upon
collecting, processing, analyzing and studying both qualitative and
quantitative data in either single and time series studies to make clearer this
problem.
In this research study, the sampling area was
limited to the borders of Kenyatta University, Main Campus, and the target
population was the student body of Kenyatta University. Since this study was
conducted by a single researcher, it displayed limitations such as inadequacy
in time and funds. Our study is therefore modified to focus only on the
quantitative part in examining a small random sample of regular program
students (n=30) so that its objectives are achieved.
In this study, 30 respondents were sampled
from the current student population studying at Kenyatta University. The number
of the selected sample in terms of their sex and frequency is organized as
shown in the diagram below.
|
Sample
|
Frequency
|
Percent
|
Valid Percent
|
Cumulative Percent
|
|
|
|
female
|
17
|
56.7
|
56.7
|
56.7
|
| male |
13
|
43.3
|
43.3
|
100.0
|
|
| Total |
30
|
100.0
|
100.0
|
|
|
Diagram 2: number of respondents and
their distribution.
Burns (2000) states that the
questionnaire method is the most commonly used descriptive method in
educational research, and gathers data at a particular point in time (p. 566).
In this study, the generation of data was done using the questionnaire
technique of data collection to ensure that the required data could be
collected within the allocated time and budget.
The
first stage involved the development and administration of a suitable ten item
questionnaire. This questionnaire was designed in a way that it captures
variables related to the use of ICTs and its sections were lifted from the
components of the Theory of Planned Behavior (see illustration in diagram 1).
The ten items are grouped into 5 sections named A-E and are on a quest to
investigate the respondents’ behavioral beliefs, normative beliefs, control beliefs,
their attitudes towards ICTs subjective norms, perceived behavior control and
lastly intentions.
Section A is an initial background section
that was included to collect background information related to the students’
demography such as their sex, age group, year of study and faculty. Further on, this section asks for their competence
and awareness levels in ICT intelligently building them up for the next
sections. Section B to D serve as collectors of information related to Normative and control, desirability, pairing
intentions, attitude, subjectivity and perceived behavior in that order. The last section E calls upon them to value
ICTs considering their future careers and also their life as students.
4.0 DATA
ANALYSIS AND DISCUSSION
A
total of N=30 respondents completed and returned the questionnaire on ICTs integration.
N comprised of 57% and 43% percent females and males respectively. Diagram 3 provides a summary of the respondents’
perceived ICT competency levels (Q5a) and their perceived importance of
ICT-based learning to their future careers (Q5b).
|
Question 5
|
Mean
|
Std
Deviation
|
Percentage
|
Cor.
|
|
5a.Your
level of competence in ICTs
|
2.4000
|
.49827
|
High:40.0%
Av:
60.0%
Cum:100.0%
|
1.000
|
|
5b.The
importance of ICT-based learning to
your coursework
|
3.000
|
.0000
|
High:100.0%
Low:-
Cum:
100.0%
|
_
|
Diagram
3: ICTs Competence and Importance. Where: N=30, Av=average, cum=cumulative
percentage, cor=correlation.
An
initial analysis of the components in the questionnaire is represented in
diagram 4 below. In each of the components, three-point bipolar scales where
used to assess the respondents perceptions of each statement presented to them.
Additionally, this diagram reports the mean, mode and median scores as measures
of central tendency for each of the scales. Also presented is the standard
deviation as a measure of dispersion, skewness and kurtosis measure the
distribution.
|
Question
|
Mean
|
Mode
|
Median
|
S.D
|
Kurtosis
|
Skewness
|
|
5a
|
2.4
|
2
|
2
|
0.49827
|
-1.95
|
0.430
|
|
5b
|
3
|
3
|
3
|
0
|
-
|
-
|
|
6a
|
3.867
|
3
|
3
|
0.346
|
3.386
|
-2.273
|
|
6b
|
1.867
|
2
|
1
|
0.819
|
0.259
|
-1.457
|
|
6c
|
2.733
|
3
|
3
|
0.521
|
2.934
|
-1.867
|
|
7a
|
1
|
1
|
1
|
0
|
-
|
-
|
|
7b
|
2.567
|
3
|
3
|
0.774
|
0.354
|
-1.434
|
|
7c
|
2.867
|
3
|
3
|
0.346
|
3.386
|
-2.274
|
|
7d
|
2.23
|
2
|
3
|
0.774
|
-1.60
|
-0.441
|
|
7e
|
1.133
|
1
|
1
|
0.507
|
12.207
|
3.660
|
|
8a
|
1
|
1
|
1
|
0
|
-
|
-
|
|
8b
|
2.767
|
3
|
3
|
0.568
|
5.036
|
-2.428
|
|
8c
|
2.9
|
3
|
3
|
0.305
|
6.308
|
-2.809
|
|
8d
|
2.9
|
3
|
3
|
0.305
|
6.308
|
-2.809
|
|
8e
|
1.333
|
1
|
1
|
0.434
|
12.514
|
3.49
|
|
9a
|
2.967
|
3
|
3
|
0.1826
|
30.00
|
-5.477
|
|
9b
|
2.933
|
3
|
3
|
0.25371
|
12.207
|
-3.660
|
|
9c
|
2.767
|
3
|
3
|
0.568
|
5.036
|
-2.428
|
|
9d
|
1.2
|
1
|
1
|
0.40684
|
0.527
|
1.58
|
|
10a
|
1
|
1
|
1
|
0
|
-
|
-
|
|
10b
|
1.767
|
1.5
|
1
|
0.858
|
-1.484
|
0.487
|
|
10c
|
1
|
1
|
1
|
0.183
|
30
|
5.477
|
|
10d
|
1
|
1
|
1
|
0
|
-
|
-
|
Diagram 4: questionnaire
analysis of individual scale mean, mode, median, standard deviation, kurtosis
and skewness. Note: please find in the attached questionnaire,
phrases to which the above numbered questions correspond.
The diagrams 5 through to 10
follow now on display the item statements in each component of the Theory of
Planned Behavior and their scale analysis. Please note that negatively polarized scores have been
reversed so that they are displayed as being positive statements. For instance,
questions 8c “ICT use makes me angry” and 8d “ICT use makes me
frustrated” are phrased as negative statements. However, since they have been
reversed scored, the mean scores of 2.9 and 2.9 respectively indicate that more
respondents chose to disagree with these two statements from those who agreed
with them.
(On a scale of 1 to 3 where
1=least likely, 2=somehow likely, 3=most likely)
The two
diagrams below 5a and 5b show response rates towards the four pillar components
of our mathematical model that is the Intention, Attitude, Subjective Norms and
Perceived Behavioral Control scale-wise. A majority of the students indicated
that they had intentions and plans to engage in ICTs use (96.7%), that ICTs
interaction is pleasant (93.3%), and that people who matter to them thought
that they should engage in ICTs interaction (83.3%). While 6.7% are in support of the subjective norm, 3.3 %,
6.7% and 10 % are the percentages showing those respondents indifferent in
behavioral intentions, attitudes and subjective norms in that order. Lastly, a large
80% indicated that they found it difficult to engage in ICTs while 20% showed
indifference in engagement.
.
|
Factors
|
Scale 1
|
Scale 2
|
Scale 3
|
Polarity
|
Mean
|
S. d
|
|
9a.Intention
|
_
|
3.3%
|
96.7%
|
+
|
2.967
|
0.1826
|
|
9b:Attitude
|
_
|
6.7%
|
93.3%
|
+
|
2.933
|
0.2537
|
|
9c:Subjective Norms
|
6.7%
|
10.0%
|
83.3%
|
+
|
2.767
|
0.5683
|
|
9d: PBC
|
80.0%
|
20.0%
|
_
|
+
|
1.2
|
0.407
|
Diagram
5 a: response rates for intentions, attitudes, subjective norms and PBC
Where Q9a: I intend/plan to engage in ICT
use, Q9b:
Interacting with ICT is helpful/pleasant, Q9c: People who matter to me think that
I should engage in ICT and Q9d:
I find it difficult to engage in ICT.
|
Paired Items
|
9a: Intention
|
9b: Attitude
|
9c:Subjective Norms
|
9d: PBC
|
|
9a: Intention
|
1
|
-0.05
|
-0.78
|
0.093
|
|
9b: Attitude
|
-0.05
|
1
|
0.128
|
0.134
|
|
9c:Subjective Norms
|
-0.078
|
0.128
|
1
|
-0.089
|
|
9d: PBC
|
0.093
|
0.134
|
-0.089
|
1
|
Diagram 5b: paired correlations between intentions,
attitude, subjective norms and PBC.
In the diagram above intention and
attitude are negatively correlated (-0.05), PBC and subjective norms (-0.089)
and intentions and subjective norms too (-0.78). However, there is a positive
relationship between Intention and PBC (0.093), attitude and subjective norms
(0.128) and between subjective norms and attitude (0.128).
(on a scale of 1 to 3 where
1=true, 2=indifferent, 3=untrue)
Diagram 6a
and 6b present the participants responses to the concept of desirability regarding
their involvement with ICTs according to each 8a-8e statements and statement
correlations in that order. This depicts an overall positive response with
competence scoring 100% and intelligence 90.0%. Negative statements have been
negatively polarized proving the fact that 83.3%, 90.0% and 90.0% of the sample
do not associate an interaction with ICTs with exhaustion, anger and
frustration. However, 10.0% each of the respondents are indifferent about the
negative statements while 6.7% and 3.3% are indifferent and found it untrue,
respectively, to associate ICTs interaction with intelligence.
|
Concept
|
Scale 1
|
Scale 2
|
Scale 3
|
Polarity
|
Mean
|
Std dev
|
|
8a
|
100.0%
|
-
|
_
|
+
|
1.00
|
0.000
|
|
8b
|
6.7%
|
10.0%
|
83.3%
|
-
|
2.76
|
0.568
|
|
8c
|
_
|
10.0%
|
90.0%
|
-
|
2.90
|
0.305
|
|
8d
|
_
|
10.0%
|
90.0%
|
-
|
2.90
|
0.305
|
|
8e
|
90.0%
|
6.7%
|
3.3%
|
+
|
1.13
|
0.434
|
Diagram 6a. Response rates for
8a: ICT makes me feel competent, 8b: ICT use makes me exhausted, 8c: ICT use
makes me angry, 8d: ICT use makes me frustrated, 8e: ICT makes me feel as though am intelligent.
|
Concept
|
8a
|
8b
|
8c
|
8d
|
8e
|
|
8a
|
k
|
k
|
k
|
k
|
k
|
|
8b
|
k
|
1
|
-0.139
|
0.259
|
0.130
|
|
8c
|
k
|
-0.139
|
1
|
-0.111
|
0.104
|
|
8d
|
k
|
-0.259
|
-0.111
|
1
|
0.104
|
|
8e
|
k
|
0.130
|
0.104
|
0.104
|
1
|
Diagram 6b.
Correlations table among 8a: ICT makes me feel competent, 8b: ICT use makes me
exhausted, 8c: ICT use makes me angry, 8d: ICT use makes me frustrated,
8e: ICT makes me feel as though am
intelligent. Where k is a constant
(On a scale of 1 to 3 where
1=least likely, 2=somehow likely, 3=most likely)
Diagram 7a presents a combination of scaled
respondents’ responses in Normative and Control factors. In normative factors, 86.7%,
26.7% and 76.7% of the students will most likely be influenced into ICTs
participation by their peers, family and lecturers in that order. Notably,
parents (40.0%) are placed as the most least likely to influence them into ICTs
engagement followed by the lecturers at 3.3%. however, 13.3%, 33.3% and 20.0%
are indifferent in whether their peers, parents and lecturers respectively will
play a significant role in influencing their choice of ICTs.
Statements
7a and 7e are negative statements therefore can be interpreted as 100.0% of the
students are never too busy to engage in ICTs and a considerable 93.3% disagree
with the suggestion that ICT is expensive and time consuming. Similarly, there
are positive responses in favor of having the essential skills to use any form
of ICTs (73.3%), having the knowledge (86.7%), and can afford and use ICTs
(43.3%). This corresponds to 10.0%, 13.3%, and 36.7% who are indifferent
respectively while scores of 16.7% and 20.0% have the skills and can afford
ICTs.
|
Concept
|
Scale
1
|
Scale
2
|
Scale
3
|
Polarity
|
Mean
|
Std dev
|
|
6a
|
_
|
13.3%
|
86.7%
|
+
|
2.867
|
0.346
|
|
6b
|
40.0%
|
33.3%
|
26.7%
|
+
|
1.867
|
0.819
|
|
6c
|
3.3%
|
20.0%
|
76.7%
|
+
|
2.733
|
0.521
|
|
7a
|
100.0%
|
_
|
_
|
-
|
1.000
|
0.000
|
|
7b
|
16.7%
|
10.0%
|
73.3%
|
+
|
2.567
|
0.774
|
|
7c
|
_
|
13.3%
|
86.7%
|
+
|
2.867
|
0.346
|
|
7d
|
20.0%
|
36.7%
|
43.3%
|
+
|
2.233
|
0.774
|
|
7e
|
93.3%
|
_
|
6.7%
|
-
|
1.133
|
0.507
|
Diagram 7a.
Response rates for 6a: Friends/peer, 6b: Family, 6c: Teachers/lecturer, 7a: Too busy to engage in ICT use, 7b: Have the
skills essential to use any form of ICT, 7c:
Have the knowledge to engage in ICT use, 7d: Afford the cost of using ICT, and 7e: It is rather expensive and time consuming to
engage in ICT use
|
Concept
|
6a
|
6b
|
6c
|
|
6a
|
1
|
0.179
|
-0.204
|
|
6b
|
0.179
|
1
|
0.237
|
|
6c
|
-0.204
|
0.237
|
1
|
Diagram 7b.
Correlations between normative concepts where 6a: Friends/peer, 6b: Family, and
6c: Teachers/lecturer.
|
Concept
|
7a
|
7b
|
7c
|
7d
|
7e
|
|
7a
|
k
|
k
|
k
|
k
|
k
|
|
7b
|
k
|
1
|
-0.223
|
0.060
|
0.152
|
|
7c
|
k
|
-0.223
|
1
|
-0.137
|
0.105
|
|
7d
|
k
|
0.060
|
-0.137
|
1
|
-0.258
|
|
7e
|
k
|
0.152
|
0.105
|
-0.258
|
1
|
Diagram 7c.
Correlation between Concepts of control 7a:
Too busy to engage in ICT use, 7b: Have the skills essential to use any
form of ICT, 7c: Have the knowledge to
engage in ICT use, 7d: Afford the cost
of using ICT, and 7e: It is rather
expensive and time consuming to engage in ICT use.
(On
a scale of 1 to 3 where 1=true, 2=indifferent, 3=untrue)
The diagram below, 8a, presents scaled
responses to ICT valuations by the respondents. These responses depict a strong
yes of 100%, 96.7% and 100% for the importance of ICTs in enhancing the
education process, making the learning process easier and efficiency in the
work place. The essentiality of ICT for good educational results however
received a fairly positive score of 50.0% with 23.3% being indifferent and
26.7% disagreeing.
|
Value
|
Scale
1
|
Scale
2
|
Scale
3
|
Polarity
|
Mean
|
Std
dev
|
|
10a
|
100.0%
|
_
|
_
|
+
|
1.000
|
0.000
|
|
10b
|
50.0%
|
23.3%
|
26.7%
|
+
|
1.767
|
0.858
|
|
10c
|
96.7%
|
3.3%
|
_
|
+
|
1.033
|
0.183
|
|
10d
|
100.0%
|
_
|
_
|
+
|
1.000
|
0.000
|
Diagram 8a.Response rates for
6a. 10a. Engagement with
ICT enhances the learning process, 10b. ICT is essential for good educational
results, 10c. ICT makes the learning process easier, and 10d. ICT is essential
in efficiency both at school and work.
Diagram 8b
shows correlations between variables in the concept that seeks to value ICTs.
The essentiality for good educational results is positively correlated to the
role of ict in making the learning process easy by +0.271. Statements 10a and
10d however are almost so perfect constants that they offer no correlation
results among themselves and the other variables.
|
Value
|
10a
|
10b
|
10c
|
10d
|
|
10a
|
k
|
k
|
k
|
k
|
|
10b
|
k
|
1
|
0.271
|
k
|
|
10c
|
k
|
0.271
|
1
|
k
|
|
10d
|
k
|
k
|
k
|
k
|
Diagram 8b. Correlation between Values. 10a.
Engagement with ICT enhances the learning process, 10b. ICT is essential for
good educational results, 10c. ICT makes the learning process easier, and 10d.
ICT is essential in efficiency both at school and work.
We
look at the mathematical model of the Theory of Planned Behavior where
intention is regressed against behavior attitude, social norms and PBC. So that
our regression equation takes the form:
Y=b0+b1X1+b2X2+b3X3+e
Where
Y is the intention, X1 is the behavior attitude, X2 is the social norm and X3
is the perceived behavioral control all of which are measured by Q9, b0, b1,
b2, and b3 are weights determined outside the model.
|
|
Coefficient
Beta
|
T
distribution
|
Significance
|
|
9b
|
-0.054
|
-0.274
|
0.786
|
|
9c
|
-0.064
|
-0.115
|
0.755
|
|
9d
|
0.095
|
0.479
|
0.636
|
Diagram
9. 9a is plotted against 9b, 9c and 9d
The strong
positive significance of 0.786, 0.755, and 0.636 shows that attitude, social
norms and PBC are all important in explaining the variations in Intention to
engage in ICTs interaction. The beta values -0.054 and -0.064 intention placed
against social norms and attitude have a negative relationship while increases
in PBC will mean that there is increased likelihood that an individual will
engage in ICT interaction.
This type
of analysis studies and expounds on Kenyatta University students’ attitudes
towards ICTs integration using tools set up by the theory of planned behavior.
This involved a questionnaire aimed at collecting data on individual
intentions, attitudes, norms, PBCs, beliefs, demographic factors, awareness and
competence. Sure positive responses can
be sighted in intentions with this sample agreeing that they intend to
participate in ICTs integration in future.
However
responses for normative beliefs had differentiated scores that resulted to a
rather weak positive figure. This is because responses are distributed between
peers, lecturers and family with peers being the major influence to a possible
engagement in ICTs integration followed by the lecturers and finally family.
This shows that the above normative concepts when placed against ICTs are of no
strong significance to this n=30.
Additionally,
the desirability concept shows a clear difference between positive and negative
statements with respondents choosing not to associate ICTs integration with
exhaustion, anger and frustration but rather competence and intelligence. We
can also conclude that on overall, this chosen sample believes that ICTs is
pleasant, helpful and easy and would result in positive classroom and workplace
outcomes with a majority further indicating that they were confident in their
level of ict competence and awareness.
5.0 CONCLUSIONS
AND RECOMMENDATIONS FOR FURTHER STUDY
The general
findings of this study reveal that students from Kenyatta University have a
rather high awareness and ability to use different technologies made available
to them. They have a positive attitude about how ict is changing classroom
learning, teacher-student interaction and understanding of the subject matter.
However, at the same time, they report a strong preference for learning some of
the units with traditional teaching methods-the classroom board and settings
such as units offered from the Mathematics department. This is an area our study
did not fully explore; a future study should therefore be conducted capturing
the possible combinations of ICTs and traditional methods in teaching such
units. This could lead to more precise suggestions on how to merge and
desirably complement these two methods.
Furthermore, from this study we sure learn of
a positive relationship existing between paired TPB components. Although this
is an interesting study that gathers positive findings, it is a result of
quantitative cross sectional data from the student perspective which limits the
scope of our conclusions. This shows that it is possible more precise results
could be obtain from longitudinal studies with together with the help of
qualitative data where students attitude and behavior towards ICTs integration
is followed through time studies. In addition to this, the behavior of teaching
and support staff could also be studied so as to expound on the study scope.
Lastly,
this study focuses on the attitudes on a young generation which believes in
opportunities exploited in a tri-factor: awareness and competence, access and
influence, and dynamism in the learning process. However, these factors may
greatly differ if we allow for a battle based on differences in demographical
factors, that is, the sexes, age groups, schools and career choices, and the
level of study. This suggests a possible difference in attitude and competence
between postgraduate and undergraduate students, students and staff and between
younger and older members of this society. Given this observation, a suggestion
arises on the need for the entire University community to be studied in a
blended hybrid research that will present the most optimal application of
components present in the TPB to be able to come up with a world class learning
environment producing and attracting competent scholars.
REFERENCES
Ajzen,
I., & Driver, B.L. (1991). Prediction of leisure participation from
behavioural, normative and control
beliefs: An application of the theory of planned behaviour. Leisure Sciences,3.
Ajzen,
I., & Fishbein, M. (2000). Attitudes and attitude behaviour: Reasoned and
automatic processes. In European review of social
psychology. W.
Stroebe, M. Hewstone, (eds). Wiley,
Chichester, England.
Ajzen,
I., & Fishbein, M. (2005). The influence of attitudes on behaviour. In D.
Albarracin., B.T
Johnson., & M.P Zanna. (Eds). The handbook of attitudes (p. 173-221). Mahwah, NJ: Erlbaum.
Fishbein,
M., & Ajzen. I. (1975). Belief,
attitude and behaviour: An introduction to theory and research.Addison
Wesley, Phillipines.
Hrubes,
D., Ajzen, I., & Daigle, J. (2001). Predicting hunting intentions and
behaviour: An application of the theory
of planned behaviour. Leisure
Sciences, 23.
Moreno,
R., & Mayer, R. E. (2000). A Learner-Centered Approach to Multimedia
Explanations: Deriving Instructional
Design Principles from Cognitive Theory. Interactive Multimedia Electronic Journal of Computer-Enhanced Learning, 2(2).
Burns, R. B. (2000). Introduction
to research methods. French’s Forest, NSW, Australia: Longman.
Caverly, D. C. & MacDonald, L.
(2004). Techtalk: Keeping up with technology. Journal of Developmental Education, 28(2),
38-39.
Cox, M., Preston, C. & Cox, K.
(1999). What factors support or prevent teachers from using ICT in their classrooms? Paper
presented at the British Educational Research Association Annual Conference, University of Sussex
at Brighton, September 2-5.
Galanouli, D., Murphy, C. &
Gardner, J. (2001). Students’ Perceptions of ICT-related Support in Teaching Placements. Journal
of Computer Assisted Learning, 17(4), 396-408.
Hope, W. C. (1996). Factors
facilitating teachers' use of computer technology. The Clearing House, 70(2), 106-107.
Laurillard, D. (1993). Rethinking
university teaching: A framework for the effective use of educational technology. London: Routledge.
Markel,
S. L. (2001). Technology and education online discussion forums: It's in the
response. Retrieved November 10,
2011, from http://www.westga.edu/~distance/ojdla/summer42/markel42.html
Newman, W. (2004). ICT: Does IT
matter? Access, 18(3), 5-8.
OECD (2005). E-learning in tertiary
education: where do we stand?. Education & Skills, 4(1), 1- 293.
Oliver,B. & Goerke, V. (2007).
Australian undergraduates' use and ownership of emerging technologies: Implications and
opportunities for creating engaging learning experiences for the Net
Generation. Australasian Journal of Educational Technology, 23(2),
171-186.
Oliver, R. (2002). The role of ICT
in higher education for the 21st century: ICT as a change agent for education. Proceedings of
the Higher Education for the 21st Century Conference. Sarawak: Curtin
University.
Pelgrum, W. J. (2001). Obstacles to the integration of ICT in
education: results from a worldwide
educational assessment. Computers
& Education, 37(2), 163-178.
Pelliccione, L. (2001). Implementing
innovative technology: Towards the transformation of a university, unpublished PhD thesis, Curtin University of
Technology, Perth, Western Australia,
Australia.
The Survey Questionnaire
Good
morning/afternoon/evening. I am Lionel, A fourth year student taking a B.Economics
(Statistics). I am conducting a survey on ICT integration and interaction in Kenyatta
University, asking students like you for their views. I will be grateful if you
would answer a few questions for me. With estimated time duration of about ten
minutes, this interview is meant to be voluntary and confidential. You may
refuse to participate and withdraw without jeopardy. Please inform me as to
whether you are willing to be part of this survey.
A. Demographics, Awareness and
Competence.
Q1. Please mark
and indicate where applicable:
a. Sex Male Female
b. Age 15-19 20-24 25-29 30+
c. Year of study
d. School/course
Q2.Do you own and /or have
access to any of the following:
Yes No
a. Internet
enabled phoneb. Personal
computer
c. Email address and or/social
networking address
(Face book, Twitter, MySpace
e.t.c)
Q3. Please indicate your
ability to use the following programs to carry out the types of tasks shown
(Mark one option per program)
Unaided
Aided Don’t Know a. Emailed a
documentb. Used PowerPoint
c. Using an online
bibliographic database to search for a publication
Q4. Please indicate how frequently, if ever, you
have used or been involved in the following on weekly basis (mark one option
per activity)
Often Once Never
Done
a. Sourced from a website
directly or indirectly reading material
b. Online
discussion forumsNote :( inclusive
of social networking academic groups and pages)c. Virtual
learning environment e.g. WebCT or Blackboard
d. Academic support and advice
from a lecturer via email
Q5.How would you rate (on a
scale of 1 to 3 where 1=low, 2=average, 3=high)
1 2 3a. Your level of
competence in ICT
b. The importance of ICT-based
learning to your coursework
B.Normative and Control
Factors (On a scale of 1 to 3 where 1=least likely,
2=somehow likely, 3=most likely)
Q6. How likely are the
following groups of people in your life to influence you into the use of ICT?
(Mark one option per group)
1 2 3
a. Friends/peer
b. Family
c. Teachers/lecturer
Q7. How likely would you as an
individual consider yourself to be in the following situations (mark only one
option per activity)
1 2 3a. Too busy to
engage in ICT useb. Have the skills
essential to use any form of ICTc. Have the
knowledge to engage in ICT used. Afford the cost
of using ICTe. It is rather
expensive and time consuming to engage in ICT use.
C. The Desirability Concept (on
a scale of 1 to 3 where 1=true, 2=indifferent, 3=untrue)
Q8. How true or untrue are the
following statements (mark only one option per statement)
1 2 3
a. ICT makes me
feel competentb. ICT use makes
me exhausted c. ICT use makes
me angryd. ICT use makes
me frustrated
e. ICT makes me feel as though
am intelligent
D. Paired Intentions,
Attitude, Subjectivity and Perceived behavior in that order (On
a scale of 1 to 3 where 1=least likely, 2=somehow likely, 3=most likely)
Q9.How likely will
each of the following statements happen? 1 2 3a. I intend/plan
to engage in ICT use.b. Interacting
with ICT is helpful/pleasant
c. People who matter to me
think that I should engage in ICT
d. I find it
difficult to engage in ICT.
E.Valuing ICT
Q10.How
would you rate your engagement with ICT considering your experiences as a K.U student?
(On a scale of 1 to 3 where 1=true, 2=indifferent, 3=untrue. Mark only one
option per engagement)
1 2 3a. Engagement with
ICT enhances the learning processb. ICT is
essential for good educational resultsc. ICT makes the
learning process easier
d. ICT is essential in
efficiency both at school and work.
F.
Do you have any further suggestion(s) on how K.U should support her students in
ICTs use and integration? And or/tell us about your personal experiences with
ICTs integration as a student of K.U
 |
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 |
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This questionnaire captures
the components present in the Theory of Planned Behavior .It has been edited
and piloted therefore approved for data collection.
Thank you for participating in
this survey.
